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Sample records for undertake spectral classifications

  1. Stellar Spectral Classification with Locality Preserving Projections ...

    Indian Academy of Sciences (India)

    With the help of computer tools and algorithms, automatic stellar spectral classification has become an area of current interest. The process of stellar spectral classification mainly includes two steps: dimension reduction and classification. As a popular dimensionality reduction technique, Principal Component Analysis (PCA) ...

  2. A Spectral-Texture Kernel-Based Classification Method for Hyperspectral Images

    Directory of Open Access Journals (Sweden)

    Yi Wang

    2016-11-01

    Full Text Available Classification of hyperspectral images always suffers from high dimensionality and very limited labeled samples. Recently, the spectral-spatial classification has attracted considerable attention and can achieve higher classification accuracy and smoother classification maps. In this paper, a novel spectral-spatial classification method for hyperspectral images by using kernel methods is investigated. For a given hyperspectral image, the principle component analysis (PCA transform is first performed. Then, the first principle component of the input image is segmented into non-overlapping homogeneous regions by using the entropy rate superpixel (ERS algorithm. Next, the local spectral histogram model is applied to each homogeneous region to obtain the corresponding texture features. Because this step is performed within each homogenous region, instead of within a fixed-size image window, the obtained local texture features in the image are more accurate, which can effectively benefit the improvement of classification accuracy. In the following step, a contextual spectral-texture kernel is constructed by combining spectral information in the image and the extracted texture information using the linearity property of the kernel methods. Finally, the classification map is achieved by the support vector machines (SVM classifier using the proposed spectral-texture kernel. Experiments on two benchmark airborne hyperspectral datasets demonstrate that our method can effectively improve classification accuracies, even though only a very limited training sample is available. Specifically, our method can achieve from 8.26% to 15.1% higher in terms of overall accuracy than the traditional SVM classifier. The performance of our method was further compared to several state-of-the-art classification methods of hyperspectral images using objective quantitative measures and a visual qualitative evaluation.

  3. Data Field Modeling and Spectral-Spatial Feature Fusion for Hyperspectral Data Classification.

    Science.gov (United States)

    Liu, Da; Li, Jianxun

    2016-12-16

    Classification is a significant subject in hyperspectral remote sensing image processing. This study proposes a spectral-spatial feature fusion algorithm for the classification of hyperspectral images (HSI). Unlike existing spectral-spatial classification methods, the influences and interactions of the surroundings on each measured pixel were taken into consideration in this paper. Data field theory was employed as the mathematical realization of the field theory concept in physics, and both the spectral and spatial domains of HSI were considered as data fields. Therefore, the inherent dependency of interacting pixels was modeled. Using data field modeling, spatial and spectral features were transformed into a unified radiation form and further fused into a new feature by using a linear model. In contrast to the current spectral-spatial classification methods, which usually simply stack spectral and spatial features together, the proposed method builds the inner connection between the spectral and spatial features, and explores the hidden information that contributed to classification. Therefore, new information is included for classification. The final classification result was obtained using a random forest (RF) classifier. The proposed method was tested with the University of Pavia and Indian Pines, two well-known standard hyperspectral datasets. The experimental results demonstrate that the proposed method has higher classification accuracies than those obtained by the traditional approaches.

  4. Soil classification basing on the spectral characteristics of topsoil samples

    Science.gov (United States)

    Liu, Huanjun; Zhang, Xiaokang; Zhang, Xinle

    2016-04-01

    Soil taxonomy plays an important role in soil utility and management, but China has only course soil map created based on 1980s data. New technology, e.g. spectroscopy, could simplify soil classification. The study try to classify soils basing on the spectral characteristics of topsoil samples. 148 topsoil samples of typical soils, including Black soil, Chernozem, Blown soil and Meadow soil, were collected from Songnen plain, Northeast China, and the room spectral reflectance in the visible and near infrared region (400-2500 nm) were processed with weighted moving average, resampling technique, and continuum removal. Spectral indices were extracted from soil spectral characteristics, including the second absorption positions of spectral curve, the first absorption vale's area, and slope of spectral curve at 500-600 nm and 1340-1360 nm. Then K-means clustering and decision tree were used respectively to build soil classification model. The results indicated that 1) the second absorption positions of Black soil and Chernozem were located at 610 nm and 650 nm respectively; 2) the spectral curve of the meadow is similar to its adjacent soil, which could be due to soil erosion; 3) decision tree model showed higher classification accuracy, and accuracy of Black soil, Chernozem, Blown soil and Meadow are 100%, 88%, 97%, 50% respectively, and the accuracy of Blown soil could be increased to 100% by adding one more spectral index (the first two vole's area) to the model, which showed that the model could be used for soil classification and soil map in near future.

  5. Stellar Spectral Classification with Minimum Within-Class and ...

    Indian Academy of Sciences (India)

    Support Vector Machine (SVM) is one of the important stellar spectral classification methods, and it is widely used in practice. But its classification efficiencies cannot be greatly improved because it does not take the class distribution into consideration. In view of this, a modified SVM-named Minimum within-class and ...

  6. Spectral band selection for classification of soil organic matter content

    Science.gov (United States)

    Henderson, Tracey L.; Szilagyi, Andrea; Baumgardner, Marion F.; Chen, Chih-Chien Thomas; Landgrebe, David A.

    1989-01-01

    This paper describes the spectral-band-selection (SBS) algorithm of Chen and Landgrebe (1987, 1988, and 1989) and uses the algorithm to classify the organic matter content in the earth's surface soil. The effectiveness of the algorithm was evaluated comparing the results of classification of the soil organic matter using SBS bands with those obtained using Landsat MSS bands and TM bands, showing that the algorithm was successful in finding important spectral bands for classification of organic matter content. Using the calculated bands, the probabilities of correct classification for climate-stratified data were found to range from 0.910 to 0.980.

  7. Automatic parquet block sorting using real-time spectral classification

    Science.gov (United States)

    Astrom, Anders; Astrand, Erik; Johansson, Magnus

    1999-03-01

    This paper presents a real-time spectral classification system based on the PGP spectrograph and a smart image sensor. The PGP is a spectrograph which extracts the spectral information from a scene and projects the information on an image sensor, which is a method often referred to as Imaging Spectroscopy. The classification is based on linear models and categorizes a number of pixels along a line. Previous systems adopting this method have used standard sensors, which often resulted in poor performance. The new system, however, is based on a patented near-sensor classification method, which exploits analogue features on the smart image sensor. The method reduces the enormous amount of data to be processed at an early stage, thus making true real-time spectral classification possible. The system has been evaluated on hardwood parquet boards showing very good results. The color defects considered in the experiments were blue stain, white sapwood, yellow decay and red decay. In addition to these four defect classes, a reference class was used to indicate correct surface color. The system calculates a statistical measure for each parquet block, giving the pixel defect percentage. The patented method makes it possible to run at very high speeds with a high spectral discrimination ability. Using a powerful illuminator, the system can run with a line frequency exceeding 2000 line/s. This opens up the possibility to maintain high production speed and still measure with good resolution.

  8. Spectral Classification of Asteroids by Random Forest

    Science.gov (United States)

    Huang, C.; Ma, Y. H.; Zhao, H. B.; Lu, X. P.

    2016-09-01

    With the increasing asteroid spectral and photometric data, a variety of classification methods for asteroids have been proposed. This paper classifies asteroids based on the observations of Sloan Digital Sky Survey (SDSS) Moving Object Catalogue (MOC) by using the random forest algorithm. With the training data derived from the taxonomies of Tholen, Bus, Lazzaro, DeMeo, and Principal Component Analysis, we classify 48642 asteroids according to g, r, i, and z SDSS magnitudes. In this way, asteroids are divided into 8 spectral classes (C, X, S, B, D, K, L, and V).

  9. Dimensionality-varied convolutional neural network for spectral-spatial classification of hyperspectral data

    Science.gov (United States)

    Liu, Wanjun; Liang, Xuejian; Qu, Haicheng

    2017-11-01

    Hyperspectral image (HSI) classification is one of the most popular topics in remote sensing community. Traditional and deep learning-based classification methods were proposed constantly in recent years. In order to improve the classification accuracy and robustness, a dimensionality-varied convolutional neural network (DVCNN) was proposed in this paper. DVCNN was a novel deep architecture based on convolutional neural network (CNN). The input of DVCNN was a set of 3D patches selected from HSI which contained spectral-spatial joint information. In the following feature extraction process, each patch was transformed into some different 1D vectors by 3D convolution kernels, which were able to extract features from spectral-spatial data. The rest of DVCNN was about the same as general CNN and processed 2D matrix which was constituted by by all 1D data. So that the DVCNN could not only extract more accurate and rich features than CNN, but also fused spectral-spatial information to improve classification accuracy. Moreover, the robustness of network on water-absorption bands was enhanced in the process of spectral-spatial fusion by 3D convolution, and the calculation was simplified by dimensionality varied convolution. Experiments were performed on both Indian Pines and Pavia University scene datasets, and the results showed that the classification accuracy of DVCNN improved by 32.87% on Indian Pines and 19.63% on Pavia University scene than spectral-only CNN. The maximum accuracy improvement of DVCNN achievement was 13.72% compared with other state-of-the-art HSI classification methods, and the robustness of DVCNN on water-absorption bands noise was demonstrated.

  10. Spectral-spatial classification of hyperspectral data with mutual information based segmented stacked autoencoder approach

    Science.gov (United States)

    Paul, Subir; Nagesh Kumar, D.

    2018-04-01

    Hyperspectral (HS) data comprises of continuous spectral responses of hundreds of narrow spectral bands with very fine spectral resolution or bandwidth, which offer feature identification and classification with high accuracy. In the present study, Mutual Information (MI) based Segmented Stacked Autoencoder (S-SAE) approach for spectral-spatial classification of the HS data is proposed to reduce the complexity and computational time compared to Stacked Autoencoder (SAE) based feature extraction. A non-parametric dependency measure (MI) based spectral segmentation is proposed instead of linear and parametric dependency measure to take care of both linear and nonlinear inter-band dependency for spectral segmentation of the HS bands. Then morphological profiles are created corresponding to segmented spectral features to assimilate the spatial information in the spectral-spatial classification approach. Two non-parametric classifiers, Support Vector Machine (SVM) with Gaussian kernel and Random Forest (RF) are used for classification of the three most popularly used HS datasets. Results of the numerical experiments carried out in this study have shown that SVM with a Gaussian kernel is providing better results for the Pavia University and Botswana datasets whereas RF is performing better for Indian Pines dataset. The experiments performed with the proposed methodology provide encouraging results compared to numerous existing approaches.

  11. An Extended Spectral-Spatial Classification Approach for Hyperspectral Data

    Science.gov (United States)

    Akbari, D.

    2017-11-01

    In this paper an extended classification approach for hyperspectral imagery based on both spectral and spatial information is proposed. The spatial information is obtained by an enhanced marker-based minimum spanning forest (MSF) algorithm. Three different methods of dimension reduction are first used to obtain the subspace of hyperspectral data: (1) unsupervised feature extraction methods including principal component analysis (PCA), independent component analysis (ICA), and minimum noise fraction (MNF); (2) supervised feature extraction including decision boundary feature extraction (DBFE), discriminate analysis feature extraction (DAFE), and nonparametric weighted feature extraction (NWFE); (3) genetic algorithm (GA). The spectral features obtained are then fed into the enhanced marker-based MSF classification algorithm. In the enhanced MSF algorithm, the markers are extracted from the classification maps obtained by both SVM and watershed segmentation algorithm. To evaluate the proposed approach, the Pavia University hyperspectral data is tested. Experimental results show that the proposed approach using GA achieves an approximately 8 % overall accuracy higher than the original MSF-based algorithm.

  12. Stellar Spectral Classification with Minimum Within-Class and ...

    Indian Academy of Sciences (India)

    spectral classification methods, and it is widely used in practice. But its ... Digital Sky Survey (SDSS) show that MMSVM performs better than. SVM. Key words. ... to feature extraction, and then the traditional classifier SVM is used to classify the.

  13. Stellar spectral classification with locality preserving projections and ...

    Indian Academy of Sciences (India)

    63

    Manuscript Number: JOAA-D-16-00002R3. Full Title: Stellar Spectral Classification with Locality Preserving Projections and Support Vector. Machine. Article Type: Original Study. Corresponding Author: zhongbao liu. North University of China. CHINA. Corresponding Author Secondary. Information: Corresponding Author's ...

  14. Hyper-spectral frequency selection for the classification of vegetation diseases

    OpenAIRE

    Dijkstra, Klaas; van de Loosdrecht, Jaap; Schomaker, Lambert; Wiering, Marco

    2017-01-01

    Reducing the use of pesticides by early visual detection of diseases in precision agriculture is important. Because of the color similarity between potato-plant diseases, narrow band hyper-spectral imaging is required. Payload constraints on unmanned aerial vehicles require reduc- tion of spectral bands. Therefore, we present a methodology for per-patch classification combined with hyper-spectral band selection. In controlled experiments performed on a set of individual leaves, we measure the...

  15. Spatial and Spectral Hybrid Image Classification for Rice Lodging Assessment through UAV Imagery

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    Ming-Der Yang

    2017-06-01

    Full Text Available Rice lodging identification relies on manual in situ assessment and often leads to a compensation dispute in agricultural disaster assessment. Therefore, this study proposes a comprehensive and efficient classification technique for agricultural lands that entails using unmanned aerial vehicle (UAV imagery. In addition to spectral information, digital surface model (DSM and texture information of the images was obtained through image-based modeling and texture analysis. Moreover, single feature probability (SFP values were computed to evaluate the contribution of spectral and spatial hybrid image information to classification accuracy. The SFP results revealed that texture information was beneficial for the classification of rice and water, DSM information was valuable for lodging and tree classification, and the combination of texture and DSM information was helpful in distinguishing between artificial surface and bare land. Furthermore, a decision tree classification model incorporating SFP values yielded optimal results, with an accuracy of 96.17% and a Kappa value of 0.941, compared with that of a maximum likelihood classification model (90.76%. The rice lodging ratio in paddies at the study site was successfully identified, with three paddies being eligible for disaster relief. The study demonstrated that the proposed spatial and spectral hybrid image classification technology is a promising tool for rice lodging assessment.

  16. Vulnerable land ecosystems classification using spatial context and spectral indices

    Science.gov (United States)

    Ibarrola-Ulzurrun, Edurne; Gonzalo-Martín, Consuelo; Marcello, Javier

    2017-10-01

    Natural habitats are exposed to growing pressure due to intensification of land use and tourism development. Thus, obtaining information on the vegetation is necessary for conservation and management projects. In this context, remote sensing is an important tool for monitoring and managing habitats, being classification a crucial stage. The majority of image classifications techniques are based upon the pixel-based approach. An alternative is the object-based (OBIA) approach, in which a previous segmentation step merges image pixels to create objects that are then classified. Besides, improved results may be gained by incorporating additional spatial information and specific spectral indices into the classification process. The main goal of this work was to implement and assess object-based classification techniques on very-high resolution imagery incorporating spectral indices and contextual spatial information in the classification models. The study area was Teide National Park in Canary Islands (Spain) using Worldview-2 orthoready imagery. In the classification model, two common indices were selected Normalized Difference Vegetation Index (NDVI) and Optimized Soil Adjusted Vegetation Index (OSAVI), as well as two specific Worldview-2 sensor indices, Worldview Vegetation Index and Worldview Soil Index. To include the contextual information, Grey Level Co-occurrence Matrices (GLCM) were used. The classification was performed training a Support Vector Machine with sufficient and representative number of vegetation samples (Spartocytisus supranubius, Pterocephalus lasiospermus, Descurainia bourgaeana and Pinus canariensis) as well as urban, road and bare soil classes. Confusion Matrices were computed to evaluate the results from each classification model obtaining the highest overall accuracy (90.07%) combining both Worldview indices with the GLCM-dissimilarity.

  17. Hyperspectral Image Classification Based on the Combination of Spatial-spectral Feature and Sparse Representation

    Directory of Open Access Journals (Sweden)

    YANG Zhaoxia

    2015-07-01

    Full Text Available In order to avoid the problem of being over-dependent on high-dimensional spectral feature in the traditional hyperspectral image classification, a novel approach based on the combination of spatial-spectral feature and sparse representation is proposed in this paper. Firstly, we extract the spatial-spectral feature by reorganizing the local image patch with the first d principal components(PCs into a vector representation, followed by a sorting scheme to make the vector invariant to local image rotation. Secondly, we learn the dictionary through a supervised method, and use it to code the features from test samples afterwards. Finally, we embed the resulting sparse feature coding into the support vector machine(SVM for hyperspectral image classification. Experiments using three hyperspectral data show that the proposed method can effectively improve the classification accuracy comparing with traditional classification methods.

  18. Spectral Classification of Similar Materials using the Tetracorder Algorithm: The Calcite-Epidote-Chlorite Problem

    Science.gov (United States)

    Dalton, J. Brad; Bove, Dana; Mladinich, Carol; Clark, Roger; Rockwell, Barnaby; Swayze, Gregg; King, Trude; Church, Stanley

    2001-01-01

    Recent work on automated spectral classification algorithms has sought to distinguish ever-more similar materials. From modest beginnings separating shade, soil, rock and vegetation to ambitious attempts to discriminate mineral types and specific plant species, the trend seems to be toward using increasingly subtle spectral differences to perform the classification. Rule-based expert systems exploiting the underlying physics of spectroscopy such as the US Geological Society Tetracorder system are now taking advantage of the high spectral resolution and dimensionality of current imaging spectrometer designs to discriminate spectrally similar materials. The current paper details recent efforts to discriminate three minerals having absorptions centered at the same wavelength, with encouraging results.

  19. SPATIAL-SPECTRAL CLASSIFICATION BASED ON THE UNSUPERVISED CONVOLUTIONAL SPARSE AUTO-ENCODER FOR HYPERSPECTRAL REMOTE SENSING IMAGERY

    Directory of Open Access Journals (Sweden)

    X. Han

    2016-06-01

    Full Text Available Current hyperspectral remote sensing imagery spatial-spectral classification methods mainly consider concatenating the spectral information vectors and spatial information vectors together. However, the combined spatial-spectral information vectors may cause information loss and concatenation deficiency for the classification task. To efficiently represent the spatial-spectral feature information around the central pixel within a neighbourhood window, the unsupervised convolutional sparse auto-encoder (UCSAE with window-in-window selection strategy is proposed in this paper. Window-in-window selection strategy selects the sub-window spatial-spectral information for the spatial-spectral feature learning and extraction with the sparse auto-encoder (SAE. Convolution mechanism is applied after the SAE feature extraction stage with the SAE features upon the larger outer window. The UCSAE algorithm was validated by two common hyperspectral imagery (HSI datasets – Pavia University dataset and the Kennedy Space Centre (KSC dataset, which shows an improvement over the traditional hyperspectral spatial-spectral classification methods.

  20. LAMOST OBSERVATIONS IN THE KEPLER FIELD: SPECTRAL CLASSIFICATION WITH THE MKCLASS CODE

    Energy Technology Data Exchange (ETDEWEB)

    Gray, R. O. [Department of Physics and Astronomy, Appalachian State University, Boone, NC 28608 (United States); Corbally, C. J. [Vatican Observatory Research Group, Steward Observatory, Tucson, AZ 85721-0065 (United States); Cat, P. De [Royal Observatory of Belgium, Ringlaan 3, B-1180 Brussel (Belgium); Fu, J. N.; Ren, A. B. [Department of Astronomy, Beijing Normal University, 19 Avenue Xinjiekouwai, Beijing 100875 (China); Shi, J. R.; Luo, A. L.; Zhang, H. T.; Wu, Y.; Cao, Z.; Li, G. [Key Laboratory for Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012 (China); Zhang, Y.; Hou, Y.; Wang, Y. [Nanjing Institute of Astronomical Optics and Technology, National Astronomical Observatories, Chinese Academy of Sciences, Nanjing 210042 (China)

    2016-01-15

    The LAMOST-Kepler project was designed to obtain high-quality, low-resolution spectra of many of the stars in the Kepler field with the Large Sky Area Multi Object Fiber Spectroscopic Telescope (LAMOST) spectroscopic telescope. To date 101,086 spectra of 80,447 objects over the entire Kepler field have been acquired. Physical parameters, radial velocities, and rotational velocities of these stars will be reported in other papers. In this paper we present MK spectral classifications for these spectra determined with the automatic classification code MKCLASS. We discuss the quality and reliability of the spectral types and present histograms showing the frequency of the spectral types in the main table organized according to luminosity class. Finally, as examples of the use of this spectral database, we compute the proportion of A-type stars that are Am stars, and identify 32 new barium dwarf candidates.

  1. Dimensionality-varied deep convolutional neural network for spectral-spatial classification of hyperspectral data

    Science.gov (United States)

    Qu, Haicheng; Liang, Xuejian; Liang, Shichao; Liu, Wanjun

    2018-01-01

    Many methods of hyperspectral image classification have been proposed recently, and the convolutional neural network (CNN) achieves outstanding performance. However, spectral-spatial classification of CNN requires an excessively large model, tremendous computations, and complex network, and CNN is generally unable to use the noisy bands caused by water-vapor absorption. A dimensionality-varied CNN (DV-CNN) is proposed to address these issues. There are four stages in DV-CNN and the dimensionalities of spectral-spatial feature maps vary with the stages. DV-CNN can reduce the computation and simplify the structure of the network. All feature maps are processed by more kernels in higher stages to extract more precise features. DV-CNN also improves the classification accuracy and enhances the robustness to water-vapor absorption bands. The experiments are performed on data sets of Indian Pines and Pavia University scene. The classification performance of DV-CNN is compared with state-of-the-art methods, which contain the variations of CNN, traditional, and other deep learning methods. The experiment of performance analysis about DV-CNN itself is also carried out. The experimental results demonstrate that DV-CNN outperforms state-of-the-art methods for spectral-spatial classification and it is also robust to water-vapor absorption bands. Moreover, reasonable parameters selection is effective to improve classification accuracy.

  2. On the relevance of spectral features for instrument classification

    DEFF Research Database (Denmark)

    Nielsen, Andreas Brinch; Sigurdsson, Sigurdur; Hansen, Lars Kai

    2007-01-01

    Automatic knowledge extraction from music signals is a key component for most music organization and music information retrieval systems. In this paper, we consider the problem of instrument modelling and instrument classification from the rough audio data. Existing systems for automatic instrument...... classification operate normally on a relatively large number of features, from which those related to the spectrum of the audio signal are particularly relevant. In this paper, we confront two different models about the spectral characterization of musical instruments. The first assumes a constant envelope...

  3. Parallel exploitation of a spatial-spectral classification approach for hyperspectral images on RVC-CAL

    Science.gov (United States)

    Lazcano, R.; Madroñal, D.; Fabelo, H.; Ortega, S.; Salvador, R.; Callicó, G. M.; Juárez, E.; Sanz, C.

    2017-10-01

    Hyperspectral Imaging (HI) assembles high resolution spectral information from hundreds of narrow bands across the electromagnetic spectrum, thus generating 3D data cubes in which each pixel gathers the spectral information of the reflectance of every spatial pixel. As a result, each image is composed of large volumes of data, which turns its processing into a challenge, as performance requirements have been continuously tightened. For instance, new HI applications demand real-time responses. Hence, parallel processing becomes a necessity to achieve this requirement, so the intrinsic parallelism of the algorithms must be exploited. In this paper, a spatial-spectral classification approach has been implemented using a dataflow language known as RVCCAL. This language represents a system as a set of functional units, and its main advantage is that it simplifies the parallelization process by mapping the different blocks over different processing units. The spatial-spectral classification approach aims at refining the classification results previously obtained by using a K-Nearest Neighbors (KNN) filtering process, in which both the pixel spectral value and the spatial coordinates are considered. To do so, KNN needs two inputs: a one-band representation of the hyperspectral image and the classification results provided by a pixel-wise classifier. Thus, spatial-spectral classification algorithm is divided into three different stages: a Principal Component Analysis (PCA) algorithm for computing the one-band representation of the image, a Support Vector Machine (SVM) classifier, and the KNN-based filtering algorithm. The parallelization of these algorithms shows promising results in terms of computational time, as the mapping of them over different cores presents a speedup of 2.69x when using 3 cores. Consequently, experimental results demonstrate that real-time processing of hyperspectral images is achievable.

  4. Land Cover Classification Using Integrated Spectral, Temporal, and Spatial Features Derived from Remotely Sensed Images

    Directory of Open Access Journals (Sweden)

    Yongguang Zhai

    2018-03-01

    Full Text Available Obtaining accurate and timely land cover information is an important topic in many remote sensing applications. Using satellite image time series data should achieve high-accuracy land cover classification. However, most satellite image time-series classification methods do not fully exploit the available data for mining the effective features to identify different land cover types. Therefore, a classification method that can take full advantage of the rich information provided by time-series data to improve the accuracy of land cover classification is needed. In this paper, a novel method for time-series land cover classification using spectral, temporal, and spatial information at an annual scale was introduced. Based on all the available data from time-series remote sensing images, a refined nonlinear dimensionality reduction method was used to extract the spectral and temporal features, and a modified graph segmentation method was used to extract the spatial features. The proposed classification method was applied in three study areas with land cover complexity, including Illinois, South Dakota, and Texas. All the Landsat time series data in 2014 were used, and different study areas have different amounts of invalid data. A series of comparative experiments were conducted on the annual time-series images using training data generated from Cropland Data Layer. The results demonstrated higher overall and per-class classification accuracies and kappa index values using the proposed spectral-temporal-spatial method compared to spectral-temporal classification methods. We also discuss the implications of this study and possibilities for future applications and developments of the method.

  5. Application of partial least squares near-infrared spectral classification in diabetic identification

    Science.gov (United States)

    Yan, Wen-juan; Yang, Ming; He, Guo-quan; Qin, Lin; Li, Gang

    2014-11-01

    In order to identify the diabetic patients by using tongue near-infrared (NIR) spectrum - a spectral classification model of the NIR reflectivity of the tongue tip is proposed, based on the partial least square (PLS) method. 39sample data of tongue tip's NIR spectra are harvested from healthy people and diabetic patients , respectively. After pretreatment of the reflectivity, the spectral data are set as the independent variable matrix, and information of classification as the dependent variables matrix, Samples were divided into two groups - i.e. 53 samples as calibration set and 25 as prediction set - then the PLS is used to build the classification model The constructed modelfrom the 53 samples has the correlation of 0.9614 and the root mean square error of cross-validation (RMSECV) of 0.1387.The predictions for the 25 samples have the correlation of 0.9146 and the RMSECV of 0.2122.The experimental result shows that the PLS method can achieve good classification on features of healthy people and diabetic patients.

  6. An expert computer program for classifying stars on the MK spectral classification system

    International Nuclear Information System (INIS)

    Gray, R. O.; Corbally, C. J.

    2014-01-01

    This paper describes an expert computer program (MKCLASS) designed to classify stellar spectra on the MK Spectral Classification system in a way similar to humans—by direct comparison with the MK classification standards. Like an expert human classifier, the program first comes up with a rough spectral type, and then refines that spectral type by direct comparison with MK standards drawn from a standards library. A number of spectral peculiarities, including barium stars, Ap and Am stars, λ Bootis stars, carbon-rich giants, etc., can be detected and classified by the program. The program also evaluates the quality of the delivered spectral type. The program currently is capable of classifying spectra in the violet-green region in either the rectified or flux-calibrated format, although the accuracy of the flux calibration is not important. We report on tests of MKCLASS on spectra classified by human classifiers; those tests suggest that over the entire HR diagram, MKCLASS will classify in the temperature dimension with a precision of 0.6 spectral subclass, and in the luminosity dimension with a precision of about one half of a luminosity class. These results compare well with human classifiers.

  7. An expert computer program for classifying stars on the MK spectral classification system

    Energy Technology Data Exchange (ETDEWEB)

    Gray, R. O. [Department of Physics and Astronomy, Appalachian State University, Boone, NC 26808 (United States); Corbally, C. J. [Vatican Observatory Research Group, Tucson, AZ 85721-0065 (United States)

    2014-04-01

    This paper describes an expert computer program (MKCLASS) designed to classify stellar spectra on the MK Spectral Classification system in a way similar to humans—by direct comparison with the MK classification standards. Like an expert human classifier, the program first comes up with a rough spectral type, and then refines that spectral type by direct comparison with MK standards drawn from a standards library. A number of spectral peculiarities, including barium stars, Ap and Am stars, λ Bootis stars, carbon-rich giants, etc., can be detected and classified by the program. The program also evaluates the quality of the delivered spectral type. The program currently is capable of classifying spectra in the violet-green region in either the rectified or flux-calibrated format, although the accuracy of the flux calibration is not important. We report on tests of MKCLASS on spectra classified by human classifiers; those tests suggest that over the entire HR diagram, MKCLASS will classify in the temperature dimension with a precision of 0.6 spectral subclass, and in the luminosity dimension with a precision of about one half of a luminosity class. These results compare well with human classifiers.

  8. Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image Classification

    Directory of Open Access Journals (Sweden)

    Qingshan Liu

    2017-12-01

    Full Text Available This paper proposes a novel deep learning framework named bidirectional-convolutional long short term memory (Bi-CLSTM network to automatically learn the spectral-spatial features from hyperspectral images (HSIs. In the network, the issue of spectral feature extraction is considered as a sequence learning problem, and a recurrent connection operator across the spectral domain is used to address it. Meanwhile, inspired from the widely used convolutional neural network (CNN, a convolution operator across the spatial domain is incorporated into the network to extract the spatial feature. In addition, to sufficiently capture the spectral information, a bidirectional recurrent connection is proposed. In the classification phase, the learned features are concatenated into a vector and fed to a Softmax classifier via a fully-connected operator. To validate the effectiveness of the proposed Bi-CLSTM framework, we compare it with six state-of-the-art methods, including the popular 3D-CNN model, on three widely used HSIs (i.e., Indian Pines, Pavia University, and Kennedy Space Center. The obtained results show that Bi-CLSTM can improve the classification performance by almost 1.5 % as compared to 3D-CNN.

  9. a Novel Deep Convolutional Neural Network for Spectral-Spatial Classification of Hyperspectral Data

    Science.gov (United States)

    Li, N.; Wang, C.; Zhao, H.; Gong, X.; Wang, D.

    2018-04-01

    Spatial and spectral information are obtained simultaneously by hyperspectral remote sensing. Joint extraction of these information of hyperspectral image is one of most import methods for hyperspectral image classification. In this paper, a novel deep convolutional neural network (CNN) is proposed, which extracts spectral-spatial information of hyperspectral images correctly. The proposed model not only learns sufficient knowledge from the limited number of samples, but also has powerful generalization ability. The proposed framework based on three-dimensional convolution can extract spectral-spatial features of labeled samples effectively. Though CNN has shown its robustness to distortion, it cannot extract features of different scales through the traditional pooling layer that only have one size of pooling window. Hence, spatial pyramid pooling (SPP) is introduced into three-dimensional local convolutional filters for hyperspectral classification. Experimental results with a widely used hyperspectral remote sensing dataset show that the proposed model provides competitive performance.

  10. Spectral Classification of Asteroids by Random Forest

    Science.gov (United States)

    Huang, Chao; Ma, Yue-hua; Zhao, Hai-bin; Lu, Xiao-ping

    2017-10-01

    With the increasing spectral and photometric data of asteroids, a variety of classification methods for asteroids have been proposed. This paper classifies asteroids based on the observations in the Sloan Digital Sky Survey (SDSS) Moving Object Catalogue (MOC) by using the random forest algorithm. In combination with the present taxonomies of Tholen, Bus, Lazzaro, and DeMeo, and the principal component analysis, we have classified 48642 asteroids according to their SDSS magnitudes at the g, r, i, and z wavebands. In this way, these asteroids are divided into 8 (C, X, S, B, D, K, L, and V) classes.

  11. Discriminative illumination: per-pixel classification of raw materials based on optimal projections of spectral BRDF.

    Science.gov (United States)

    Liu, Chao; Gu, Jinwei

    2014-01-01

    Classifying raw, unpainted materials--metal, plastic, ceramic, fabric, and so on--is an important yet challenging task for computer vision. Previous works measure subsets of surface spectral reflectance as features for classification. However, acquiring the full spectral reflectance is time consuming and error-prone. In this paper, we propose to use coded illumination to directly measure discriminative features for material classification. Optimal illumination patterns--which we call "discriminative illumination"--are learned from training samples, after projecting to which the spectral reflectance of different materials are maximally separated. This projection is automatically realized by the integration of incident light for surface reflection. While a single discriminative illumination is capable of linear, two-class classification, we show that multiple discriminative illuminations can be used for nonlinear and multiclass classification. We also show theoretically that the proposed method has higher signal-to-noise ratio than previous methods due to light multiplexing. Finally, we construct an LED-based multispectral dome and use the discriminative illumination method for classifying a variety of raw materials, including metal (aluminum, alloy, steel, stainless steel, brass, and copper), plastic, ceramic, fabric, and wood. Experimental results demonstrate its effectiveness.

  12. Improved classification accuracy of powdery mildew infection levels of wine grapes by spatial-spectral analysis of hyperspectral images.

    Science.gov (United States)

    Knauer, Uwe; Matros, Andrea; Petrovic, Tijana; Zanker, Timothy; Scott, Eileen S; Seiffert, Udo

    2017-01-01

    Hyperspectral imaging is an emerging means of assessing plant vitality, stress parameters, nutrition status, and diseases. Extraction of target values from the high-dimensional datasets either relies on pixel-wise processing of the full spectral information, appropriate selection of individual bands, or calculation of spectral indices. Limitations of such approaches are reduced classification accuracy, reduced robustness due to spatial variation of the spectral information across the surface of the objects measured as well as a loss of information intrinsic to band selection and use of spectral indices. In this paper we present an improved spatial-spectral segmentation approach for the analysis of hyperspectral imaging data and its application for the prediction of powdery mildew infection levels (disease severity) of intact Chardonnay grape bunches shortly before veraison. Instead of calculating texture features (spatial features) for the huge number of spectral bands independently, dimensionality reduction by means of Linear Discriminant Analysis (LDA) was applied first to derive a few descriptive image bands. Subsequent classification was based on modified Random Forest classifiers and selective extraction of texture parameters from the integral image representation of the image bands generated. Dimensionality reduction, integral images, and the selective feature extraction led to improved classification accuracies of up to [Formula: see text] for detached berries used as a reference sample (training dataset). Our approach was validated by predicting infection levels for a sample of 30 intact bunches. Classification accuracy improved with the number of decision trees of the Random Forest classifier. These results corresponded with qPCR results. An accuracy of 0.87 was achieved in classification of healthy, infected, and severely diseased bunches. However, discrimination between visually healthy and infected bunches proved to be challenging for a few samples

  13. A Method of Particle Swarm Optimized SVM Hyper-spectral Remote Sensing Image Classification

    International Nuclear Information System (INIS)

    Liu, Q J; Jing, L H; Wang, L M; Lin, Q Z

    2014-01-01

    Support Vector Machine (SVM) has been proved to be suitable for classification of remote sensing image and proposed to overcome the Hughes phenomenon. Hyper-spectral sensors are intrinsically designed to discriminate among a broad range of land cover classes which may lead to high computational time in SVM mutil-class algorithms. Model selection for SVM involving kernel and the margin parameter values selection which is usually time-consuming, impacts training efficiency of SVM model and final classification accuracies of SVM hyper-spectral remote sensing image classifier greatly. Firstly, based on combinatorial optimization theory and cross-validation method, particle swarm algorithm is introduced to the optimal selection of SVM (PSSVM) kernel parameter σ and margin parameter C to improve the modelling efficiency of SVM model. Then an experiment of classifying AVIRIS in India Pine site of USA was performed for evaluating the novel PSSVM, as well as traditional SVM classifier with general Grid-Search cross-validation method (GSSVM). And then, evaluation indexes including SVM model training time, classification Overall Accuracy (OA) and Kappa index of both PSSVM and GSSVM are all analyzed quantitatively. It is demonstrated that OA of PSSVM on test samples and whole image are 85% and 82%, the differences with that of GSSVM are both within 0.08% respectively. And Kappa indexes reach 0.82 and 0.77, the differences with that of GSSVM are both within 0.001. While the modelling time of PSSVM can be only 1/10 of that of GSSVM, and the modelling. Therefore, PSSVM is an fast and accurate algorithm for hyper-spectral image classification and is superior to GSSVM

  14. Improving Spectral Image Classification through Band-Ratio Optimization and Pixel Clustering

    Science.gov (United States)

    O'Neill, M.; Burt, C.; McKenna, I.; Kimblin, C.

    2017-12-01

    The Underground Nuclear Explosion Signatures Experiment (UNESE) seeks to characterize non-prompt observables from underground nuclear explosions (UNE). As part of this effort, we evaluated the ability of DigitalGlobe's WorldView-3 (WV3) to detect and map UNE signatures. WV3 is the current state-of-the-art, commercial, multispectral imaging satellite; however, it has relatively limited spectral and spatial resolutions. These limitations impede image classifiers from detecting targets that are spatially small and lack distinct spectral features. In order to improve classification results, we developed custom algorithms to reduce false positive rates while increasing true positive rates via a band-ratio optimization and pixel clustering front-end. The clusters resulting from these algorithms were processed with standard spectral image classifiers such as Mixture-Tuned Matched Filter (MTMF) and Adaptive Coherence Estimator (ACE). WV3 and AVIRIS data of Cuprite, Nevada, were used as a validation data set. These data were processed with a standard classification approach using MTMF and ACE algorithms. They were also processed using the custom front-end prior to the standard approach. A comparison of the results shows that the custom front-end significantly increases the true positive rate and decreases the false positive rate.This work was done by National Security Technologies, LLC, under Contract No. DE-AC52-06NA25946 with the U.S. Department of Energy. DOE/NV/25946-3283.

  15. Combining Spectral Data and a DSM from UAS-Images for Improved Classification of Non-Submerged Aquatic Vegetation

    Directory of Open Access Journals (Sweden)

    Eva Husson

    2017-03-01

    Full Text Available Monitoring of aquatic vegetation is an important component in the assessment of freshwater ecosystems. Remote sensing with unmanned aircraft systems (UASs can provide sub-decimetre-resolution aerial images and is a useful tool for detailed vegetation mapping. In a previous study, non-submerged aquatic vegetation was successfully mapped using automated classification of spectral and textural features from a true-colour UAS-orthoimage with 5-cm pixels. In the present study, height data from a digital surface model (DSM created from overlapping UAS-images has been incorporated together with the spectral and textural features from the UAS-orthoimage to test if classification accuracy can be improved further. We studied two levels of thematic detail: (a Growth forms including the classes of water, nymphaeid, and helophyte; and (b dominant taxa including seven vegetation classes. We hypothesized that the incorporation of height data together with spectral and textural features would increase classification accuracy as compared to using spectral and textural features alone, at both levels of thematic detail. We tested our hypothesis at five test sites (100 m × 100 m each with varying vegetation complexity and image quality using automated object-based image analysis in combination with Random Forest classification. Overall accuracy at each of the five test sites ranged from 78% to 87% at the growth-form level and from 66% to 85% at the dominant-taxon level. In comparison to using spectral and textural features alone, the inclusion of height data increased the overall accuracy significantly by 4%–21% for growth-forms and 3%–30% for dominant taxa. The biggest improvement gained by adding height data was observed at the test site with the most complex vegetation. Height data derived from UAS-images has a large potential to efficiently increase the accuracy of automated classification of non-submerged aquatic vegetation, indicating good possibilities

  16. Object-Based Land Use Classification of Agricultural Land by Coupling Multi-Temporal Spectral Characteristics and Phenological Events in Germany

    Science.gov (United States)

    Knoefel, Patrick; Loew, Fabian; Conrad, Christopher

    2015-04-01

    Crop maps based on classification of remotely sensed data are of increased attendance in agricultural management. This induces a more detailed knowledge about the reliability of such spatial information. However, classification of agricultural land use is often limited by high spectral similarities of the studied crop types. More, spatially and temporally varying agro-ecological conditions can introduce confusion in crop mapping. Classification errors in crop maps in turn may have influence on model outputs, like agricultural production monitoring. One major goal of the PhenoS project ("Phenological structuring to determine optimal acquisition dates for Sentinel-2 data for field crop classification"), is the detection of optimal phenological time windows for land cover classification purposes. Since many crop species are spectrally highly similar, accurate classification requires the right selection of satellite images for a certain classification task. In the course of one growing season, phenological phases exist where crops are separable with higher accuracies. For this purpose, coupling of multi-temporal spectral characteristics and phenological events is promising. The focus of this study is set on the separation of spectrally similar cereal crops like winter wheat, barley, and rye of two test sites in Germany called "Harz/Central German Lowland" and "Demmin". However, this study uses object based random forest (RF) classification to investigate the impact of image acquisition frequency and timing on crop classification uncertainty by permuting all possible combinations of available RapidEye time series recorded on the test sites between 2010 and 2014. The permutations were applied to different segmentation parameters. Then, classification uncertainty was assessed and analysed, based on the probabilistic soft-output from the RF algorithm at the per-field basis. From this soft output, entropy was calculated as a spatial measure of classification uncertainty

  17. Spectral classification by the near infrared photometric parameters

    International Nuclear Information System (INIS)

    Tignanelli, H.L.; Feinstein, A.

    1985-01-01

    From the analysis of the measurements of KM-type stars done in the near infrared (1 to 3.5 microns: the JHKL bands of Johnsons's system), with an 83 cm reflector and a PbS detector at La Plata Observatory, we try to establish a new photometric classification system that discriminates luminosity class by means of certain parameters defined by infrared colours and infrared magnitudes. Data compiled and homogenized by J.Koornneef of southern bright stars in those bands were also included. The results give us information about the spectral types and reddening of those stars. We also indicate how to calculate the radiation excess that those stars could have. (author)

  18. SPECTRAL CLASSIFICATION AND PROPERTIES OF THE O Vz STARS IN THE GALACTIC O-STAR SPECTROSCOPIC SURVEY (GOSSS)

    International Nuclear Information System (INIS)

    Arias, Julia I.; Barbá, Rodolfo H.; Sabín-Sanjulián, Carolina; Walborn, Nolan R.; Díaz, Sergio Simón; Apellániz, Jesús Maíz; Gamen, Roberto C.; Morrell, Nidia I.; Sota, Alfredo; Marco, Amparo; Negueruela, Ignacio

    2016-01-01

    On the basis of the Galactic O Star Spectroscopic Survey (GOSSS), we present a detailed systematic investigation of the O Vz stars. The currently used spectral classification criteria are rediscussed, and the Vz phenomenon is recalibrated through the addition of a quantitative criterion based on the equivalent widths of the He i λ 4471, He ii λ 4542, and He ii λ 4686 spectral lines. The GOSSS O Vz and O V populations resulting from the newly adopted spectral classification criteria are comparatively analyzed. The locations of the O Vz stars are probed, showing a concentration of the most extreme cases toward the youngest star-forming regions. The occurrence of the Vz spectral peculiarity in a solar-metallicity environment, as predicted by the fastwind code, is also investigated, confirming the importance of taking into account several processes for the correct interpretation of the phenomenon.

  19. SPECTRAL CLASSIFICATION AND PROPERTIES OF THE O Vz STARS IN THE GALACTIC O-STAR SPECTROSCOPIC SURVEY (GOSSS)

    Energy Technology Data Exchange (ETDEWEB)

    Arias, Julia I.; Barbá, Rodolfo H.; Sabín-Sanjulián, Carolina [Departamento de Física y Astronomía, Universidad de La Serena, Av. Cisternas 1200 Norte, La Serena (Chile); Walborn, Nolan R. [Space Telescope Science Institute, 3700 San Martin Drive, MD 21218, Baltimore (United States); Díaz, Sergio Simón [Instituto de Astrofísica de Canarias, E-38200, Departamento de Astrofísica, Universidad de La Laguna, E-38205, La Laguna, Tenerife (Spain); Apellániz, Jesús Maíz [Centro de Astrobiología, CSIC-INTA, campus ESAC, Camino Bajo del Castillo s/n, E-28 692 Madrid (Spain); Gamen, Roberto C. [Instituto de Astrofísica de La Plata (CONICET, UNLP), Facultad de Ciencias Astronómicas y Geofísicas, Universidad Nacional de La Plata, Paseo del Bosque s/n, 1900 La Plata (Argentina); Morrell, Nidia I. [Las Campanas Observatory, Carnegie Observatories, Casilla 601, La Serena (Chile); Sota, Alfredo [Instituto de Astrofísica de Andalucía-CSIC, Glorieta de la Astronomía s/n, E-18 008 Granada (Spain); Marco, Amparo; Negueruela, Ignacio, E-mail: jarias@userena.cl [Departamento de Física, Ingeniería de Sistemas y Teoría de la Señal, Escuela Politécnica Superior, Universidad de Alicante, Carretera San Vicente del Raspeig s/n, E03690, San Vicente del Raspeig (Spain); and others

    2016-08-01

    On the basis of the Galactic O Star Spectroscopic Survey (GOSSS), we present a detailed systematic investigation of the O Vz stars. The currently used spectral classification criteria are rediscussed, and the Vz phenomenon is recalibrated through the addition of a quantitative criterion based on the equivalent widths of the He i λ 4471, He ii λ 4542, and He ii λ 4686 spectral lines. The GOSSS O Vz and O V populations resulting from the newly adopted spectral classification criteria are comparatively analyzed. The locations of the O Vz stars are probed, showing a concentration of the most extreme cases toward the youngest star-forming regions. The occurrence of the Vz spectral peculiarity in a solar-metallicity environment, as predicted by the fastwind code, is also investigated, confirming the importance of taking into account several processes for the correct interpretation of the phenomenon.

  20. On the classification of the spectrally stable standing waves of the Hartree problem

    Science.gov (United States)

    Georgiev, Vladimir; Stefanov, Atanas

    2018-05-01

    We consider the fractional Hartree model, with general power non-linearity and arbitrary spatial dimension. We construct variationally the "normalized" solutions for the corresponding Choquard-Pekar model-in particular a number of key properties, like smoothness and bell-shapedness are established. As a consequence of the construction, we show that these solitons are spectrally stable as solutions to the time-dependent Hartree model. In addition, we analyze the spectral stability of the Moroz-Van Schaftingen solitons of the classical Hartree problem, in any dimensions and power non-linearity. A full classification is obtained, the main conclusion of which is that only and exactly the "normalized" solutions (which exist only in a portion of the range) are spectrally stable.

  1. Automated classification and visualization of healthy and pathological dental tissues based on near-infrared hyper-spectral imaging

    Science.gov (United States)

    Usenik, Peter; Bürmen, Miran; Vrtovec, Tomaž; Fidler, Aleš; Pernuš, Franjo; Likar, Boštjan

    2011-03-01

    Despite major improvements in dental healthcare and technology, dental caries remains one of the most prevalent chronic diseases of modern society. The initial stages of dental caries are characterized by demineralization of enamel crystals, commonly known as white spots which are difficult to diagnose. If detected early enough, such demineralization can be arrested and reversed by non-surgical means through well established dental treatments (fluoride therapy, anti-bacterial therapy, low intensity laser irradiation). Near-infrared (NIR) hyper-spectral imaging is a new promising technique for early detection of demineralization based on distinct spectral features of healthy and pathological dental tissues. In this study, we apply NIR hyper-spectral imaging to classify and visualize healthy and pathological dental tissues including enamel, dentin, calculus, dentin caries, enamel caries and demineralized areas. For this purpose, a standardized teeth database was constructed consisting of 12 extracted human teeth with different degrees of natural dental lesions imaged by NIR hyper-spectral system, X-ray and digital color camera. The color and X-ray images of teeth were presented to a clinical expert for localization and classification of the dental tissues, thereby obtaining the gold standard. Principal component analysis was used for multivariate local modeling of healthy and pathological dental tissues. Finally, the dental tissues were classified by employing multiple discriminant analysis. High agreement was observed between the resulting classification and the gold standard with the classification sensitivity and specificity exceeding 85 % and 97 %, respectively. This study demonstrates that NIR hyper-spectral imaging has considerable diagnostic potential for imaging hard dental tissues.

  2. Performance Evaluation of Downscaling Sentinel-2 Imagery for Land Use and Land Cover Classification by Spectral-Spatial Features

    Directory of Open Access Journals (Sweden)

    Hongrui Zheng

    2017-12-01

    Full Text Available Land Use and Land Cover (LULC classification is vital for environmental and ecological applications. Sentinel-2 is a new generation land monitoring satellite with the advantages of novel spectral capabilities, wide coverage and fine spatial and temporal resolutions. The effects of different spatial resolution unification schemes and methods on LULC classification have been scarcely investigated for Sentinel-2. This paper bridged this gap by comparing the differences between upscaling and downscaling as well as different downscaling algorithms from the point of view of LULC classification accuracy. The studied downscaling algorithms include nearest neighbor resampling and five popular pansharpening methods, namely, Gram-Schmidt (GS, nearest neighbor diffusion (NNDiffusion, PANSHARP algorithm proposed by Y. Zhang, wavelet transformation fusion (WTF and high-pass filter fusion (HPF. Two spatial features, textural metrics derived from Grey-Level-Co-occurrence Matrix (GLCM and extended attribute profiles (EAPs, are investigated to make up for the shortcoming of pixel-based spectral classification. Random forest (RF is adopted as the classifier. The experiment was conducted in Xitiaoxi watershed, China. The results demonstrated that downscaling obviously outperforms upscaling in terms of classification accuracy. For downscaling, image sharpening has no obvious advantages than spatial interpolation. Different image sharpening algorithms have distinct effects. Two multiresolution analysis (MRA-based methods, i.e., WTF and HFP, achieve the best performance. GS achieved a similar accuracy with NNDiffusion and PANSHARP. Compared to image sharpening, the introduction of spatial features, both GLCM and EAPs can greatly improve the classification accuracy for Sentinel-2 imagery. Their effects on overall accuracy are similar but differ significantly to specific classes. In general, using the spectral bands downscaled by nearest neighbor interpolation can meet

  3. Classification of Hyperspectral or Trichromatic Measurements of Ocean Color Data into Spectral Classes

    Directory of Open Access Journals (Sweden)

    Dilip K. Prasad

    2016-03-01

    Full Text Available We propose a method for classifying radiometric oceanic color data measured by hyperspectral satellite sensors into known spectral classes, irrespective of the downwelling irradiance of the particular day, i.e., the illumination conditions. The focus is not on retrieving the inherent optical properties but to classify the pixels according to the known spectral classes of the reflectances from the ocean. The method compensates for the unknown downwelling irradiance by white balancing the radiometric data at the ocean pixels using the radiometric data of bright pixels (typically from clouds. The white-balanced data is compared with the entries in a pre-calibrated lookup table in which each entry represents the spectral properties of one class. The proposed approach is tested on two datasets of in situ measurements and 26 different daylight illumination spectra for medium resolution imaging spectrometer (MERIS, moderate-resolution imaging spectroradiometer (MODIS, sea-viewing wide field-of-view sensor (SeaWiFS, coastal zone color scanner (CZCS, ocean and land colour instrument (OLCI, and visible infrared imaging radiometer suite (VIIRS sensors. Results are also shown for CIMEL’s SeaPRISM sun photometer sensor used on-board field trips. Accuracy of more than 92% is observed on the validation dataset and more than 86% is observed on the other dataset for all satellite sensors. The potential of applying the algorithms to non-satellite and non-multi-spectral sensors mountable on airborne systems is demonstrated by showing classification results for two consumer cameras. Classification on actual MERIS data is also shown. Additional results comparing the spectra of remote sensing reflectance with level 2 MERIS data and chlorophyll concentration estimates of the data are included.

  4. Classification of Hyperspectral Images by SVM Using a Composite Kernel by Employing Spectral, Spatial and Hierarchical Structure Information

    Directory of Open Access Journals (Sweden)

    Yi Wang

    2018-03-01

    Full Text Available In this paper, we introduce a novel classification framework for hyperspectral images (HSIs by jointly employing spectral, spatial, and hierarchical structure information. In this framework, the three types of information are integrated into the SVM classifier in a way of multiple kernels. Specifically, the spectral kernel is constructed through each pixel’s vector value in the original HSI, and the spatial kernel is modeled by using the extended morphological profile method due to its simplicity and effectiveness. To accurately characterize hierarchical structure features, the techniques of Fish-Markov selector (FMS, marker-based hierarchical segmentation (MHSEG and algebraic multigrid (AMG are combined. First, the FMS algorithm is used on the original HSI for feature selection to produce its spectral subset. Then, the multigrid structure of this subset is constructed using the AMG method. Subsequently, the MHSEG algorithm is exploited to obtain a hierarchy consist of a series of segmentation maps. Finally, the hierarchical structure information is represented by using these segmentation maps. The main contributions of this work is to present an effective composite kernel for HSI classification by utilizing spatial structure information in multiple scales. Experiments were conducted on two hyperspectral remote sensing images to validate that the proposed framework can achieve better classification results than several popular kernel-based classification methods in terms of both qualitative and quantitative analysis. Specifically, the proposed classification framework can achieve 13.46–15.61% in average higher than the standard SVM classifier under different training sets in the terms of overall accuracy.

  5. Spectral-spatial classification of hyperspectral image using three-dimensional convolution network

    Science.gov (United States)

    Liu, Bing; Yu, Xuchu; Zhang, Pengqiang; Tan, Xiong; Wang, Ruirui; Zhi, Lu

    2018-01-01

    Recently, hyperspectral image (HSI) classification has become a focus of research. However, the complex structure of an HSI makes feature extraction difficult to achieve. Most current methods build classifiers based on complex handcrafted features computed from the raw inputs. The design of an improved 3-D convolutional neural network (3D-CNN) model for HSI classification is described. This model extracts features from both the spectral and spatial dimensions through the application of 3-D convolutions, thereby capturing the important discrimination information encoded in multiple adjacent bands. The designed model views the HSI cube data altogether without relying on any pre- or postprocessing. In addition, the model is trained in an end-to-end fashion without any handcrafted features. The designed model was applied to three widely used HSI datasets. The experimental results demonstrate that the 3D-CNN-based method outperforms conventional methods even with limited labeled training samples.

  6. On the construction of a new stellar classification template library for the LAMOST spectral analysis pipeline

    Energy Technology Data Exchange (ETDEWEB)

    Wei, Peng; Luo, Ali; Li, Yinbi; Tu, Liangping; Wang, Fengfei; Zhang, Jiannan; Chen, Xiaoyan; Hou, Wen; Kong, Xiao; Wu, Yue; Zuo, Fang; Yi, Zhenping; Zhao, Yongheng; Chen, Jianjun; Du, Bing; Guo, Yanxin; Ren, Juanjuan [Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012 (China); Pan, Jingchang; Jiang, Bin; Liu, Jie, E-mail: lal@nao.cas.cn, E-mail: weipeng@nao.cas.cn [School of Mechanical, Electrical, and Information Engineering, Shandong University, Weihai 264209 (China); and others

    2014-05-01

    The LAMOST spectral analysis pipeline, called the 1D pipeline, aims to classify and measure the spectra observed in the LAMOST survey. Through this pipeline, the observed stellar spectra are classified into different subclasses by matching with template spectra. Consequently, the performance of the stellar classification greatly depends on the quality of the template spectra. In this paper, we construct a new LAMOST stellar spectral classification template library, which is supposed to improve the precision and credibility of the present LAMOST stellar classification. About one million spectra are selected from LAMOST Data Release One to construct the new stellar templates, and they are gathered in 233 groups by two criteria: (1) pseudo g – r colors obtained by convolving the LAMOST spectra with the Sloan Digital Sky Survey ugriz filter response curve, and (2) the stellar subclass given by the LAMOST pipeline. In each group, the template spectra are constructed using three steps. (1) Outliers are excluded using the Local Outlier Probabilities algorithm, and then the principal component analysis method is applied to the remaining spectra of each group. About 5% of the one million spectra are ruled out as outliers. (2) All remaining spectra are reconstructed using the first principal components of each group. (3) The weighted average spectrum is used as the template spectrum in each group. Using the previous 3 steps, we initially obtain 216 stellar template spectra. We visually inspect all template spectra, and 29 spectra are abandoned due to low spectral quality. Furthermore, the MK classification for the remaining 187 template spectra is manually determined by comparing with 3 template libraries. Meanwhile, 10 template spectra whose subclass is difficult to determine are abandoned. Finally, we obtain a new template library containing 183 LAMOST template spectra with 61 different MK classes by combining it with the current library.

  7. A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data

    Directory of Open Access Journals (Sweden)

    Himmelreich Uwe

    2009-07-01

    Full Text Available Abstract Background Regularized regression methods such as principal component or partial least squares regression perform well in learning tasks on high dimensional spectral data, but cannot explicitly eliminate irrelevant features. The random forest classifier with its associated Gini feature importance, on the other hand, allows for an explicit feature elimination, but may not be optimally adapted to spectral data due to the topology of its constituent classification trees which are based on orthogonal splits in feature space. Results We propose to combine the best of both approaches, and evaluated the joint use of a feature selection based on a recursive feature elimination using the Gini importance of random forests' together with regularized classification methods on spectral data sets from medical diagnostics, chemotaxonomy, biomedical analytics, food science, and synthetically modified spectral data. Here, a feature selection using the Gini feature importance with a regularized classification by discriminant partial least squares regression performed as well as or better than a filtering according to different univariate statistical tests, or using regression coefficients in a backward feature elimination. It outperformed the direct application of the random forest classifier, or the direct application of the regularized classifiers on the full set of features. Conclusion The Gini importance of the random forest provided superior means for measuring feature relevance on spectral data, but – on an optimal subset of features – the regularized classifiers might be preferable over the random forest classifier, in spite of their limitation to model linear dependencies only. A feature selection based on Gini importance, however, may precede a regularized linear classification to identify this optimal subset of features, and to earn a double benefit of both dimensionality reduction and the elimination of noise from the classification task.

  8. Assessing the Impact of Spectral Resolution on Classification of Lowland Native Grassland Communities Based on Field Spectroscopy in Tasmania, Australia

    Directory of Open Access Journals (Sweden)

    Bethany Melville

    2018-02-01

    Full Text Available This paper presents a case study for the analysis of endangered lowland native grassland communities in the Tasmanian Midlands region using field spectroscopy and spectral convolution techniques. The aim of the study was to determine whether there was significant improvement in classification accuracy for lowland native grasslands and other vegetation communities based on hyperspectral resolution datasets over multispectral equivalents. A spectral dataset was collected using an ASD Handheld-2 spectroradiometer at Tunbridge Township Lagoon. The study then employed a k-fold cross-validation approach for repeated classification of a full hyperspectral dataset, a reduced hyperspectral dataset, and two convoluted multispectral datasets. Classification was performed on each of the four datasets a total of 30 times, based on two different class configurations. The classes analysed were Themeda triandra grassland, Danthonia/Poa grassland, Wilsonia rotundifolia/Selliera radicans, saltpan, and a simplified C3 vegetation class. The results of the classifications were then tested for statistically significant differences using ANOVA and Tukey’s post-hoc comparisons. The results of the study indicated that hyperspectral resolution provides small but statistically significant increases in classification accuracy for Themeda and Danthonia grasslands. For other classes, differences in classification accuracy for all datasets were not statistically significant. The results obtained here indicate that there is some potential for enhanced detection of major lowland native grassland community types using hyperspectral resolution datasets, and that future analysis should prioritise good performance in these classes over others. This study presents a method for identification of optimal spectral resolution across multiple datasets, and constitutes an important case study for lowland native grassland mapping in Tasmania.

  9. The Galah Survey: Classification and Diagnostics with t-SNE Reduction of Spectral Information

    Energy Technology Data Exchange (ETDEWEB)

    Traven, G.; Zwitter, T.; Žerjal, M. [Faculty of Mathematics and Physics, University of Ljubljana, Jadranska 19, 1000 Ljubljana (Slovenia); Matijevič, G. [Leibniz-Institut für Astrophysik Potsdam (AIP), An der Sternwarte 16, D-14482 Potsdam (Germany); Kos, J.; Bland-Hawthorn, J.; De Silva, G.; Sharma, S. [Sydney Institute for Astronomy, School of Physics, A28, The University of Sydney, NSW 2006 (Australia); Asplund, M.; Freeman, K.; Lin, J.; Da Costa, G.; Duong, L. [Research School of Astronomy and Astrophysics, Australian National University, Canberra, ACT 2611 (Australia); Casey, A. R. [Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge CB3 0HA (United Kingdom); Martell, S. L. [School of Physics, University of New South Wales, Sydney, NSW 2052 (Australia); Schlesinger, K. J. [Research School of Astronomy and Astrophysics, Mount Stromlo Observatory, Cotter Road, Weston Creek, ACT 2611 (Australia); Simpson, J. D. [Australian Astronomical Observatory, North Ryde, NSW 2113 (Australia); Zucker, D. B. [Australian Astronomical Observatory, P.O. Box 915, North Ryde, NSW 1670 (Australia); Anguiano, B. [Department of Physics and Astronomy, Macquarie University, North Ryde, NSW 2109 (Australia); Horner, J., E-mail: gregor.traven@fmf.uni-lj.si [Computational Engineering and Science Research Centre, University of Southern Queensland, Towoomba QLD 4350 (Australia); and others

    2017-02-01

    Galah is an ongoing high-resolution spectroscopic survey with the goal of disentangling the formation history of the Milky Way using the fossil remnants of disrupted star formation sites that are now dispersed around the Galaxy. It is targeting a randomly selected magnitude-limited ( V ≤ 14) sample of stars, with the goal of observing one million objects. To date, 300,000 spectra have been obtained. Not all of them are correctly processed by parameter estimation pipelines, and we need to know about them. We present a semi-automated classification scheme that identifies different types of peculiar spectral morphologies in an effort to discover and flag potentially problematic spectra and thus help to preserve the integrity of the survey results. To this end, we employ the recently developed dimensionality reduction technique t-SNE ( t -distributed stochastic neighbor embedding), which enables us to represent the complex spectral morphology in a two-dimensional projection map while still preserving the properties of the local neighborhoods of spectra. We find that the majority (178,483) of the 209,533 Galah spectra considered in this study represents normal single stars, whereas 31,050 peculiar and problematic spectra with very diverse spectral features pertaining to 28,579 stars are distributed into 10 classification categories: hot stars, cool metal-poor giants, molecular absorption bands, binary stars, H α /H β emission, H α /H β emission superimposed on absorption, H α /H β P-Cygni, H α /H β inverted P-Cygni, lithium absorption, and problematic. Classified spectra with supplementary information are presented in the catalog, indicating candidates for follow-up observations and population studies of the short-lived phases of stellar evolution.

  10. PARALLEL IMPLEMENTATION OF MORPHOLOGICAL PROFILE BASED SPECTRAL-SPATIAL CLASSIFICATION SCHEME FOR HYPERSPECTRAL IMAGERY

    Directory of Open Access Journals (Sweden)

    B. Kumar

    2016-06-01

    Full Text Available Extended morphological profile (EMP is a good technique for extracting spectral-spatial information from the images but large size of hyperspectral images is an important concern for creating EMPs. However, with the availability of modern multi-core processors and commodity parallel processing systems like graphics processing units (GPUs at desktop level, parallel computing provides a viable option to significantly accelerate execution of such computations. In this paper, parallel implementation of an EMP based spectralspatial classification method for hyperspectral imagery is presented. The parallel implementation is done both on multi-core CPU and GPU. The impact of parallelization on speed up and classification accuracy is analyzed. For GPU, the implementation is done in compute unified device architecture (CUDA C. The experiments are carried out on two well-known hyperspectral images. It is observed from the experimental results that GPU implementation provides a speed up of about 7 times, while parallel implementation on multi-core CPU resulted in speed up of about 3 times. It is also observed that parallel implementation has no adverse impact on the classification accuracy.

  11. Public Undertakings and Imputability

    DEFF Research Database (Denmark)

    Ølykke, Grith Skovgaard

    2013-01-01

    In this article, the issue of impuability to the State of public undertakings’ decision-making is analysed and discussed in the context of the DSBFirst case. DSBFirst is owned by the independent public undertaking DSB and the private undertaking FirstGroup plc and won the contracts in the 2008...... Oeresund tender for the provision of passenger transport by railway. From the start, the services were provided at a loss, and in the end a part of DSBFirst was wound up. In order to frame the problems illustrated by this case, the jurisprudence-based imputability requirement in the definition of State aid...... in Article 107(1) TFEU is analysed. It is concluded that where the public undertaking transgresses the control system put in place by the State, conditions for imputability are not fulfilled, and it is argued that in the current state of law, there is no conditional link between the level of control...

  12. A Framework for Coxeter Spectral Classification of Finite Posets and Their Mesh Geometries of Roots

    Directory of Open Access Journals (Sweden)

    Daniel Simson

    2013-01-01

    Full Text Available Following our paper [Linear Algebra Appl. 433(2010, 699–717], we present a framework and computational tools for the Coxeter spectral classification of finite posets J≡(J,⪯. One of the main motivations for the study is an application of matrix representations of posets in representation theory explained by Drozd [Funct. Anal. Appl. 8(1974, 219–225]. We are mainly interested in a Coxeter spectral classification of posets J such that the symmetric Gram matrix GJ:=(1/2[CJ+CJtr]∈J(ℚ is positive semidefinite, where CJ∈J(ℤ is the incidence matrix of J. Following the idea of Drozd mentioned earlier, we associate to J its Coxeter matrix CoxJ:=-CJ·CJ-tr, its Coxeter spectrum speccJ, a Coxeter polynomial coxJ(t∈ℤ[t], and a Coxeter number  cJ. In case GJ is positive semi-definite, we also associate to J a reduced Coxeter number   čJ, and the defect homomorphism ∂J:ℤJ→ℤ. In this case, the Coxeter spectrum speccJ is a subset of the unit circle and consists of roots of unity. In case GJ is positive semi-definite of corank one, we relate the Coxeter spectral properties of the posets J with the Coxeter spectral properties of a simply laced Euclidean diagram DJ∈{̃n,̃6,̃7,̃8} associated with J. Our aim of the Coxeter spectral analysis of such posets J is to answer the question when the Coxeter type CtypeJ:=(speccJ,cJ,  čJ of J determines its incidence matrix CJ (and, hence, the poset J uniquely, up to a ℤ-congruency. In connection with this question, we also discuss the problem studied by Horn and Sergeichuk [Linear Algebra Appl. 389(2004, 347–353], if for any ℤ-invertible matrix A∈n(ℤ, there is B∈n(ℤ such that Atr=Btr·A·B and B2=E is the identity matrix.

  13. A k-mer-based barcode DNA classification methodology based on spectral representation and a neural gas network.

    Science.gov (United States)

    Fiannaca, Antonino; La Rosa, Massimo; Rizzo, Riccardo; Urso, Alfonso

    2015-07-01

    In this paper, an alignment-free method for DNA barcode classification that is based on both a spectral representation and a neural gas network for unsupervised clustering is proposed. In the proposed methodology, distinctive words are identified from a spectral representation of DNA sequences. A taxonomic classification of the DNA sequence is then performed using the sequence signature, i.e., the smallest set of k-mers that can assign a DNA sequence to its proper taxonomic category. Experiments were then performed to compare our method with other supervised machine learning classification algorithms, such as support vector machine, random forest, ripper, naïve Bayes, ridor, and classification tree, which also consider short DNA sequence fragments of 200 and 300 base pairs (bp). The experimental tests were conducted over 10 real barcode datasets belonging to different animal species, which were provided by the on-line resource "Barcode of Life Database". The experimental results showed that our k-mer-based approach is directly comparable, in terms of accuracy, recall and precision metrics, with the other classifiers when considering full-length sequences. In addition, we demonstrate the robustness of our method when a classification is performed task with a set of short DNA sequences that were randomly extracted from the original data. For example, the proposed method can reach the accuracy of 64.8% at the species level with 200-bp fragments. Under the same conditions, the best other classifier (random forest) reaches the accuracy of 20.9%. Our results indicate that we obtained a clear improvement over the other classifiers for the study of short DNA barcode sequence fragments. Copyright © 2015 Elsevier B.V. All rights reserved.

  14. VizieR Online Data Catalog: Catalogue of Stellar Spectral Classifications (Skiff, 2010)

    Science.gov (United States)

    Skiff, B. A.

    2009-02-01

    This file contains spectral classifications for stars collected from the literature, serving as a continuation of the compilations produced by the Jascheks, by Kennedy, and by Buscombe. The source of each spectral type is indicated by a standard 19-digit bibcode citation. These papers of course should be cited in publication, not this compilation. The stars are identified either by the name used in each publication or by a valid SIMBAD identifier. Some effort has been made to determine accurate (~1" or better) coordinates for equinox J2000 (and epoch 2000 if possible), and these serve as a secondary identifier. To the extent possible with current astrometric sources, the components of double stars and stars with composite spectra are shown as separate entries. Magnitudes are provided as an indication of brightness, but these data are not necessarily accurate, as they often derive from photographic photometry or rough estimates. The file includes only spectral types determined from spectra (viz. line and band strengths or ratios), omitting those determined from photometry (e.g. DDO, Vilnius) or inferred from broadband colors or spectral energy distributions. The classifications include MK types as well as types not strictly on the MK system (white dwarfs, Wolf-Rayet, etc), and in addition simple HD-style temperature types. Luminosity classes in the early Mount Wilson style (e.g. 'd' for dwarf, 'g' for giant) and other similar schemes have been converted to modern notation. Since a citation is provided for each entry, the source paper should be consulted for details about classification schemes, spectral dispersion, and instrumentation used. System-defining primary MK standard stars are included from the last lists by Morgan and Keenan, and are flagged by a + sign in column 83. The early-type standards comprise the 1973 "dagger standards" (1973ARA&A..11...29M) and stars from the Morgan, Abt, and Tapscott atlas (1978rmsa.book.....M). Standards from Table I of the

  15. Fourier transform infrared spectroscopy microscopic imaging classification based on spatial-spectral features

    Science.gov (United States)

    Liu, Lian; Yang, Xiukun; Zhong, Mingliang; Liu, Yao; Jing, Xiaojun; Yang, Qin

    2018-04-01

    The discrete fractional Brownian incremental random (DFBIR) field is used to describe the irregular, random, and highly complex shapes of natural objects such as coastlines and biological tissues, for which traditional Euclidean geometry cannot be used. In this paper, an anisotropic variable window (AVW) directional operator based on the DFBIR field model is proposed for extracting spatial characteristics of Fourier transform infrared spectroscopy (FTIR) microscopic imaging. Probabilistic principal component analysis first extracts spectral features, and then the spatial features of the proposed AVW directional operator are combined with the former to construct a spatial-spectral structure, which increases feature-related information and helps a support vector machine classifier to obtain more efficient distribution-related information. Compared to Haralick’s grey-level co-occurrence matrix, Gabor filters, and local binary patterns (e.g. uniform LBPs, rotation-invariant LBPs, uniform rotation-invariant LBPs), experiments on three FTIR spectroscopy microscopic imaging datasets show that the proposed AVW directional operator is more advantageous in terms of classification accuracy, particularly for low-dimensional spaces of spatial characteristics.

  16. A COMPARISON STUDY OF DIFFERENT MARKER SELECTION METHODS FOR SPECTRAL-SPATIAL CLASSIFICATION OF HYPERSPECTRAL IMAGES

    Directory of Open Access Journals (Sweden)

    D. Akbari

    2015-12-01

    Full Text Available An effective approach based on the Minimum Spanning Forest (MSF, grown from automatically selected markers using Support Vector Machines (SVM, has been proposed for spectral-spatial classification of hyperspectral images by Tarabalka et al. This paper aims at improving this approach by using image segmentation to integrate the spatial information into marker selection process. In this study, the markers are extracted from the classification maps, obtained by both SVM and segmentation algorithms, and then are used to build the MSF. The segmentation algorithms are the watershed, expectation maximization (EM and hierarchical clustering. These algorithms are used in parallel and independently to segment the image. Moreover, the pixels of each class, with the largest population in the classification map, are kept for each region of the segmentation map. Lastly, the most reliable classified pixels are chosen from among the exiting pixels as markers. Two benchmark urban hyperspectral datasets are used for evaluation: Washington DC Mall and Berlin. The results of our experiments indicate that, compared to the original MSF approach, the marker selection using segmentation algorithms leads in more accurate classification maps.

  17. Classification of Error-Diffused Halftone Images Based on Spectral Regression Kernel Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Zhigao Zeng

    2016-01-01

    Full Text Available This paper proposes a novel algorithm to solve the challenging problem of classifying error-diffused halftone images. We firstly design the class feature matrices, after extracting the image patches according to their statistics characteristics, to classify the error-diffused halftone images. Then, the spectral regression kernel discriminant analysis is used for feature dimension reduction. The error-diffused halftone images are finally classified using an idea similar to the nearest centroids classifier. As demonstrated by the experimental results, our method is fast and can achieve a high classification accuracy rate with an added benefit of robustness in tackling noise.

  18. VizieR Online Data Catalog: Catalogue of Stellar Spectral Classifications (Skiff, 2009-2016)

    Science.gov (United States)

    Skiff, B. A.

    2014-10-01

    This file contains spectral classifications for stars collected from the literature, serving as a continuation of the compilations produced by the Jascheks, by Kennedy, and by Buscombe. The source of each spectral type is indicated by a standard 19-digit bibcode citation. These papers of course should be cited in publication, not this compilation. The stars are identified either by the name used in each publication or by a valid SIMBAD identifier. Some effort has been made to determine accurate (~1" or better) coordinates for equinox J2000 (and epoch 2000 if possible), and these serve as a secondary identifier. To the extent possible with current astrometric sources, the components of double stars and stars with composite spectra are shown as separate entries. Magnitudes are provided as an indication of brightness, but these data are not necessarily accurate, as they often derive from photographic photometry or rough estimates. The file includes only spectral types determined from spectra (viz. line and band strengths or ratios), omitting those determined from photometry (e.g. DDO, Vilnius) or inferred from broadband colors or spectral energy distributions. The classifications include MK types as well as types not strictly on the MK system (white dwarfs, Wolf-Rayet, etc), and in addition simple HD-style temperature types. Luminosity classes in the early Mount Wilson style (e.g. 'd' for dwarf, 'g' for giant) and other similar schemes have been converted to modern notation. Since a citation is provided for each entry, the source paper should be consulted for details about classification schemes, spectral dispersion, and instrumentation used. System-defining primary MK standard stars are included from the last lists by Morgan and Keenan, and are flagged by a + sign in column 83. The early-type standards comprise the 1973 "dagger standards" (1973ARA&A..11...29M) and stars from the Morgan, Abt, and Tapscott atlas (1978rmsa.book.....M). Standards from Table I of the

  19. VizieR Online Data Catalog: Catalogue of Stellar Spectral Classifications (Skiff, 2009-2012)

    Science.gov (United States)

    Skiff, B. A.

    2010-11-01

    This file contains spectral classifications for stars collected from the literature, serving as a continuation of the compilations produced by the Jascheks, by Kennedy, and by Buscombe. The source of each spectral type is indicated by a standard 19-digit bibcode citation. These papers of course should be cited in publication, not this compilation. The stars are identified either by the name used in each publication or by a valid SIMBAD identifier. Some effort has been made to determine accurate (~1" or better) coordinates for equinox J2000 (and epoch 2000 if possible), and these serve as a secondary identifier. To the extent possible with current astrometric sources, the components of double stars and stars with composite spectra are shown as separate entries. Magnitudes are provided as an indication of brightness, but these data are not necessarily accurate, as they often derive from photographic photometry or rough estimates. The file includes only spectral types determined from spectra (viz. line and band strengths or ratios), omitting those determined from photometry (e.g. DDO, Vilnius) or inferred from broadband colors or spectral energy distributions. The classifications include MK types as well as types not strictly on the MK system (white dwarfs, Wolf-Rayet, etc), and in addition simple HD-style temperature types. Luminosity classes in the early Mount Wilson style (e.g. 'd' for dwarf, 'g' for giant) and other similar schemes have been converted to modern notation. Since a citation is provided for each entry, the source paper should be consulted for details about classification schemes, spectral dispersion, and instrumentation used. System-defining primary MK standard stars are included from the last lists by Morgan and Keenan, and are flagged by a + sign in column 83. The early-type standards comprise the 1973 "dagger standards" (1973ARA&A..11...29M) and stars from the Morgan, Abt, and Tapscott atlas (1978rmsa.book.....M). Standards from Table I of the

  20. VizieR Online Data Catalog: Catalogue of Stellar Spectral Classifications (Skiff, 2009-2014)

    Science.gov (United States)

    Skiff, B. A.

    2014-10-01

    This file contains spectral classifications for stars collected from the literature, serving as a continuation of the compilations produced by the Jascheks, by Kennedy, and by Buscombe. The source of each spectral type is indicated by a standard 19-digit bibcode citation. These papers of course should be cited in publication, not this compilation. The stars are identified either by the name used in each publication or by a valid SIMBAD identifier. Some effort has been made to determine accurate (~1" or better) coordinates for equinox J2000 (and epoch 2000 if possible), and these serve as a secondary identifier. To the extent possible with current astrometric sources, the components of double stars and stars with composite spectra are shown as separate entries. Magnitudes are provided as an indication of brightness, but these data are not necessarily accurate, as they often derive from photographic photometry or rough estimates. The file includes only spectral types determined from spectra (viz. line and band strengths or ratios), omitting those determined from photometry (e.g. DDO, Vilnius) or inferred from broadband colors or spectral energy distributions. The classifications include MK types as well as types not strictly on the MK system (white dwarfs, Wolf-Rayet, etc), and in addition simple HD-style temperature types. Luminosity classes in the early Mount Wilson style (e.g. 'd' for dwarf, 'g' for giant) and other similar schemes have been converted to modern notation. Since a citation is provided for each entry, the source paper should be consulted for details about classification schemes, spectral dispersion, and instrumentation used. System-defining primary MK standard stars are included from the last lists by Morgan and Keenan, and are flagged by a + sign in column 83. The early-type standards comprise the 1973 "dagger standards" (1973ARA&A..11...29M) and stars from the Morgan, Abt, and Tapscott atlas (1978rmsa.book.....M). Standards from Table I of the

  1. VizieR Online Data Catalog: Catalogue of Stellar Spectral Classifications (Skiff, 2009-2013)

    Science.gov (United States)

    Skiff, B. A.

    2013-05-01

    This file contains spectral classifications for stars collected from the literature, serving as a continuation of the compilations produced by the Jascheks, by Kennedy, and by Buscombe. The source of each spectral type is indicated by a standard 19-digit bibcode citation. These papers of course should be cited in publication, not this compilation. The stars are identified either by the name used in each publication or by a valid SIMBAD identifier. Some effort has been made to determine accurate (~1" or better) coordinates for equinox J2000 (and epoch 2000 if possible), and these serve as a secondary identifier. To the extent possible with current astrometric sources, the components of double stars and stars with composite spectra are shown as separate entries. Magnitudes are provided as an indication of brightness, but these data are not necessarily accurate, as they often derive from photographic photometry or rough estimates. The file includes only spectral types determined from spectra (viz. line and band strengths or ratios), omitting those determined from photometry (e.g. DDO, Vilnius) or inferred from broadband colors or spectral energy distributions. The classifications include MK types as well as types not strictly on the MK system (white dwarfs, Wolf-Rayet, etc), and in addition simple HD-style temperature types. Luminosity classes in the early Mount Wilson style (e.g. 'd' for dwarf, 'g' for giant) and other similar schemes have been converted to modern notation. Since a citation is provided for each entry, the source paper should be consulted for details about classification schemes, spectral dispersion, and instrumentation used. System-defining primary MK standard stars are included from the last lists by Morgan and Keenan, and are flagged by a + sign in column 83. The early-type standards comprise the 1973 "dagger standards" (1973ARA&A..11...29M) and stars from the Morgan, Abt, and Tapscott atlas (1978rmsa.book.....M). Standards from Table I of the

  2. An unsupervised technique for optimal feature selection in attribute profiles for spectral-spatial classification of hyperspectral images

    Science.gov (United States)

    Bhardwaj, Kaushal; Patra, Swarnajyoti

    2018-04-01

    Inclusion of spatial information along with spectral features play a significant role in classification of remote sensing images. Attribute profiles have already proved their ability to represent spatial information. In order to incorporate proper spatial information, multiple attributes are required and for each attribute large profiles need to be constructed by varying the filter parameter values within a wide range. Thus, the constructed profiles that represent spectral-spatial information of an hyperspectral image have huge dimension which leads to Hughes phenomenon and increases computational burden. To mitigate these problems, this work presents an unsupervised feature selection technique that selects a subset of filtered image from the constructed high dimensional multi-attribute profile which are sufficiently informative to discriminate well among classes. In this regard the proposed technique exploits genetic algorithms (GAs). The fitness function of GAs are defined in an unsupervised way with the help of mutual information. The effectiveness of the proposed technique is assessed using one-against-all support vector machine classifier. The experiments conducted on three hyperspectral data sets show the robustness of the proposed method in terms of computation time and classification accuracy.

  3. Spectral reflectance of carbonate sediments and application to remote sensing classification of benthic habitats

    Science.gov (United States)

    Louchard, Eric Michael

    Remote sensing is a valuable tool in marine research that has advanced to the point that images from shallow waters can be used to identify different seafloor types and create maps of benthic habitats. A major goal of this dissertation is to examine differences in spectral reflectance and create new methods of analyzing shallow water remote sensing data to identify different seafloor types quickly and accurately. Carbonate sediments were used as a model system as they presented a relatively uniform, smooth surface for measurement and are a major bottom type in tropical coral reef systems. Experimental results found that sediment reflectance varied in shape and magnitude depending on pigment content, but only varied in magnitude with variations in grain size and shape. Derivative analysis of the reflectance spectra identified wavelength regions that correlate to chlorophyll a and chlorophyllide a as well as accessory pigments, indicating differences in microbial community structure. Derivative peak height also correlated to pigment content in the sediments. In remote sensing data, chlorophyll a, chlorophyllide a, and some xanthophylls were identified in derivative spectra and could be quantified from second derivative peak height. Most accessory pigments were attenuated by the water column, however, and could not be used to quantify pigments in sediments from remote sensing images. Radiative transfer modeling of remote sensing reflectance showed that there was sufficient spectral variation to separate major sediment types, such as ooid shoals and sediment with microbial layers, from different densities of seagrass and pavement bottom communities. Both supervised classification with a spectral library and unsupervised classification with principal component analysis were used to create maps of seafloor type. The results of the experiments were promising; classified seafloor types correlated with ground truth observations taken from underwater video and were

  4. Data preprocessing methods of FT-NIR spectral data for the classification cooking oil

    Science.gov (United States)

    Ruah, Mas Ezatul Nadia Mohd; Rasaruddin, Nor Fazila; Fong, Sim Siong; Jaafar, Mohd Zuli

    2014-12-01

    This recent work describes the data pre-processing method of FT-NIR spectroscopy datasets of cooking oil and its quality parameters with chemometrics method. Pre-processing of near-infrared (NIR) spectral data has become an integral part of chemometrics modelling. Hence, this work is dedicated to investigate the utility and effectiveness of pre-processing algorithms namely row scaling, column scaling and single scaling process with Standard Normal Variate (SNV). The combinations of these scaling methods have impact on exploratory analysis and classification via Principle Component Analysis plot (PCA). The samples were divided into palm oil and non-palm cooking oil. The classification model was build using FT-NIR cooking oil spectra datasets in absorbance mode at the range of 4000cm-1-14000cm-1. Savitzky Golay derivative was applied before developing the classification model. Then, the data was separated into two sets which were training set and test set by using Duplex method. The number of each class was kept equal to 2/3 of the class that has the minimum number of sample. Then, the sample was employed t-statistic as variable selection method in order to select which variable is significant towards the classification models. The evaluation of data pre-processing were looking at value of modified silhouette width (mSW), PCA and also Percentage Correctly Classified (%CC). The results show that different data processing strategies resulting to substantial amount of model performances quality. The effects of several data pre-processing i.e. row scaling, column standardisation and single scaling process with Standard Normal Variate indicated by mSW and %CC. At two PCs model, all five classifier gave high %CC except Quadratic Distance Analysis.

  5. JET Joint Undertaking

    International Nuclear Information System (INIS)

    Keen, B.E.

    1987-03-01

    The paper presents the progress report of the Joint European Torus (JET) Joint Undertaking, 1986. The report contains a survey of the scientific and technical achievements on JET during 1986; the more important articles referred to in this survey are reproduced as appendices to this Report. The last section discusses developments which might improve the overall performance of the machine. (U.K.)

  6. Land Cover Classification in a Complex Urban-Rural Landscape with Quickbird Imagery

    OpenAIRE

    Moran, Emilio Federico.

    2010-01-01

    High spatial resolution images have been increasingly used for urban land use/cover classification, but the high spectral variation within the same land cover, the spectral confusion among different land covers, and the shadow problem often lead to poor classification performance based on the traditional per-pixel spectral-based classification methods. This paper explores approaches to improve urban land cover classification with Quickbird imagery. Traditional per-pixel spectral-based supervi...

  7. Image-Based Airborne Sensors: A Combined Approach for Spectral Signatures Classification through Deterministic Simulated Annealing

    Science.gov (United States)

    Guijarro, María; Pajares, Gonzalo; Herrera, P. Javier

    2009-01-01

    The increasing technology of high-resolution image airborne sensors, including those on board Unmanned Aerial Vehicles, demands automatic solutions for processing, either on-line or off-line, the huge amountds of image data sensed during the flights. The classification of natural spectral signatures in images is one potential application. The actual tendency in classification is oriented towards the combination of simple classifiers. In this paper we propose a combined strategy based on the Deterministic Simulated Annealing (DSA) framework. The simple classifiers used are the well tested supervised parametric Bayesian estimator and the Fuzzy Clustering. The DSA is an optimization approach, which minimizes an energy function. The main contribution of DSA is its ability to avoid local minima during the optimization process thanks to the annealing scheme. It outperforms simple classifiers used for the combination and some combined strategies, including a scheme based on the fuzzy cognitive maps and an optimization approach based on the Hopfield neural network paradigm. PMID:22399989

  8. Central stars of planetary nebulae: New spectral classifications and catalogue

    Science.gov (United States)

    Weidmann, W. A.; Gamen, R.

    2011-02-01

    Context. There are more than 3000 confirmed and probable known Galactic planetary nebulae (PNe), but central star spectroscopic information is available for only 13% of them. Aims: We undertook a spectroscopic survey of central stars of PNe at low resolution and compiled a large list of central stars for which information was dispersed in the literature. Methods: We observed 45 PNs using the 2.15 m telescope at Casleo, Argentina. Results: We present a catalogue of 492 confirmed and probable CSPN and provide a preliminary spectral classification for 45 central star of PNe. This revises previous values of the proportion of CSPN with atmospheres poor in hydrogen in at least 30% of cases and provide statistical information that allows us to infer the origin of H-poor stars. Based on data collected at the Complejo Astronómico El Leoncito (CASLEO), which is operated under agreement between the Consejo Nacional de Investigaciones Científicas y Técnicas de la República Argentina y Universidades Nacionales de La Plata, Córdoba y San Juan, Argentina.

  9. Spectral classification of emission-line galaxies

    International Nuclear Information System (INIS)

    Veilleux, S.; Osterbrock, D.E.

    1987-01-01

    A revised method of classification of narrow-line active galaxies and H II region-like galaxies is proposed. It involves the line ratios which take full advantage of the physical distinction between the two types of objects and minimize the effects of reddening correction and errors in the flux calibration. Large sets of internally consistent data are used, including new, previously unpublished measurements. Predictions of recent photoionization models by power-law spectra and by hot stars are compared with the observations. The classification is based on the observational data interpreted on the basis of these models. 63 references

  10. Metallicity and the spectral energy distribution and spectral types of dwarf O-stars

    NARCIS (Netherlands)

    Mokiem, MR; Martin-Hernandez, NL; Lenorzer, A; de Koter, A; Tielens, AGGA

    We present a systematic study of the effect of metallicity on the stellar spectral energy distribution (SED) of 0 main sequence (dwarf) stars, focussing on the hydrogen and helium ionizing continua, and on the optical and near-IR lines used for spectral classification. The spectra are based on

  11. Metallicity and the spectral energy distribution and spectral types of dwarf O-stars

    NARCIS (Netherlands)

    Mokiem, M.R.; Martín-Hernández, N.L.; Lenorzer, A.; de Koter, A.; Tielens, A.G.G.M.

    2004-01-01

    We present a systematic study of the effect of metallicity on the stellar spectral energy distribution (SED) of O main sequence (dwarf) stars, focussing on the hydrogen and helium ionizing continua, and on the optical and near-IR lines used for spectral classification. The spectra are based on

  12. Jet Joint Undertaking

    International Nuclear Information System (INIS)

    Keen, B.E.; O'Hara, G.W.; Pollard, I.E.

    1988-07-01

    The paper presents the Jet Joint Undertaking annual report 1987. A description is given of the JET and Euratom and International Fusion Programmes. The technical status of JET is outlined, including the development and improvements made to the system in 1987. The results of JET Operation in 1987 are described within the areas of: density effects, temperature improvements, energy confinement studies and other material effects. The contents also contain a summary of the future programme of JET. (U.K.)

  13. Comparison Between Spectral, Spatial and Polarimetric Classification of Urban and Periurban Landcover Using Temporal Sentinel - 1 Images

    Science.gov (United States)

    Roychowdhury, K.

    2016-06-01

    Landcover is the easiest detectable indicator of human interventions on land. Urban and peri-urban areas present a complex combination of landcover, which makes classification challenging. This paper assesses the different methods of classifying landcover using dual polarimetric Sentinel-1 data collected during monsoon (July) and winter (December) months of 2015. Four broad landcover classes such as built up areas, water bodies and wetlands, vegetation and open spaces of Kolkata and its surrounding regions were identified. Polarimetric analyses were conducted on Single Look Complex (SLC) data of the region while ground range detected (GRD) data were used for spectral and spatial classification. Unsupervised classification by means of K-Means clustering used backscatter values and was able to identify homogenous landcovers over the study area. The results produced an overall accuracy of less than 50% for both the seasons. Higher classification accuracy (around 70%) was achieved by adding texture variables as inputs along with the backscatter values. However, the accuracy of classification increased significantly with polarimetric analyses. The overall accuracy was around 80% in Wishart H-A-Alpha unsupervised classification. The method was useful in identifying urban areas due to their double-bounce scattering and vegetated areas, which have more random scattering. Normalized Difference Built-up index (NDBI) and Normalized Difference Vegetation Index (NDVI) obtained from Landsat 8 data over the study area were used to verify vegetation and urban classes. The study compares the accuracies of different methods of classifying landcover using medium resolution SAR data in a complex urban area and suggests that polarimetric analyses present the most accurate results for urban and suburban areas.

  14. Multispectral Image classification using the theories of neural networks

    International Nuclear Information System (INIS)

    Ardisasmita, M.S.; Subki, M.I.R.

    1997-01-01

    Image classification is the one of the important part of digital image analysis. the objective of image classification is to identify and regroup the features occurring in an image into one or several classes in terms of the object. basic to the understanding of multispectral classification is the concept of the spectral response of an object as a function of the electromagnetic radiation and the wavelength of the spectrum. new approaches to classification has been developed to improve the result of analysis, these state-of-the-art classifiers are based upon the theories of neural networks. Neural network classifiers are algorithmes which mimic the computational abilities of the human brain. Artificial neurons are simple emulation's of biological neurons; they take in information from sensors or other artificial neurons, perform very simple operations on this data, and pass the result to other recognize the spectral signature of each image pixel. Neural network image classification has been divided into supervised and unsupervised training procedures. In the supervised approach, examples of each cover type can be located and the computer can compute spectral signatures to categorize all pixels in a digital image into several land cover classes. In supervised classification, spectral signatures are generated by mathematically grouping and it does not require analyst-specified training data. Thus, in the supervised approach we define useful information categories and then examine their spectral reparability; in the unsupervised approach the computer determines spectrally sapable classes and then we define thei information value

  15. Price Undertakings, VERs, and Foreign Direct Investment

    OpenAIRE

    Ishikawa, Jota; Miyagiwa, Kaz

    2006-01-01

    We compare the relative effect of a voluntary export restraint (VER) and a price undertaking on foreign firms' incentive to engage in FDI. We emphasize foreign rivalry as a determinant of FDI. We show, in a model that has two foreign firms competing with a home firm in the home country, that a price undertaking induces more FDI than a VER. The home country government, operating under the constraint to protect the home firm, is generally better off settling an antidumping case with a VER than ...

  16. A computer method for spectral classification

    International Nuclear Information System (INIS)

    Appenzeller, I.; Zekl, H.

    1978-01-01

    The authors describe the start of an attempt to improve the accuracy of spectroscopic parallaxes by evaluating spectroscopic temperature and luminosity criteria such as those of the MK classification spectrograms which were analyzed automatically by means of a suitable computer program. (Auth.)

  17. Hyperspectral image classifier based on beach spectral feature

    International Nuclear Information System (INIS)

    Liang, Zhang; Lianru, Gao; Bing, Zhang

    2014-01-01

    The seashore, especially coral bank, is sensitive to human activities and environmental changes. A multispectral image, with coarse spectral resolution, is inadaptable for identify subtle spectral distinctions between various beaches. To the contrary, hyperspectral image with narrow and consecutive channels increases our capability to retrieve minor spectral features which is suit for identification and classification of surface materials on the shore. Herein, this paper used airborne hyperspectral data, in addition to ground spectral data to study the beaches in Qingdao. The image data first went through image pretreatment to deal with the disturbance of noise, radiation inconsistence and distortion. In succession, the reflection spectrum, the derivative spectrum and the spectral absorption features of the beach surface were inspected in search of diagnostic features. Hence, spectra indices specific for the unique environment of seashore were developed. According to expert decisions based on image spectrums, the beaches are ultimately classified into sand beach, rock beach, vegetation beach, mud beach, bare land and water. In situ surveying reflection spectrum from GER1500 field spectrometer validated the classification production. In conclusion, the classification approach under expert decision based on feature spectrum is proved to be feasible for beaches

  18. Adinkras, Dessins, Origami, and Supersymmetry Spectral Triples

    OpenAIRE

    Marcolli, Matilde; Zolman, Nick

    2016-01-01

    We investigate the spectral geometry and spectral action functionals associated to 1D Supersymmetry Algebras, using the classification of these superalgebras in terms of Adinkra graphs and the construction of associated dessin d'enfant and origami curves. The resulting spectral action functionals are computed in terms of the Selberg (super) trace formula.

  19. COMPARISON BETWEEN SPECTRAL, SPATIAL AND POLARIMETRIC CLASSIFICATION OF URBAN AND PERIURBAN LANDCOVER USING TEMPORAL SENTINEL – 1 IMAGES

    Directory of Open Access Journals (Sweden)

    K. Roychowdhury

    2016-06-01

    Full Text Available Landcover is the easiest detectable indicator of human interventions on land. Urban and peri-urban areas present a complex combination of landcover, which makes classification challenging. This paper assesses the different methods of classifying landcover using dual polarimetric Sentinel-1 data collected during monsoon (July and winter (December months of 2015. Four broad landcover classes such as built up areas, water bodies and wetlands, vegetation and open spaces of Kolkata and its surrounding regions were identified. Polarimetric analyses were conducted on Single Look Complex (SLC data of the region while ground range detected (GRD data were used for spectral and spatial classification. Unsupervised classification by means of K-Means clustering used backscatter values and was able to identify homogenous landcovers over the study area. The results produced an overall accuracy of less than 50% for both the seasons. Higher classification accuracy (around 70% was achieved by adding texture variables as inputs along with the backscatter values. However, the accuracy of classification increased significantly with polarimetric analyses. The overall accuracy was around 80% in Wishart H-A-Alpha unsupervised classification. The method was useful in identifying urban areas due to their double-bounce scattering and vegetated areas, which have more random scattering. Normalized Difference Built-up index (NDBI and Normalized Difference Vegetation Index (NDVI obtained from Landsat 8 data over the study area were used to verify vegetation and urban classes. The study compares the accuracies of different methods of classifying landcover using medium resolution SAR data in a complex urban area and suggests that polarimetric analyses present the most accurate results for urban and suburban areas.

  20. Importance of spatial and spectral data reduction in the detection of internal defects in food products.

    Science.gov (United States)

    Zhang, Xuechen; Nansen, Christian; Aryamanesh, Nader; Yan, Guijun; Boussaid, Farid

    2015-04-01

    Despite the importance of data reduction as part of the processing of reflection-based classifications, this study represents one of the first in which the effects of both spatial and spectral data reductions on classification accuracies are quantified. Furthermore, the effects of approaches to data reduction were quantified for two separate classification methods, linear discriminant analysis (LDA) and support vector machine (SVM). As the model dataset, reflection data were acquired using a hyperspectral camera in 230 spectral channels from 401 to 879 nm (spectral resolution of 2.1 nm) from field pea (Pisum sativum) samples with and without internal pea weevil (Bruchus pisorum) infestation. We deployed five levels of spatial data reduction (binning) and eight levels of spectral data reduction (40 datasets). Forward stepwise LDA was used to select and include only spectral channels contributing the most to the separation of pixels from non-infested and infested field peas. Classification accuracies obtained with LDA and SVM were based on the classification of independent validation datasets. Overall, SVMs had significantly higher classification accuracies than LDAs (P food products with internal defects, and it highlights that spatial and spectral data reductions can (1) improve classification accuracies, (2) vastly decrease computer constraints, and (3) reduce analytical concerns associated with classifications of large and high-dimensional datasets.

  1. Spectrally based mapping of riverbed composition

    Science.gov (United States)

    Legleiter, Carl; Stegman, Tobin K.; Overstreet, Brandon T.

    2016-01-01

    Remote sensing methods provide an efficient means of characterizing fluvial systems. This study evaluated the potential to map riverbed composition based on in situ and/or remote measurements of reflectance. Field spectra and substrate photos from the Snake River, Wyoming, USA, were used to identify different sediment facies and degrees of algal development and to quantify their optical characteristics. We hypothesized that accounting for the effects of depth and water column attenuation to isolate the reflectance of the streambed would enhance distinctions among bottom types and facilitate substrate classification. A bottom reflectance retrieval algorithm adapted from coastal research yielded realistic spectra for the 450 to 700 nm range; but bottom reflectance-based substrate classifications, generated using a random forest technique, were no more accurate than classifications derived from above-water field spectra. Additional hypothesis testing indicated that a combination of reflectance magnitude (brightness) and indices of spectral shape provided the most accurate riverbed classifications. Convolving field spectra to the response functions of a multispectral satellite and a hyperspectral imaging system did not reduce classification accuracies, implying that high spectral resolution was not essential. Supervised classifications of algal density produced from hyperspectral data and an inferred bottom reflectance image were not highly accurate, but unsupervised classification of the bottom reflectance image revealed distinct spectrally based clusters, suggesting that such an image could provide additional river information. We attribute the failure of bottom reflectance retrieval to yield more reliable substrate maps to a latent correlation between depth and bottom type. Accounting for the effects of depth might have eliminated a key distinction among substrates and thus reduced discriminatory power. Although further, more systematic study across a broader

  2. Virtual Satellite Construction and Application for Image Classification

    International Nuclear Information System (INIS)

    Su, W G; Su, F Z; Zhou, C H

    2014-01-01

    Nowadays, most remote sensing image classification uses single satellite remote sensing data, so the number of bands and band spectral width is consistent. In addition, observed phenomenon such as land cover have the same spectral signature, which causes the classification accuracy to decrease as different data have unique characteristic. Therefore, this paper analyzes different optical remote sensing satellites, comparing the spectral differences and proposes the ideas and methods to build a virtual satellite. This article illustrates the research on the TM, HJ-1 and MODIS data. We obtained the virtual band X 0 through these satellites' bands combined it with the 4 bands of a TM image to build a virtual satellite with five bands. Based on this, we used these data for image classification. The experimental results showed that the virtual satellite classification results of building land and water information were superior to the HJ-1 and TM data respectively

  3. Applying aerial digital photography as a spectral remote sensing technique for macrophytic cover assessment in small rural streams

    Science.gov (United States)

    Anker, Y.; Hershkovitz, Y.; Gasith, A.; Ben-Dor, E.

    2011-12-01

    Although remote sensing of fluvial ecosystems is well developed, the tradeoff between spectral and spatial resolutions prevents its application in small streams (habitat scales classifications, acquisition of aerial digital RGB datasets. B. For section scale classification, hyperspectral (HSR) dataset acquisition. C. For calibration, HSR reflectance measurements of specific ground targets, in close proximity to each dataset acquisition swath. D. For habitat scale classification, manual, in-stream flora grid transects classification. The digital RGB datasets were converted to reflectance units by spectral calibration against colored reference plates. These red, green, blue, white, and black EVA foam reference plates were measured by an ASD field spectrometer and each was given a spectral value. Each spectral value was later applied to the spectral calibration and radiometric correction of spectral RGB (SRGB) cube. Spectral calibration of the HSR dataset was done using the empirical line method, based on reference values of progressive grey scale targets. Differentiation between the vegetation species was done by supervised classification both for the HSR and for the SRGB datasets. This procedure was done using the Spectral Angle Mapper function with the spectral pattern of each vegetation species as a spectral end member. Comparison between the two remote sensing techniques and between the SRGB classification and the in-situ transects indicates that: A. Stream vegetation classification resolution is about 4 cm by the SRGB method compared to about 1 m by HSR. Moreover, this resolution is also higher than of the manual grid transect classification. B. The SRGB method is by far the most cost-efficient. The combination of spectral information (rather than the cognitive color) and high spatial resolution of aerial photography provides noise filtration and better sub-water detection capabilities than the HSR technique. C. Only the SRGB method applies for habitat and

  4. Behavioral state classification in epileptic brain using intracranial electrophysiology

    Science.gov (United States)

    Kremen, Vaclav; Duque, Juliano J.; Brinkmann, Benjamin H.; Berry, Brent M.; Kucewicz, Michal T.; Khadjevand, Fatemeh; Van Gompel, Jamie; Stead, Matt; St. Louis, Erik K.; Worrell, Gregory A.

    2017-04-01

    Objective. Automated behavioral state classification can benefit next generation implantable epilepsy devices. In this study we explored the feasibility of automated awake (AW) and slow wave sleep (SWS) classification using wide bandwidth intracranial EEG (iEEG) in patients undergoing evaluation for epilepsy surgery. Approach. Data from seven patients (age 34+/- 12 , 4 women) who underwent intracranial depth electrode implantation for iEEG monitoring were included. Spectral power features (0.1-600 Hz) spanning several frequency bands from a single electrode were used to train and test a support vector machine classifier. Main results. Classification accuracy of 97.8  ±  0.3% (normal tissue) and 89.4  ±  0.8% (epileptic tissue) across seven subjects using multiple spectral power features from a single electrode was achieved. Spectral power features from electrodes placed in normal temporal neocortex were found to be more useful (accuracy 90.8  ±  0.8%) for sleep-wake state classification than electrodes located in normal hippocampus (87.1  ±  1.6%). Spectral power in high frequency band features (Ripple (80-250 Hz), Fast Ripple (250-600 Hz)) showed comparable performance for AW and SWS classification as the best performing Berger bands (Alpha, Beta, low Gamma) with accuracy  ⩾90% using a single electrode contact and single spectral feature. Significance. Automated classification of wake and SWS should prove useful for future implantable epilepsy devices with limited computational power, memory, and number of electrodes. Applications include quantifying patient sleep patterns and behavioral state dependent detection, prediction, and electrical stimulation therapies.

  5. Spectral multi-energy CT texture analysis with machine learning for tissue classification: an investigation using classification of benign parotid tumours as a testing paradigm.

    Science.gov (United States)

    Al Ajmi, Eiman; Forghani, Behzad; Reinhold, Caroline; Bayat, Maryam; Forghani, Reza

    2018-06-01

    There is a rich amount of quantitative information in spectral datasets generated from dual-energy CT (DECT). In this study, we compare the performance of texture analysis performed on multi-energy datasets to that of virtual monochromatic images (VMIs) at 65 keV only, using classification of the two most common benign parotid neoplasms as a testing paradigm. Forty-two patients with pathologically proven Warthin tumour (n = 25) or pleomorphic adenoma (n = 17) were evaluated. Texture analysis was performed on VMIs ranging from 40 to 140 keV in 5-keV increments (multi-energy analysis) or 65-keV VMIs only, which is typically considered equivalent to single-energy CT. Random forest (RF) models were constructed for outcome prediction using separate randomly selected training and testing sets or the entire patient set. Using multi-energy texture analysis, tumour classification in the independent testing set had accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 92%, 86%, 100%, 100%, and 83%, compared to 75%, 57%, 100%, 100%, and 63%, respectively, for single-energy analysis. Multi-energy texture analysis demonstrates superior performance compared to single-energy texture analysis of VMIs at 65 keV for classification of benign parotid tumours. • We present and validate a paradigm for texture analysis of DECT scans. • Multi-energy dataset texture analysis is superior to single-energy dataset texture analysis. • DECT texture analysis has high accura\\cy for diagnosis of benign parotid tumours. • DECT texture analysis with machine learning can enhance non-invasive diagnostic tumour evaluation.

  6. JET Joint Undertaking

    International Nuclear Information System (INIS)

    Keen, B.E.; Lallia, P.; O'Hara, G.W.; Pollard, I.E.

    1987-06-01

    The paper presents the annual report of the Joint European Torus (JET) Joint Undertaking, 1986. The report is divided into two parts: a part on the scientific and technical programme of the project, and a part setting out the administration and organisation of the Project. The first part includes: a summary of the main features of the JET apparatus, the JET experimental programme, the position of the Project in the overall Euratom programme, and how JET relates to other large fusion devices throughout the world. In addition, the technical status of JET is described, as well as the results of the JET operations in 1986. The final section of the first part outlines the proposed future programme of JET. (U.K.)

  7. An Active Learning Framework for Hyperspectral Image Classification Using Hierarchical Segmentation

    Science.gov (United States)

    Zhang, Zhou; Pasolli, Edoardo; Crawford, Melba M.; Tilton, James C.

    2015-01-01

    Augmenting spectral data with spatial information for image classification has recently gained significant attention, as classification accuracy can often be improved by extracting spatial information from neighboring pixels. In this paper, we propose a new framework in which active learning (AL) and hierarchical segmentation (HSeg) are combined for spectral-spatial classification of hyperspectral images. The spatial information is extracted from a best segmentation obtained by pruning the HSeg tree using a new supervised strategy. The best segmentation is updated at each iteration of the AL process, thus taking advantage of informative labeled samples provided by the user. The proposed strategy incorporates spatial information in two ways: 1) concatenating the extracted spatial features and the original spectral features into a stacked vector and 2) extending the training set using a self-learning-based semi-supervised learning (SSL) approach. Finally, the two strategies are combined within an AL framework. The proposed framework is validated with two benchmark hyperspectral datasets. Higher classification accuracies are obtained by the proposed framework with respect to five other state-of-the-art spectral-spatial classification approaches. Moreover, the effectiveness of the proposed pruning strategy is also demonstrated relative to the approaches based on a fixed segmentation.

  8. Organization of multinational undertakings in the nuclear field

    International Nuclear Information System (INIS)

    Yajima, Masayuki

    1982-01-01

    Various proposals have been put forward to establish multinational undertakings for enrichment, fuel fabrication, reprocessing, spent fuel storage and waste management. The purpose of this paper is to investigate the legal, institutional framework aspects of multinational undertakings in the field of nuclear fuel cycle. The selection of the appropriate bodies representing the interest of participating countries would largely depend on the object or role of multinational undertakings. Regarding the principle of formation, URENCO is a much informative model of formation, which distinguishes the equity participation at national level and multinational level. The allocation of service between equity participants and non-equity participants depends on the objective of establishing business. Some priority in service allocation should be given to equity participants, and the participants having non-proliferation objective may require service allocation to avoid proliferation risk. The degree of achieving non-proliferation goal is related to the scope of participation. The experience in the field of nuclear energy seems to suggest that the concept of two-tiered decisionmaking structure is generally accepted. Various legal instruments appropriate to constitute multinational fuel cycle arrangement were examined, referring to the precedents and experience. (Kako, I.)

  9. Classification of Pansharpened Urban Satellite Images

    DEFF Research Database (Denmark)

    Palsson, Frosti; Sveinsson, Johannes R.; Benediktsson, Jon Atli

    2012-01-01

    The classification of high resolution urban remote sensing imagery is addressed with the focus on classification of imagery that has been pansharpened by a number of different pansharpening methods. The pansharpening process introduces some spectral and spatial distortions in the resulting fused...... multispectral image, the amount of which highly varies depending on which pansharpening technique is used. In the majority of the pansharpening techniques that have been proposed, there is a compromise between the spatial enhancement and the spectral consistency. Here we study the effects of the spectral...... information from the panchromatic data. Random Forests (RF) and Support Vector Machines (SVM) will be used as classifiers. Experiments are done for three different datasets that have been obtained by two different imaging sensors, IKONOS and QuickBird. These sensors deliver multispectral images that have four...

  10. Investigating the Potential of Using the Spatial and Spectral Information of Multispectral LiDAR for Object Classification

    Directory of Open Access Journals (Sweden)

    Wei Gong

    2015-09-01

    Full Text Available The abilities of multispectral LiDAR (MSL as a new high-potential active instrument for remote sensing have not been fully revealed. This study demonstrates the potential of using the spectral and spatial features derived from a novel MSL to discriminate surface objects. Data acquired with the MSL include distance information and the intensities of four wavelengths at 556, 670, 700, and 780 nm channels. A support vector machine was used to classify diverse objects in the experimental scene into seven types: wall, ceramic pots, Cactaceae, carton, plastic foam block, and healthy and dead leaves of E. aureum. Different features were used during classification to compare the performance of different detection systems. The spectral backscattered reflectance of one wavelength and distance represented the features from an equivalent single-wavelength LiDAR system; reflectance of the four wavelengths represented the features from an equivalent multispectral image with four bands. Results showed that the overall accuracy of using MSL data was as high as 88.7%, this value was 9.8%–39.2% higher than those obtained using a single-wavelength LiDAR, and 4.2% higher than for multispectral image.

  11. Research on Remote Sensing Image Classification Based on Feature Level Fusion

    Science.gov (United States)

    Yuan, L.; Zhu, G.

    2018-04-01

    Remote sensing image classification, as an important direction of remote sensing image processing and application, has been widely studied. However, in the process of existing classification algorithms, there still exists the phenomenon of misclassification and missing points, which leads to the final classification accuracy is not high. In this paper, we selected Sentinel-1A and Landsat8 OLI images as data sources, and propose a classification method based on feature level fusion. Compare three kind of feature level fusion algorithms (i.e., Gram-Schmidt spectral sharpening, Principal Component Analysis transform and Brovey transform), and then select the best fused image for the classification experimental. In the classification process, we choose four kinds of image classification algorithms (i.e. Minimum distance, Mahalanobis distance, Support Vector Machine and ISODATA) to do contrast experiment. We use overall classification precision and Kappa coefficient as the classification accuracy evaluation criteria, and the four classification results of fused image are analysed. The experimental results show that the fusion effect of Gram-Schmidt spectral sharpening is better than other methods. In four kinds of classification algorithms, the fused image has the best applicability to Support Vector Machine classification, the overall classification precision is 94.01 % and the Kappa coefficients is 0.91. The fused image with Sentinel-1A and Landsat8 OLI is not only have more spatial information and spectral texture characteristics, but also enhances the distinguishing features of the images. The proposed method is beneficial to improve the accuracy and stability of remote sensing image classification.

  12. Classification of Hyperspectral Images Using Kernel Fully Constrained Least Squares

    Directory of Open Access Journals (Sweden)

    Jianjun Liu

    2017-11-01

    Full Text Available As a widely used classifier, sparse representation classification (SRC has shown its good performance for hyperspectral image classification. Recent works have highlighted that it is the collaborative representation mechanism under SRC that makes SRC a highly effective technique for classification purposes. If the dimensionality and the discrimination capacity of a test pixel is high, other norms (e.g., ℓ 2 -norm can be used to regularize the coding coefficients, except for the sparsity ℓ 1 -norm. In this paper, we show that in the kernel space the nonnegative constraint can also play the same role, and thus suggest the investigation of kernel fully constrained least squares (KFCLS for hyperspectral image classification. Furthermore, in order to improve the classification performance of KFCLS by incorporating spatial-spectral information, we investigate two kinds of spatial-spectral methods using two regularization strategies: (1 the coefficient-level regularization strategy, and (2 the class-level regularization strategy. Experimental results conducted on four real hyperspectral images demonstrate the effectiveness of the proposed KFCLS, and show which way to incorporate spatial-spectral information efficiently in the regularization framework.

  13. Classification in Astronomy: Past and Present

    Science.gov (United States)

    Feigelson, Eric

    2012-03-01

    Astronomers have always classified celestial objects. The ancient Greeks distinguished between asteros, the fixed stars, and planetos, the roving stars. The latter were associated with the Gods and, starting with Plato in his dialog Timaeus, provided the first mathematical models of celestial phenomena. Giovanni Hodierna classified nebulous objects, seen with a Galilean refractor telescope in the mid-seventeenth century into three classes: "Luminosae," "Nebulosae," and "Occultae." A century later, Charles Messier compiled a larger list of nebulae, star clusters and galaxies, but did not attempt a classification. Classification of comets was a significant enterprise in the 19th century: Alexander (1850) considered two groups based on orbit sizes, Lardner (1853) proposed three groups of orbits, and Barnard (1891) divided them into two classes based on morphology. Aside from the segmentation of the bright stars into constellations, most stellar classifications were based on colors and spectral properties. During the 1860s, the pioneering spectroscopist Angelo Secchi classified stars into five classes: white, yellow, orange, carbon stars, and emission line stars. After many debates, the stellar spectral sequence was refined by the group at Harvard into the familiar OBAFGKM spectral types, later found to be a sequence on surface temperature (Cannon 1926). The spectral classification is still being extended with recent additions of O2 hot stars (Walborn et al. 2002) and L and T brown dwarfs (Kirkpatrick 2005). Townley (1913) reviews 30 years of variable star classification, emerging with six classes with five subclasses. The modern classification of variable stars has about 80 (sub)classes, and is still under debate (Samus 2009). Shortly after his confirmation that some nebulae are external galaxies, Edwin Hubble (1926) proposed his famous bifurcated classification of galaxy morphologies with three classes: ellipticals, spirals, and irregulars. These classes are still

  14. APPLICATION OF FUSION WITH SAR AND OPTICAL IMAGES IN LAND USE CLASSIFICATION BASED ON SVM

    Directory of Open Access Journals (Sweden)

    C. Bao

    2012-07-01

    Full Text Available As the increment of remote sensing data with multi-space resolution, multi-spectral resolution and multi-source, data fusion technologies have been widely used in geological fields. Synthetic Aperture Radar (SAR and optical camera are two most common sensors presently. The multi-spectral optical images express spectral features of ground objects, while SAR images express backscatter information. Accuracy of the image classification could be effectively improved fusing the two kinds of images. In this paper, Terra SAR-X images and ALOS multi-spectral images were fused for land use classification. After preprocess such as geometric rectification, radiometric rectification noise suppression and so on, the two kind images were fused, and then SVM model identification method was used for land use classification. Two different fusion methods were used, one is joining SAR image into multi-spectral images as one band, and the other is direct fusing the two kind images. The former one can raise the resolution and reserve the texture information, and the latter can reserve spectral feature information and improve capability of identifying different features. The experiment results showed that accuracy of classification using fused images is better than only using multi-spectral images. Accuracy of classification about roads, habitation and water bodies was significantly improved. Compared to traditional classification method, the method of this paper for fused images with SVM classifier could achieve better results in identifying complicated land use classes, especially for small pieces ground features.

  15. A comparison of autonomous techniques for multispectral image analysis and classification

    Science.gov (United States)

    Valdiviezo-N., Juan C.; Urcid, Gonzalo; Toxqui-Quitl, Carina; Padilla-Vivanco, Alfonso

    2012-10-01

    Multispectral imaging has given place to important applications related to classification and identification of objects from a scene. Because of multispectral instruments can be used to estimate the reflectance of materials in the scene, these techniques constitute fundamental tools for materials analysis and quality control. During the last years, a variety of algorithms has been developed to work with multispectral data, whose main purpose has been to perform the correct classification of the objects in the scene. The present study introduces a brief review of some classical as well as a novel technique that have been used for such purposes. The use of principal component analysis and K-means clustering techniques as important classification algorithms is here discussed. Moreover, a recent method based on the min-W and max-M lattice auto-associative memories, that was proposed for endmember determination in hyperspectral imagery, is introduced as a classification method. Besides a discussion of their mathematical foundation, we emphasize their main characteristics and the results achieved for two exemplar images conformed by objects similar in appearance, but spectrally different. The classification results state that the first components computed from principal component analysis can be used to highlight areas with different spectral characteristics. In addition, the use of lattice auto-associative memories provides good results for materials classification even in the cases where some spectral similarities appears in their spectral responses.

  16. Spectral analysis and classification of igneous and metamorphic rocks of Hamedan region for remote sensing studies; using laboratory reflectance spectra (350-2500 nm)

    International Nuclear Information System (INIS)

    Rangzan, K.; Saki, A.; Hassanshahi, H.; Mojaradi, B.

    2012-01-01

    Reflectance spectrometry techniques with the integration of remote sensing data help us in identifying and mapping the phenomena on the earth. Using these techniques to discriminate the petrologic units independently and without knowing the spectral behavior of rocks along the electromagnetic wavelengths can not be so much useful. For the purposes of this study, 65 samples of igneous and metamorphic rocks from Hamedan region were collected and their spectra were measured using Fieldspec3 device in laboratory. The spectra were analyzed on the basis of absorption, position and shape. Petrographic analyses were used to interpret the absorption patterns as well. Then the spectra were classified according to spectral patterns. This measurement was done on both freshly cut and exposed surfaces of the samples and except a few samples, the two sets of spectra did not differ significantly. Finally, to evaluate the possibility of recognition of these targets, the responses of two hyper spectral and multispectral sensors were simulated from spectra representative of the spectral classes, showing that significant identification and classification of well exposed rocks are potentially possible using remote instruments providing high quality spectra. Also Aster simulation showed that a preliminary gross discrimination of rocks was however possible.

  17. Parallel computation for blood cell classification in medical hyperspectral imagery

    International Nuclear Information System (INIS)

    Li, Wei; Wu, Lucheng; Qiu, Xianbo; Ran, Qiong; Xie, Xiaoming

    2016-01-01

    With the advantage of fine spectral resolution, hyperspectral imagery provides great potential for cell classification. This paper provides a promising classification system including the following three stages: (1) band selection for a subset of spectral bands with distinctive and informative features, (2) spectral-spatial feature extraction, such as local binary patterns (LBP), and (3) followed by an effective classifier. Moreover, these three steps are further implemented on graphics processing units (GPU) respectively, which makes the system real-time and more practical. The GPU parallel implementation is compared with the serial implementation on central processing units (CPU). Experimental results based on real medical hyperspectral data demonstrate that the proposed system is able to offer high accuracy and fast speed, which are appealing for cell classification in medical hyperspectral imagery. (paper)

  18. Classification of objects on hyperspectral images — further developments

    DEFF Research Database (Denmark)

    Kucheryavskiy, Sergey V.; Williams, Paul

    Classification of objects (such as tablets, cereals, fruits, etc.) is one of the very important applications of hyperspectral imaging and image analysis. Quite often, a hyperspectral image is represented and analyzed just as a bunch of spectra without taking into account spatial information about...... the pixels, which makes classification objects inefficient. Recently, several methods, which combine spectral and spatial information, has been also developed and this approach becomes more and more wide-spread. The methods use local rank, topology, spectral features calculated for separate objects and other...... spatial characteristics. In this work we would like to show several improvements to the classification method, which utilizes spectral features calculated for individual objects [1]. The features are based (in general) on descriptors of spatial patterns of individual object’s pixels in a common principal...

  19. Accuracy assessment between different image classification ...

    African Journals Online (AJOL)

    What image classification does is to assign pixel to a particular land cover and land use type that has the most similar spectral signature. However, there are possibilities that different methods or algorithms of image classification of the same data set could produce appreciable variant results in the sizes, shapes and areas of ...

  20. Morphological Interpretation of Reflectance Spectrum (MIRS using libraries looking towards soil classification

    Directory of Open Access Journals (Sweden)

    José Alexandre Melo Demattê

    2014-12-01

    Full Text Available The search for tools to perform soil surveying faster and cheaper has led to the development of technological innovations such as remote sensing (RS and the so-called spectral libraries in recent years. However, there are no studies which collate all the RS background to demonstrate how to use this technology for soil classification. The present study aims to describe a simple method of how to classify soils by the morphology of spectra associated with a quantitative view (400-2,500 nm. For this, we constructed three spectral libraries: (i one for quantitative model performance; (ii a second to function as the spectral patterns; and (iii a third to serve as a validation stage. All samples had their chemical and granulometric attributes determined by laboratory analysis and prediction models were created based on soil spectra. The system is based on seven steps summarized as follows: i interpretation of the spectral curve intensity; ii observation of the general shape of curves; iii evaluation of absorption features; iv comparison of spectral curves between the same profile horizons; v quantification of soil attributes by spectral library models; vi comparison of a pre-existent spectral library with unknown profile spectra; vii most probable soil classification. A soil cannot be classified from one spectral curve alone. The behavior between the horizons of a profile, however, was correlated with its classification. In fact, the validation showed 85 % accuracy between the Morphological Interpretation of Reflectance Spectrum (MIRS method and the traditional classification, showing the importance and potential of a combination of descriptive and quantitative evaluations.

  1. Comparison between Possibilistic c-Means (PCM and Artificial Neural Network (ANN Classification Algorithms in Land use/ Land cover Classification

    Directory of Open Access Journals (Sweden)

    Ganchimeg Ganbold

    2017-03-01

    Full Text Available There are several statistical classification algorithms available for landuse/land cover classification. However, each has a certain bias orcompromise. Some methods like the parallel piped approach in supervisedclassification, cannot classify continuous regions within a feature. Onthe other hand, while unsupervised classification method takes maximumadvantage of spectral variability in an image, the maximally separableclusters in spectral space may not do much for our perception of importantclasses in a given study area. In this research, the output of an ANNalgorithm was compared with the Possibilistic c-Means an improvementof the fuzzy c-Means on both moderate resolutions Landsat8 and a highresolution Formosat 2 images. The Formosat 2 image comes with an8m spectral resolution on the multispectral data. This multispectral imagedata was resampled to 10m in order to maintain a uniform ratio of1:3 against Landsat 8 image. Six classes were chosen for analysis including:Dense forest, eucalyptus, water, grassland, wheat and riverine sand. Using a standard false color composite (FCC, the six features reflecteddifferently in the infrared region with wheat producing the brightestpixel values. Signature collection per class was therefore easily obtainedfor all classifications. The output of both ANN and FCM, were analyzedseparately for accuracy and an error matrix generated to assess the qualityand accuracy of the classification algorithms. When you compare theresults of the two methods on a per-class-basis, ANN had a crisperoutput compared to PCM which yielded clusters with pixels especiallyon the moderate resolution Landsat 8 imagery.

  2. Topographic gradient based site characterization in India complemented by strong ground-motion spectral attributes

    KAUST Repository

    Nath, Sankar Kumar; Thingbaijam, Kiran Kumar; Adhikari, M. D.; Nayak, Avinash; Devaraj, N.; Ghosh, Soumalya K.; Mahajan, Arun K.

    2013-01-01

    We appraise topographic-gradient approach for site classification that employs correlations between 30. m column averaged shear-wave velocity and topographic gradients. Assessments based on site classifications reported from cities across India indicate that the approach is reasonably viable at regional level. Additionally, we experiment three techniques for site classification based on strong ground-motion recordings, namely Horizontal-to-Vertical Spectral Ratio (HVSR), Response Spectra Shape (RSS), and Horizontal-to-Vertical Response Spectral Ratio (HVRSR) at the strong motion stations located across the Himalayas and northeast India. Statistical tests on the results indicate that these three techniques broadly differentiate soil and rock sites while RSS and HVRSR yield better signatures. The results also support the implemented site classification in the light of strong ground-motion spectral attributes observed in different parts of the globe. © 2013 Elsevier Ltd.

  3. Topographic gradient based site characterization in India complemented by strong ground-motion spectral attributes

    KAUST Repository

    Nath, Sankar Kumar

    2013-12-01

    We appraise topographic-gradient approach for site classification that employs correlations between 30. m column averaged shear-wave velocity and topographic gradients. Assessments based on site classifications reported from cities across India indicate that the approach is reasonably viable at regional level. Additionally, we experiment three techniques for site classification based on strong ground-motion recordings, namely Horizontal-to-Vertical Spectral Ratio (HVSR), Response Spectra Shape (RSS), and Horizontal-to-Vertical Response Spectral Ratio (HVRSR) at the strong motion stations located across the Himalayas and northeast India. Statistical tests on the results indicate that these three techniques broadly differentiate soil and rock sites while RSS and HVRSR yield better signatures. The results also support the implemented site classification in the light of strong ground-motion spectral attributes observed in different parts of the globe. © 2013 Elsevier Ltd.

  4. Astrophysical Information from Objective Prism Digitized Images: Classification with an Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Bratsolis Emmanuel

    2005-01-01

    Full Text Available Stellar spectral classification is not only a tool for labeling individual stars but is also useful in studies of stellar population synthesis. Extracting the physical quantities from the digitized spectral plates involves three main stages: detection, extraction, and classification of spectra. Low-dispersion objective prism images have been used and automated methods have been developed. The detection and extraction problems have been presented in previous works. In this paper, we present a classification method based on an artificial neural network (ANN. We make a brief presentation of the entire automated system and we compare the new classification method with the previously used method of maximum correlation coefficient (MCC. Digitized photographic material has been used here. The method can also be used on CCD spectral images.

  5. 31 CFR 248.4 - Undertaking of indemnity.

    Science.gov (United States)

    2010-07-01

    ... 31 Money and Finance: Treasury 2 2010-07-01 2010-07-01 false Undertaking of indemnity. 248.4 Section 248.4 Money and Finance: Treasury Regulations Relating to Money and Finance (Continued) FISCAL... in the circumstances set forth below, a corporate surety authorized by the Secretary of the Treasury...

  6. Analysis of Landsat-4 Thematic Mapper data for classification of forest stands in Baldwin County, Alabama

    Science.gov (United States)

    Hill, C. L.

    1984-01-01

    A computer-implemented classification has been derived from Landsat-4 Thematic Mapper data acquired over Baldwin County, Alabama on January 15, 1983. One set of spectral signatures was developed from the data by utilizing a 3x3 pixel sliding window approach. An analysis of the classification produced from this technique identified forested areas. Additional information regarding only the forested areas. Additional information regarding only the forested areas was extracted by employing a pixel-by-pixel signature development program which derived spectral statistics only for pixels within the forested land covers. The spectral statistics from both approaches were integrated and the data classified. This classification was evaluated by comparing the spectral classes produced from the data against corresponding ground verification polygons. This iterative data analysis technique resulted in an overall classification accuracy of 88.4 percent correct for slash pine, young pine, loblolly pine, natural pine, and mixed hardwood-pine. An accuracy assessment matrix has been produced for the classification.

  7. Graph-Based Semi-Supervised Hyperspectral Image Classification Using Spatial Information

    Science.gov (United States)

    Jamshidpour, N.; Homayouni, S.; Safari, A.

    2017-09-01

    Hyperspectral image classification has been one of the most popular research areas in the remote sensing community in the past decades. However, there are still some problems that need specific attentions. For example, the lack of enough labeled samples and the high dimensionality problem are two most important issues which degrade the performance of supervised classification dramatically. The main idea of semi-supervised learning is to overcome these issues by the contribution of unlabeled samples, which are available in an enormous amount. In this paper, we propose a graph-based semi-supervised classification method, which uses both spectral and spatial information for hyperspectral image classification. More specifically, two graphs were designed and constructed in order to exploit the relationship among pixels in spectral and spatial spaces respectively. Then, the Laplacians of both graphs were merged to form a weighted joint graph. The experiments were carried out on two different benchmark hyperspectral data sets. The proposed method performed significantly better than the well-known supervised classification methods, such as SVM. The assessments consisted of both accuracy and homogeneity analyses of the produced classification maps. The proposed spectral-spatial SSL method considerably increased the classification accuracy when the labeled training data set is too scarce.When there were only five labeled samples for each class, the performance improved 5.92% and 10.76% compared to spatial graph-based SSL, for AVIRIS Indian Pine and Pavia University data sets respectively.

  8. GRAPH-BASED SEMI-SUPERVISED HYPERSPECTRAL IMAGE CLASSIFICATION USING SPATIAL INFORMATION

    Directory of Open Access Journals (Sweden)

    N. Jamshidpour

    2017-09-01

    Full Text Available Hyperspectral image classification has been one of the most popular research areas in the remote sensing community in the past decades. However, there are still some problems that need specific attentions. For example, the lack of enough labeled samples and the high dimensionality problem are two most important issues which degrade the performance of supervised classification dramatically. The main idea of semi-supervised learning is to overcome these issues by the contribution of unlabeled samples, which are available in an enormous amount. In this paper, we propose a graph-based semi-supervised classification method, which uses both spectral and spatial information for hyperspectral image classification. More specifically, two graphs were designed and constructed in order to exploit the relationship among pixels in spectral and spatial spaces respectively. Then, the Laplacians of both graphs were merged to form a weighted joint graph. The experiments were carried out on two different benchmark hyperspectral data sets. The proposed method performed significantly better than the well-known supervised classification methods, such as SVM. The assessments consisted of both accuracy and homogeneity analyses of the produced classification maps. The proposed spectral-spatial SSL method considerably increased the classification accuracy when the labeled training data set is too scarce.When there were only five labeled samples for each class, the performance improved 5.92% and 10.76% compared to spatial graph-based SSL, for AVIRIS Indian Pine and Pavia University data sets respectively.

  9. Multi-spectral Image Analysis for Astaxanthin Coating Classification

    DEFF Research Database (Denmark)

    Ljungqvist, Martin Georg; Ersbøll, Bjarne Kjær; Nielsen, Michael Engelbrecht

    2011-01-01

    Industrial quality inspection using image analysis on astaxanthin coating in aquaculture feed pellets is of great importance for automatic production control. In this study multi-spectral image analysis of pellets was performed using LDA, QDA, SNV and PCA on pixel level and mean value of pixels...

  10. A Study of Spectral Integration and Normalization in NMR-based Metabonomic Analyses

    Energy Technology Data Exchange (ETDEWEB)

    Webb-Robertson, Bobbie-Jo M.; Lowry, David F.; Jarman, Kristin H.; Harbo, Sam J.; Meng, Quanxin; Fuciarelli, Alfred F.; Pounds, Joel G.; Lee, Monica T.

    2005-09-15

    Metabonomics involves the quantitation of the dynamic multivariate metabolic response of an organism to a pathological event or genetic modification (Nicholson, Lindon and Holmes, 1999). The analysis of these data involves the use of appropriate multivariate statistical methods. Exploratory Data Analysis (EDA) linear projection methods, primarily Principal Component Analysis (PCA), have been documented as a valuable pattern recognition technique for 1H NMR spectral data (Brindle et al., 2002, Potts et al., 2001, Robertson et al., 2000, Robosky et al., 2002). Prior to PCA the raw data is typically processed through four steps; (1) baseline correction, (2) endogenous peak removal, (3) integration over spectral regions to reduce the number of variables, and (4) normalization. The effect of the size of spectral integration regions and normalization has not been well studied. We assess the variability structure and classification accuracy on two distinctly different datasets via PCA and a leave-one-out cross-validation approach under two normalization approaches and an array of spectral integration regions. This study indicates that independent of the normalization method the classification accuracy achieved from metabonomic studies is not highly sensitive to the size of the spectral integration region. Additionally, both datasets scaled to mean zero and unity variance (auto-scaled) has higher variability within classification accuracy over spectral integration window widths than data scaled to the total intensity of the spectrum.

  11. 20 CFR 703.304 - Filing of Agreement and Undertaking; deposit of security.

    Science.gov (United States)

    2010-04-01

    ... the amount fixed by the Office, or deposit negotiable securities under §§ 703.306 and 703.307 in that... 20 Employees' Benefits 3 2010-04-01 2010-04-01 false Filing of Agreement and Undertaking; deposit... REGULATIONS Authorization of Self-Insurers § 703.304 Filing of Agreement and Undertaking; deposit of security...

  12. Classification of breast microcalcifications using spectral mammography

    Science.gov (United States)

    Ghammraoui, B.; Glick, S. J.

    2017-03-01

    Purpose: To investigate the potential of spectral mammography to distinguish between type I calcifications, consisting of calcium oxalate dihydrate or weddellite compounds that are more often associated with benign lesions, and type II calcifications containing hydroxyapatite which are predominantly associated with malignant tumors. Methods: Using a ray tracing algorithm, we simulated the total number of x-ray photons recorded by the detector at one pixel from a single pencil-beam projection through a breast of 50/50 (adipose/glandular) tissues with inserted microcalcifications of different types and sizes. Material decomposition using two energy bins was then applied to characterize the simulated calcifications into hydroxyapatite and weddellite using maximumlikelihood estimation, taking into account the polychromatic source, the detector response function and the energy dependent attenuation. Results: Simulation tests were carried out for different doses and calcification sizes for multiple realizations. The results were summarized using receiver operating characteristic (ROC) analysis with the area under the curve (AUC) taken as an overall indicator of discrimination performance and showing high AUC values up to 0.99. Conclusion: Our simulation results obtained for a uniform breast imaging phantom indicate that spectral mammography using two energy bins has the potential to be used as a non-invasive method for discrimination between type I and type II microcalcifications to improve early breast cancer diagnosis and reduce the number of unnecessary breast biopsies.

  13. The Effect of Epidermal Structures on Leaf Spectral Signatures of Ice Plants (Aizoaceae

    Directory of Open Access Journals (Sweden)

    René Hans-Jürgen Heim

    2015-12-01

    Full Text Available Epidermal structures (ES of leaves are known to affect the functional properties and spectral responses. Spectral studies focused mostly on the effect of hairs or wax layers only. We studied a wider range of different ES and their impact on spectral properties. Additionally, we identified spectral regions that allow distinguishing different ES. We used a field spectrometer to measure ex situ leaf spectral responses from 350 nm–2500 nm. A spectral library for 25 species of the succulent family Aizoaceae was assembled. Five functional types were defined based on ES: flat epidermal cell surface, convex to papillary epidermal cell surface, bladder cells, hairs and wax cover. We tested the separability of ES using partial least squares discriminant analysis (PLS-DA based on the spectral data. Subsequently, variable importance (VIP was calculated to identify spectral regions relevant for discriminating our functional types (classes. Classification performance was high, with a kappa value of 0.9 indicating well-separable spectral classes. VIP calculations identified six spectral regions of increased importance for the classification. We confirmed and extended previous findings regarding the visible-near-infrared spectral region. Our experiments also confirmed that epidermal leaf traits can be classified due to clearly distinguishable spectral signatures across species and genera within the Aizoaceae.

  14. Jet Joint Undertaking. Vol. 2

    International Nuclear Information System (INIS)

    1989-06-01

    The scientific, technical, experimental and theoretical investigations related to JET tokamak are presented. The JET Joint Undertaking, Volume 2, includes papers presented at: the 15th European Conference on controlled fusion and plasma heating, the 15th Symposium on fusion technology, the 12th IAEA Conference on plasma physics and controlled nuclear fusion research, the 8th Topical Meeting on technology of fusion. Moreover, the following topics, concerning JET, are discussed: experience with wall materials, plasma performance, high power ion cyclotron resonance heating, plasma boundary, results and prospects for fusion, preparation for D-T operation, active gas handling system and remote handling equipment

  15. [Automatic Classification of Dry Cough and Wet Cough Based on Improved Reverse Mel Frequency Cepstrum Coefficients].

    Science.gov (United States)

    Zhu, Chunmei; Liu, Baojun; Li, Ping; Mo, Hongqiang; Zheng, Zeguang

    2016-04-01

    Automatic classification of different types of cough plays an important role in clinical.In the previous research of cough classification or cough recognition,traditional Mel frequency cepstrum coefficients(MFCC)which extracts feature mainly from low frequency band is usually used as feature expression.In this paper,by analyzing the distributions of spectral energy of dry/wet cough,it is found that spectral difference of two types of cough exits mainly in middle frequency band and high frequency band.To better reflect the spectral difference of dry cough and wet cough,an improved method of extracting reverse MFCC is proposed.In this method,reverse Mel filter-bank in which filters are allocated in reverse Mel scale is adopted and is improved by placing filters only in the frequency band with high spectral energy.As a result,features are mainly extracted from the frequency band where two types of cough show both high spectral energy and distinguished difference.Detailed process of accessing improved reverse MFCC was introduced and hidden Markov models trained by 60 dry cough and 60 wet cough were used as cough classification model.Classification experiment results for 120 dry cough and 85 wet cough showed that,compared to traditional MFCC,better classification performance was achieved by the proposed method and the total classification accuracy was raised from 89.76%to 93.66%.

  16. Students Collaborating to Undertake Tracking Efforts for Sturgeon(SCUTES)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Students Collaborating to Undertake Tracking Efforts for Sturgeon (SCUTES) is a collaboration between NOAA Fisheries, sturgeon researchers, and teachers/educators in...

  17. 12 CFR 980.2 - Limitation on Bank authority to undertake new business activities.

    Science.gov (United States)

    2010-01-01

    ... business activities. 980.2 Section 980.2 Banks and Banking FEDERAL HOUSING FINANCE BOARD NEW FEDERAL HOME LOAN BANK ACTIVITIES NEW BUSINESS ACTIVITIES § 980.2 Limitation on Bank authority to undertake new business activities. No Bank shall undertake any new business activity except in accordance with the...

  18. METHODS OF ANALYSIS AND CLASSIFICATION OF THE COMPONENTS OF GRAIN MIXTURES BASED ON MEASURING THE REFLECTION AND TRANSMISSION SPECTRA

    Directory of Open Access Journals (Sweden)

    Artem O. Donskikh*

    2017-10-01

    Full Text Available The paper considers methods of classification of grain mixture components based on spectral analysis in visible and near-infrared wavelength ranges using various measurement approaches - reflection, transmission and combined spectrum methods. It also describes the experimental measuring units used and suggests the prototype of a multispectral grain mixture analyzer. The results of the spectral measurement were processed using neural network based classification algorithms. The probabilities of incorrect recognition for various numbers of spectral parts and combinations of spectral methods were estimated. The paper demonstrates that combined usage of two spectral analysis methods leads to higher classification accuracy and allows for reducing the number of the analyzed spectral parts. A detailed description of the proposed measurement device for high-performance real-time multispectral analysis of the components of grain mixtures is given.

  19. Texture classification of vegetation cover in high altitude wetlands zone

    International Nuclear Information System (INIS)

    Wentao, Zou; Bingfang, Wu; Hongbo, Ju; Hua, Liu

    2014-01-01

    The aim of this study was to investigate the utility of datasets composed of texture measures and other features for the classification of vegetation cover, specifically wetlands. QUEST decision tree classifier was applied to a SPOT-5 image sub-scene covering the typical wetlands area in Three River Sources region in Qinghai province, China. The dataset used for the classification comprised of: (1) spectral data and the components of principal component analysis; (2) texture measures derived from pixel basis; (3) DEM and other ancillary data covering the research area. Image textures is an important characteristic of remote sensing images; it can represent spatial variations with spectral brightness in digital numbers. When the spectral information is not enough to separate the different land covers, the texture information can be used to increase the classification accuracy. The texture measures used in this study were calculated from GLCM (Gray level Co-occurrence Matrix); eight frequently used measures were chosen to conduct the classification procedure. The results showed that variance, mean and entropy calculated by GLCM with a 9*9 size window were effective in distinguishing different vegetation types in wetlands zone. The overall accuracy of this method was 84.19% and the Kappa coefficient was 0.8261. The result indicated that the introduction of texture measures can improve the overall accuracy by 12.05% and the overall kappa coefficient by 0.1407 compared with the result using spectral and ancillary data

  20. Machine learning in APOGEE. Unsupervised spectral classification with K-means

    Science.gov (United States)

    Garcia-Dias, Rafael; Allende Prieto, Carlos; Sánchez Almeida, Jorge; Ordovás-Pascual, Ignacio

    2018-05-01

    Context. The volume of data generated by astronomical surveys is growing rapidly. Traditional analysis techniques in spectroscopy either demand intensive human interaction or are computationally expensive. In this scenario, machine learning, and unsupervised clustering algorithms in particular, offer interesting alternatives. The Apache Point Observatory Galactic Evolution Experiment (APOGEE) offers a vast data set of near-infrared stellar spectra, which is perfect for testing such alternatives. Aims: Our research applies an unsupervised classification scheme based on K-means to the massive APOGEE data set. We explore whether the data are amenable to classification into discrete classes. Methods: We apply the K-means algorithm to 153 847 high resolution spectra (R ≈ 22 500). We discuss the main virtues and weaknesses of the algorithm, as well as our choice of parameters. Results: We show that a classification based on normalised spectra captures the variations in stellar atmospheric parameters, chemical abundances, and rotational velocity, among other factors. The algorithm is able to separate the bulge and halo populations, and distinguish dwarfs, sub-giants, RC, and RGB stars. However, a discrete classification in flux space does not result in a neat organisation in the parameters' space. Furthermore, the lack of obvious groups in flux space causes the results to be fairly sensitive to the initialisation, and disrupts the efficiency of commonly-used methods to select the optimal number of clusters. Our classification is publicly available, including extensive online material associated with the APOGEE Data Release 12 (DR12). Conclusions: Our description of the APOGEE database can help greatly with the identification of specific types of targets for various applications. We find a lack of obvious groups in flux space, and identify limitations of the K-means algorithm in dealing with this kind of data. Full Tables B.1-B.4 are only available at the CDS via

  1. Non-financial reporting, CSR frameworks and groups of undertakings

    DEFF Research Database (Denmark)

    Szabó, Dániel Gergely; Sørensen, Karsten Engsig

    2017-01-01

    The recently adopted Directive on non-financial reporting (Directive 2014/95/EU) and several CSR frameworks are based on the assumption that groups of undertakings adopt, report and implement one group policy. This is a very important but also rather unique approach to groups. This article first...... shows how the Directive as well as a few CSR frameworks intend to be implemented in groups and next it discusses potential barriers to do so. Even though company law does not always facilitate the adoption, communication and implementation of a group CSR policy, it may not in practice be a problem to do...... so. However, it is shown that doing so may have unforeseen consequences for the parent undertaking. To avoid them, it is recommended to make adjustments to the implementation of the group policy....

  2. JET joint undertaking. Annual report 1978

    International Nuclear Information System (INIS)

    1979-02-01

    This document is intended for information only and should not be used as a technical reference. After an introductive part on the controlled nuclear fusion research and an historical survey of the JET project, are presented: the JET joint undertaking (members of council and committee...) with its administration (finance, personnel, external relations), and the scientific and technical department with its divisions for systems (experimental, magnet, plasma, assembly, power supplies, control and data acquisition, and site and building). In appendix is described the Euratom fusion research programme

  3. Hyperspectral Biofilm Classification Analysis for Carrying Capacity of Migratory Birds in the South Bay Salt Ponds

    Science.gov (United States)

    Hsu, Wei-Chen; Kuss, Amber Jean; Ketron, Tyler; Nguyen, Andrew; Remar, Alex Covello; Newcomer, Michelle; Fleming, Erich; Debout, Leslie; Debout, Brad; Detweiler, Angela; hide

    2011-01-01

    Tidal marshes are highly productive ecosystems that support migratory birds as roosting and over-wintering habitats on the Pacific Flyway. Microphytobenthos, or more commonly 'biofilms' contribute significantly to the primary productivity of wetland ecosystems, and provide a substantial food source for macroinvertebrates and avian communities. In this study, biofilms were characterized based on taxonomic classification, density differences, and spectral signatures. These techniques were then applied to remotely sensed images to map biofilm densities and distributions in the South Bay Salt Ponds and predict the carrying capacity of these newly restored ponds for migratory birds. The GER-1500 spectroradiometer was used to obtain in situ spectral signatures for each density-class of biofilm. The spectral variation and taxonomic classification between high, medium, and low density biofilm cover types was mapped using in-situ spectral measurements and classification of EO-1 Hyperion and Landsat TM 5 images. Biofilm samples were also collected in the field to perform laboratory analyses including chlorophyll-a, taxonomic classification, and energy content. Comparison of the spectral signatures between the three density groups shows distinct variations useful for classification. Also, analysis of chlorophyll-a concentrations show statistically significant differences between each density group, using the Tukey-Kramer test at an alpha level of 0.05. The potential carrying capacity in South Bay Salt Ponds is estimated to be 250,000 birds.

  4. A method to incorporate uncertainty in the classification of remote sensing images

    OpenAIRE

    Gonçalves, Luísa M. S.; Fonte, Cidália C.; Júlio, Eduardo N. B. S.; Caetano, Mario

    2009-01-01

    The aim of this paper is to investigate if the incorporation of the uncertainty associated with the classification of surface elements into the classification of landscape units (LUs) increases the results accuracy. To this end, a hybrid classification method is developed, including uncertainty information in the classification of very high spatial resolution multi-spectral satellite images, to obtain a map of LUs. The developed classification methodology includes the following...

  5. Classification of high resolution imagery based on fusion of multiscale texture features

    International Nuclear Information System (INIS)

    Liu, Jinxiu; Liu, Huiping; Lv, Ying; Xue, Xiaojuan

    2014-01-01

    In high resolution data classification process, combining texture features with spectral bands can effectively improve the classification accuracy. However, the window size which is difficult to choose is regarded as an important factor influencing overall classification accuracy in textural classification and current approaches to image texture analysis only depend on a single moving window which ignores different scale features of various land cover types. In this paper, we propose a new method based on the fusion of multiscale texture features to overcome these problems. The main steps in new method include the classification of fixed window size spectral/textural images from 3×3 to 15×15 and comparison of all the posterior possibility values for every pixel, as a result the biggest probability value is given to the pixel and the pixel belongs to a certain land cover type automatically. The proposed approach is tested on University of Pavia ROSIS data. The results indicate that the new method improve the classification accuracy compared to results of methods based on fixed window size textural classification

  6. VizieR Online Data Catalog: Spectral properties of 441 radio pulsars (Jankowski+, 2018)

    Science.gov (United States)

    Jankowski, F.; van Straten, W.; Keane, E. F.; Bailes, M.; Barr, E. D.; Johnston, S.; Kerr, M.

    2018-03-01

    We present spectral parameters for 441 radio pulsars. These were obtained from observations centred at 728, 1382 and 3100MHz using the 10-50cm and the 20cm multibeam receiver at the Parkes radio telescope. In particular, we list the pulsar names (J2000), the calibrated, band-integrated flux densities at 728, 1382 and 3100MHz, the spectral classifications, the frequency ranges the spectral classifications were performed over, the spectral indices for pulsars with simple power-law spectra and the robust modulation indices at all three centre frequencies for pulsars of which we have at least six measurement epochs. The flux density uncertainties include scintillation and a systematic contribution, in addition to the statistical uncertainty. Upper limits are reported at the 3σ level and all other uncertainties at the 1σ level. (1 data file).

  7. Acoustic and spectral characteristics of young children's fricative productions: A developmental perspective

    Science.gov (United States)

    Nissen, Shawn L.; Fox, Robert Allen

    2005-10-01

    Scientists have made great strides toward understanding the mechanisms of speech production and perception. However, the complex relationships between the acoustic structures of speech and the resulting psychological percepts have yet to be fully and adequately explained, especially in speech produced by younger children. Thus, this study examined the acoustic structure of voiceless fricatives (/f, θ, s, /sh/) produced by adults and typically developing children from 3 to 6 years of age in terms of multiple acoustic parameters (durations, normalized amplitude, spectral slope, and spectral moments). It was found that the acoustic parameters of spectral slope and variance (commonly excluded from previous studies of child speech) were important acoustic parameters in the differentiation and classification of the voiceless fricatives, with spectral variance being the only measure to separate all four places of articulation. It was further shown that the sibilant contrast between /s/ and /sh/ was less distinguished in children than adults, characterized by a dramatic change in several spectral parameters at approximately five years of age. Discriminant analysis revealed evidence that classification models based on adult data were sensitive to these spectral differences in the five-year-old age group.

  8. Spectral identification of plant communities for mapping of semi-natural grasslands

    DEFF Research Database (Denmark)

    Jacobsen, Anne; Nielsen, Allan Aasbjerg; Ejrnæs, Rasmus

    2000-01-01

    identification of plant communities was based on a hierarchical approach relating the test sites to i) management (Ma) and ii) flora (Fl) using spectral consistency and separability as the main criteria. Evaluation of spectral consistency was based on unsupervised clustering of test sites of Ma classes 1 to 7...... as a measure of plant community heterogeneity within management classes. The spectral analysis as well as the maximum likelihood classification indicated that the source of spectral variation within management classes might be related to vegetation composition....

  9. Hyperspectral imaging of polymer banknotes for building and analysis of spectral library

    Science.gov (United States)

    Lim, Hoong-Ta; Murukeshan, Vadakke Matham

    2017-11-01

    The use of counterfeit banknotes increases crime rates and cripples the economy. New countermeasures are required to stop counterfeiters who use advancing technologies with criminal intent. Many countries started adopting polymer banknotes to replace paper notes, as polymer notes are more durable and have better quality. The research on authenticating such banknotes is of much interest to the forensic investigators. Hyperspectral imaging can be employed to build a spectral library of polymer notes, which can then be used for classification to authenticate these notes. This is however not widely reported and has become a research interest in forensic identification. This paper focuses on the use of hyperspectral imaging on polymer notes to build spectral libraries, using a pushbroom hyperspectral imager which has been previously reported. As an initial study, a spectral library will be built from three arbitrarily chosen regions of interest of five circulated genuine polymer notes. Principal component analysis is used for dimension reduction and to convert the information in the spectral library to principal components. A 99% confidence ellipse is formed around the cluster of principal component scores of each class and then used as classification criteria. The potential of the adopted methodology is demonstrated by the classification of the imaged regions as training samples.

  10. The exclusion of 'public undertakings' from the re-use of public sector information regime

    NARCIS (Netherlands)

    Ricolfi, M.; Drexl, J.; van Eechoud, M.; Salmeron, M.; Sappa, C.; Tziavos, P.; Valero, J.; Pavoni, F.; Patrito, P.

    2011-01-01

    Should public undertakings be covered by the PSI Directive? The definitions of public sector bodies and bodies governed by public law, to which the PSI Directive applies, are currently taken from the public procurement Directives and public undertakings are not covered by these definitions. Should

  11. [Environmental licensing of major undertakings: possible connection between health and environment].

    Science.gov (United States)

    Silveira, Missifany; Araújo Neto, Mário Diniz de

    2014-09-01

    The prospect of multidisciplinary assessment that considers the environmental impacts on the health of the population during the implementation of potentially polluting projects is incipient in Brazil. Considering the scenario of major undertakings in the country, broadening the outlook on the health and environment relationship based on social and economic development processes striving for environmentally sustainable projects is a key strategy. This article examines the debate on the relationship between the current development model, the risks, the environment and health and discusses the importance of the participation of the health sector in the environmental licensing procedures, which is the instrument of the Environmental Impact Assessment (EIA). Seeking to create more environmentally and socially sustainable territories, the health sector has been looking for opportunities to participate in the licensing processes of major undertakings from the EIA standpoint. Results of research conducted by the Ministry of Health have demonstrated the form of participation in these processes, highlighting the strengths and weaknesses that favor or hinder the increase of preventive actions in public health in the implementation of major undertakings in Brazil.

  12. A classification model of Hyperion image base on SAM combined decision tree

    Science.gov (United States)

    Wang, Zhenghai; Hu, Guangdao; Zhou, YongZhang; Liu, Xin

    2009-10-01

    Monitoring the Earth using imaging spectrometers has necessitated more accurate analyses and new applications to remote sensing. A very high dimensional input space requires an exponentially large amount of data to adequately and reliably represent the classes in that space. On the other hand, with increase in the input dimensionality the hypothesis space grows exponentially, which makes the classification performance highly unreliable. Traditional classification algorithms Classification of hyperspectral images is challenging. New algorithms have to be developed for hyperspectral data classification. The Spectral Angle Mapper (SAM) is a physically-based spectral classification that uses an ndimensional angle to match pixels to reference spectra. The algorithm determines the spectral similarity between two spectra by calculating the angle between the spectra, treating them as vectors in a space with dimensionality equal to the number of bands. The key and difficulty is that we should artificial defining the threshold of SAM. The classification precision depends on the rationality of the threshold of SAM. In order to resolve this problem, this paper proposes a new automatic classification model of remote sensing image using SAM combined with decision tree. It can automatic choose the appropriate threshold of SAM and improve the classify precision of SAM base on the analyze of field spectrum. The test area located in Heqing Yunnan was imaged by EO_1 Hyperion imaging spectrometer using 224 bands in visual and near infrared. The area included limestone areas, rock fields, soil and forests. The area was classified into four different vegetation and soil types. The results show that this method choose the appropriate threshold of SAM and eliminates the disturbance and influence of unwanted objects effectively, so as to improve the classification precision. Compared with the likelihood classification by field survey data, the classification precision of this model

  13. Land-Use and Land-Cover Mapping Using a Gradable Classification Method

    Directory of Open Access Journals (Sweden)

    Keigo Kitada

    2012-05-01

    Full Text Available Conventional spectral-based classification methods have significant limitations in the digital classification of urban land-use and land-cover classes from high-resolution remotely sensed data because of the lack of consideration given to the spatial properties of images. To recognize the complex distribution of urban features in high-resolution image data, texture information consisting of a group of pixels should be considered. Lacunarity is an index used to characterize different texture appearances. It is often reported that the land-use and land-cover in urban areas can be effectively classified using the lacunarity index with high-resolution images. However, the applicability of the maximum-likelihood approach for hybrid analysis has not been reported. A more effective approach that employs the original spectral data and lacunarity index can be expected to improve the accuracy of the classification. A new classification procedure referred to as “gradable classification method” is proposed in this study. This method improves the classification accuracy in incremental steps. The proposed classification approach integrates several classification maps created from original images and lacunarity maps, which consist of lacnarity values, to create a new classification map. The results of this study confirm the suitability of the gradable classification approach, which produced a higher overall accuracy (68% and kappa coefficient (0.64 than those (65% and 0.60, respectively obtained with the maximum-likelihood approach.

  14. Selection/extraction of spectral regions for autofluorescence spectra measured in the oral cavity

    NARCIS (Netherlands)

    Skurichina, M; Paclik, P; Duin, RPW; de Veld, D; Sterenborg, HJCM; Witjes, MJH; Roodenburg, JLN; Fred, A; Caelli, T; Duin, RPW; Campilho, A; DeRidder, D

    2004-01-01

    Recently a number of successful algorithms to select/extract discriminative spectral regions was introduced. These methods may be more beneficial than the standard feature selection/extraction methods for spectral classification. In this paper, on the example of autofluorescence spectra measured in

  15. Fluorescence-based classification of Caribbean coral reef organisms and substrates

    Science.gov (United States)

    Zawada, David G.; Mazel, Charles H.

    2014-01-01

    A diverse group of coral reef organisms, representing several phyla, possess fluorescent pigments. We investigated the potential of using the characteristic fluorescence emission spectra of these pigments to enable unsupervised, optical classification of coral reef habitats. We compiled a library of characteristic fluorescence spectra through in situ and laboratory measurements from a variety of specimens throughout the Caribbean. Because fluorescent pigments are not species-specific, the spectral library is organized in terms of 15 functional groups. We investigated the spectral separability of the functional groups in terms of the number of wavebands required to distinguish between them, using the similarity measures Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), SID-SAM mixed measure, and Mahalanobis distance. This set of measures represents geometric, stochastic, joint geometric-stochastic, and statistical approaches to classifying spectra. Our hyperspectral fluorescence data were used to generate sets of 4-, 6-, and 8-waveband spectra, including random variations in relative signal amplitude, spectral peak shifts, and water-column attenuation. Each set consisted of 2 different band definitions: ‘optimally-picked’ and ‘evenly-spaced.’ The optimally-picked wavebands were chosen to coincide with as many peaks as possible in the functional group spectra. Reference libraries were formed from half of the spectra in each set and used for training purposes. Average classification accuracies ranged from 76.3% for SAM with 4 evenly-spaced wavebands to 93.8% for Mahalanobis distance with 8 evenly-spaced wavebands. The Mahalanobis distance consistently outperformed the other measures. In a second test, empirically-measured spectra were classified using the same reference libraries and the Mahalanobis distance for just the 8 evenly-spaced waveband case. Average classification accuracies were 84% and 87%, corresponding to the extremes in modeled

  16. Multiple endmember spectral-angle-mapper (SAM) analysis improves discrimination of Savanna tree species

    CSIR Research Space (South Africa)

    Cho, Moses A

    2009-08-01

    Full Text Available of this paper was to evaluate the classification performance of a multiple-endmember spectral angle mapper (SAM) classification approach in discriminating seven common African savanna tree species and to compare the results with the traditional SAM classifier...

  17. Orthogonal feature selection method. [For preprocessing of man spectral data

    Energy Technology Data Exchange (ETDEWEB)

    Kowalski, B R [Univ. of Washington, Seattle; Bender, C F

    1976-01-01

    A new method of preprocessing spectral data for extraction of molecular structural information is desired. This SELECT method generates orthogonal features that are important for classification purposes and that also retain their identity to the original measurements. A brief introduction to chemical pattern recognition is presented. A brief description of the method and an application to mass spectral data analysis follow. (BLM)

  18. Crown-Level Tree Species Classification Using Integrated Airborne Hyperspectral and LIDAR Remote Sensing Data

    Science.gov (United States)

    Wang, Z.; Wu, J.; Wang, Y.; Kong, X.; Bao, H.; Ni, Y.; Ma, L.; Jin, J.

    2018-05-01

    Mapping tree species is essential for sustainable planning as well as to improve our understanding of the role of different trees as different ecological service. However, crown-level tree species automatic classification is a challenging task due to the spectral similarity among diversified tree species, fine-scale spatial variation, shadow, and underlying objects within a crown. Advanced remote sensing data such as airborne Light Detection and Ranging (LiDAR) and hyperspectral imagery offer a great potential opportunity to derive crown spectral, structure and canopy physiological information at the individual crown scale, which can be useful for mapping tree species. In this paper, an innovative approach was developed for tree species classification at the crown level. The method utilized LiDAR data for individual tree crown delineation and morphological structure extraction, and Compact Airborne Spectrographic Imager (CASI) hyperspectral imagery for pure crown-scale spectral extraction. Specifically, four steps were include: 1) A weighted mean filtering method was developed to improve the accuracy of the smoothed Canopy Height Model (CHM) derived from LiDAR data; 2) The marker-controlled watershed segmentation algorithm was, therefore, also employed to delineate the tree-level canopy from the CHM image in this study, and then individual tree height and tree crown were calculated according to the delineated crown; 3) Spectral features within 3 × 3 neighborhood regions centered on the treetops detected by the treetop detection algorithm were derived from the spectrally normalized CASI imagery; 4) The shape characteristics related to their crown diameters and heights were established, and different crown-level tree species were classified using the combination of spectral and shape characteristics. Analysis of results suggests that the developed classification strategy in this paper (OA = 85.12 %, Kc = 0.90) performed better than LiDAR-metrics method (OA = 79

  19. The Ultracool Typing Kit - An Open-Source, Qualitative Spectral Typing GUI for L Dwarfs

    Science.gov (United States)

    Schwab, Ellianna; Cruz, Kelle; Núñez, Alejandro; Burgasser, Adam J.; Rice, Emily; Reid, Neill; Faherty, Jacqueline K.; BDNYC

    2018-01-01

    The Ultracool Typing Kit (UTK) is an open-source graphical user interface for classifying the NIR spectral types of L dwarfs, including field and low-gravity dwarfs spanning L0-L9. The user is able to input an NIR spectrum and qualitatively compare the input spectrum to a full suite of spectral templates, including low-gravity beta and gamma templates. The user can choose to view the input spectrum as both a band-by-band comparison with the templates and a full bandwidth comparison with NIR spectral standards. Once an optimal qualitative comparison is selected, the user can save their spectral type selection both graphically and to a database. Using UTK to classify 78 previously typed L dwarfs, we show that a band-by-band classification method more accurately agrees with optical spectral typing systems than previous L dwarf NIR classification schemes. UTK is written in python, released on Zenodo with a BSD-3 clause license and publicly available on the BDNYC Github page.

  20. AUTOMATIC APPROACH TO VHR SATELLITE IMAGE CLASSIFICATION

    Directory of Open Access Journals (Sweden)

    P. Kupidura

    2016-06-01

    Full Text Available In this paper, we present a proposition of a fully automatic classification of VHR satellite images. Unlike the most widespread approaches: supervised classification, which requires prior defining of class signatures, or unsupervised classification, which must be followed by an interpretation of its results, the proposed method requires no human intervention except for the setting of the initial parameters. The presented approach bases on both spectral and textural analysis of the image and consists of 3 steps. The first step, the analysis of spectral data, relies on NDVI values. Its purpose is to distinguish between basic classes, such as water, vegetation and non-vegetation, which all differ significantly spectrally, thus they can be easily extracted basing on spectral analysis. The second step relies on granulometric maps. These are the product of local granulometric analysis of an image and present information on the texture of each pixel neighbourhood, depending on the texture grain. The purpose of texture analysis is to distinguish between different classes, spectrally similar, but yet of different texture, e.g. bare soil from a built-up area, or low vegetation from a wooded area. Due to the use of granulometric analysis, based on mathematical morphology opening and closing, the results are resistant to the border effect (qualifying borders of objects in an image as spaces of high texture, which affect other methods of texture analysis like GLCM statistics or fractal analysis. Therefore, the effectiveness of the analysis is relatively high. Several indices based on values of different granulometric maps have been developed to simplify the extraction of classes of different texture. The third and final step of the process relies on a vegetation index, based on near infrared and blue bands. Its purpose is to correct partially misclassified pixels. All the indices used in the classification model developed relate to reflectance values, so the

  1. SNSEDextend: SuperNova Spectral Energy Distributions extrapolation toolkit

    Science.gov (United States)

    Pierel, Justin D. R.; Rodney, Steven A.; Avelino, Arturo; Bianco, Federica; Foley, Ryan J.; Friedman, Andrew; Hicken, Malcolm; Hounsell, Rebekah; Jha, Saurabh W.; Kessler, Richard; Kirshner, Robert; Mandel, Kaisey; Narayan, Gautham; Filippenko, Alexei V.; Scolnic, Daniel; Strolger, Louis-Gregory

    2018-05-01

    SNSEDextend extrapolates core-collapse and Type Ia Spectral Energy Distributions (SEDs) into the UV and IR for use in simulations and photometric classifications. The user provides a library of existing SED templates (such as those in the authors' SN SED Repository) along with new photometric constraints in the UV and/or NIR wavelength ranges. The software then extends the existing template SEDs so their colors match the input data at all phases. SNSEDextend can also extend the SALT2 spectral time-series model for Type Ia SN for a "first-order" extrapolation of the SALT2 model components, suitable for use in survey simulations and photometric classification tools; as the code does not do a rigorous re-training of the SALT2 model, the results should not be relied on for precision applications such as light curve fitting for cosmology.

  2. Multi Angle Imaging With Spectral Remote Sensing for Scene Classification

    National Research Council Canada - National Science Library

    Prasert, Sunyaruk

    2005-01-01

    .... This study analyses the BRDF (Bidirectional Reflectance Distribution Function) impact and effectiveness of texture analysis on terrain classification within Fresno County area in state of California...

  3. CONSIDERATIONS ON THE RULES ON COMPETITION GOVERNING UNDERTAKINGS IN THE EUROPEAN UNION

    Directory of Open Access Journals (Sweden)

    Vlad – Teodor Florea

    2014-11-01

    Full Text Available This study concerns the general rules on competition between undertakings in the EU. The author paid attention primarly to matters on the prohibition of agreements that aim to distort or impair competition on the internal market. Moreover, he examined in detail the matter concerning the regulation and interdiction of the abuse of a dominant position. The work also reviews doctrinal opinions, as well as the jurisprudential solutions in the area. The author’s concern to summarize and develop the conditions for the implementation of each of the two legal mechanisms is worth noting: the prohibition of agreements between undertakings and the abuse of a dominant position. The essential considerations taken into account by the Court of Justice of the European Union in settling a case whose subject consisted of assessing the manner in which an undertaking reflected on competition on the internal market were selected at the end of the work.

  4. Site classification for National Strong Motion Observation Network System (NSMONS) stations in China using an empirical H/V spectral ratio method

    Science.gov (United States)

    Ji, Kun; Ren, Yefei; Wen, Ruizhi

    2017-10-01

    Reliable site classification of the stations of the China National Strong Motion Observation Network System (NSMONS) has not yet been assigned because of lacking borehole data. This study used an empirical horizontal-to-vertical (H/V) spectral ratio (hereafter, HVSR) site classification method to overcome this problem. First, according to their borehole data, stations selected from KiK-net in Japan were individually assigned a site class (CL-I, CL-II, or CL-III), which is defined in the Chinese seismic code. Then, the mean HVSR curve for each site class was computed using strong motion recordings captured during the period 1996-2012. These curves were compared with those proposed by Zhao et al. (2006a) for four types of site classes (SC-I, SC-II, SC-III, and SC-IV) defined in the Japanese seismic code (JRA, 1980). It was found that an approximate range of the predominant period Tg could be identified by the predominant peak of the HVSR curve for the CL-I and SC-I sites, CL-II and SC-II sites, and CL-III and SC-III + SC-IV sites. Second, an empirical site classification method was proposed based on comprehensive consideration of peak period, amplitude, and shape of the HVSR curve. The selected stations from KiK-net were classified using the proposed method. The results showed that the success rates of the proposed method in identifying CL-I, CL-II, and CL-III sites were 63%, 64%, and 58% respectively. Finally, the HVSRs of 178 NSMONS stations were computed based on recordings from 2007 to 2015 and the sites classified using the proposed method. The mean HVSR curves were re-calculated for three site classes and compared with those from KiK-net data. It was found that both the peak period and the amplitude were similar for the mean HVSR curves derived from NSMONS classification results and KiK-net borehole data, implying the effectiveness of the proposed method in identifying different site classes. The classification results have good agreement with site classes

  5. Active-passive data fusion algorithms for seafloor imaging and classification from CZMIL data

    Science.gov (United States)

    Park, Joong Yong; Ramnath, Vinod; Feygels, Viktor; Kim, Minsu; Mathur, Abhinav; Aitken, Jennifer; Tuell, Grady

    2010-04-01

    CZMIL will simultaneously acquire lidar and passive spectral data. These data will be fused to produce enhanced seafloor reflectance images from each sensor, and combined at a higher level to achieve seafloor classification. In the DPS software, the lidar data will first be processed to solve for depth, attenuation, and reflectance. The depth measurements will then be used to constrain the spectral optimization of the passive spectral data, and the resulting water column estimates will be used recursively to improve the estimates of seafloor reflectance from the lidar. Finally, the resulting seafloor reflectance cube will be combined with texture metrics estimated from the seafloor topography to produce classifications of the seafloor.

  6. Spectral signature selection for mapping unvegetated soils

    Science.gov (United States)

    May, G. A.; Petersen, G. W.

    1975-01-01

    Airborne multispectral scanner data covering the wavelength interval from 0.40-2.60 microns were collected at an altitude of 1000 m above the terrain in southeastern Pennsylvania. Uniform training areas were selected within three sites from this flightline. Soil samples were collected from each site and a procedure developed to allow assignment of scan line and element number from the multispectral scanner data to each sampling location. These soil samples were analyzed on a spectrophotometer and laboratory spectral signatures were derived. After correcting for solar radiation and atmospheric attenuation, the laboratory signatures were compared to the spectral signatures derived from these same soils using multispectral scanner data. Both signatures were used in supervised and unsupervised classification routines. Computer-generated maps using the laboratory and multispectral scanner derived signatures resulted in maps that were similar to maps resulting from field surveys. Approximately 90% agreement was obtained between classification maps produced using multispectral scanner derived signatures and laboratory derived signatures.

  7. Collaborative classification of hyperspectral and visible images with convolutional neural network

    Science.gov (United States)

    Zhang, Mengmeng; Li, Wei; Du, Qian

    2017-10-01

    Recent advances in remote sensing technology have made multisensor data available for the same area, and it is well-known that remote sensing data processing and analysis often benefit from multisource data fusion. Specifically, low spatial resolution of hyperspectral imagery (HSI) degrades the quality of the subsequent classification task while using visible (VIS) images with high spatial resolution enables high-fidelity spatial analysis. A collaborative classification framework is proposed to fuse HSI and VIS images for finer classification. First, the convolutional neural network model is employed to extract deep spectral features for HSI classification. Second, effective binarized statistical image features are learned as contextual basis vectors for the high-resolution VIS image, followed by a classifier. The proposed approach employs diversified data in a decision fusion, leading to an integration of the rich spectral information, spatial information, and statistical representation information. In particular, the proposed approach eliminates the potential problems of the curse of dimensionality and excessive computation time. The experiments evaluated on two standard data sets demonstrate better classification performance offered by this framework.

  8. [Modeling and Simulation of Spectral Polarimetric BRDF].

    Science.gov (United States)

    Ling, Jin-jiang; Li, Gang; Zhang, Ren-bin; Tang, Qian; Ye, Qiu

    2016-01-01

    Under the conditions of the polarized light, The reflective surface of the object is affected by many factors, refractive index, surface roughness, and so the angle of incidence. For the rough surface in the different wavelengths of light exhibit different reflection characteristics of polarization, a spectral polarimetric BRDF based on Kirchhof theory is proposee. The spectral model of complex refraction index is combined with refraction index and extinction coefficient spectral model which were got by using the known complex refraction index at different value. Then get the spectral model of surface roughness derived from the classical surface roughness measuring method combined with the Fresnel reflection function. Take the spectral model of refraction index and roughness into the BRDF model, then the spectral polarimetirc BRDF model is proposed. Compare the simulation results of the refractive index varies with wavelength, roughness is constant, the refraction index and roughness both vary with wavelength and origin model with other papers, it shows that, the spectral polarimetric BRDF model can show the polarization characteristics of the surface accurately, and can provide a reliable basis for the application of polarization remote sensing, and other aspects of the classification of substances.

  9. Aspiring to Spectral Ignorance in Earth Observation

    Science.gov (United States)

    Oliver, S. A.

    2016-12-01

    Enabling robust, defensible and integrated decision making in the Era of Big Earth Data requires the fusion of data from multiple and diverse sensor platforms and networks. While the application of standardised global grid systems provides a common spatial analytics framework that facilitates the computationally efficient and statistically valid integration and analysis of these various data sources across multiple scales, there remains the challenge of sensor equivalency; particularly when combining data from different earth observation satellite sensors (e.g. combining Landsat and Sentinel-2 observations). To realise the vision of a sensor ignorant analytics platform for earth observation we require automation of spectral matching across the available sensors. Ultimately, the aim is to remove the requirement for the user to possess any sensor knowledge in order to undertake analysis. This paper introduces the concept of spectral equivalence and proposes a methodology through which equivalent bands may be sourced from a set of potential target sensors through application of equivalence metrics and thresholds. A number of parameters can be used to determine whether a pair of spectra are equivalent for the purposes of analysis. A baseline set of thresholds for these parameters and how to apply them systematically to enable relation of spectral bands amongst numerous different sensors is proposed. The base unit for comparison in this work is the relative spectral response. From this input, determination of a what may constitute equivalence can be related by a user, based on their own conceptualisation of equivalence.

  10. Comparing Features for Classification of MEG Responses to Motor Imagery.

    Directory of Open Access Journals (Sweden)

    Hanna-Leena Halme

    Full Text Available Motor imagery (MI with real-time neurofeedback could be a viable approach, e.g., in rehabilitation of cerebral stroke. Magnetoencephalography (MEG noninvasively measures electric brain activity at high temporal resolution and is well-suited for recording oscillatory brain signals. MI is known to modulate 10- and 20-Hz oscillations in the somatomotor system. In order to provide accurate feedback to the subject, the most relevant MI-related features should be extracted from MEG data. In this study, we evaluated several MEG signal features for discriminating between left- and right-hand MI and between MI and rest.MEG was measured from nine healthy participants imagining either left- or right-hand finger tapping according to visual cues. Data preprocessing, feature extraction and classification were performed offline. The evaluated MI-related features were power spectral density (PSD, Morlet wavelets, short-time Fourier transform (STFT, common spatial patterns (CSP, filter-bank common spatial patterns (FBCSP, spatio-spectral decomposition (SSD, and combined SSD+CSP, CSP+PSD, CSP+Morlet, and CSP+STFT. We also compared four classifiers applied to single trials using 5-fold cross-validation for evaluating the classification accuracy and its possible dependence on the classification algorithm. In addition, we estimated the inter-session left-vs-right accuracy for each subject.The SSD+CSP combination yielded the best accuracy in both left-vs-right (mean 73.7% and MI-vs-rest (mean 81.3% classification. CSP+Morlet yielded the best mean accuracy in inter-session left-vs-right classification (mean 69.1%. There were large inter-subject differences in classification accuracy, and the level of the 20-Hz suppression correlated significantly with the subjective MI-vs-rest accuracy. Selection of the classification algorithm had only a minor effect on the results.We obtained good accuracy in sensor-level decoding of MI from single-trial MEG data. Feature extraction

  11. APPLICATION OF SENSOR FUSION TO IMPROVE UAV IMAGE CLASSIFICATION

    Directory of Open Access Journals (Sweden)

    S. Jabari

    2017-08-01

    Full Text Available Image classification is one of the most important tasks of remote sensing projects including the ones that are based on using UAV images. Improving the quality of UAV images directly affects the classification results and can save a huge amount of time and effort in this area. In this study, we show that sensor fusion can improve image quality which results in increasing the accuracy of image classification. Here, we tested two sensor fusion configurations by using a Panchromatic (Pan camera along with either a colour camera or a four-band multi-spectral (MS camera. We use the Pan camera to benefit from its higher sensitivity and the colour or MS camera to benefit from its spectral properties. The resulting images are then compared to the ones acquired by a high resolution single Bayer-pattern colour camera (here referred to as HRC. We assessed the quality of the output images by performing image classification tests. The outputs prove that the proposed sensor fusion configurations can achieve higher accuracies compared to the images of the single Bayer-pattern colour camera. Therefore, incorporating a Pan camera on-board in the UAV missions and performing image fusion can help achieving higher quality images and accordingly higher accuracy classification results.

  12. Application of Sensor Fusion to Improve Uav Image Classification

    Science.gov (United States)

    Jabari, S.; Fathollahi, F.; Zhang, Y.

    2017-08-01

    Image classification is one of the most important tasks of remote sensing projects including the ones that are based on using UAV images. Improving the quality of UAV images directly affects the classification results and can save a huge amount of time and effort in this area. In this study, we show that sensor fusion can improve image quality which results in increasing the accuracy of image classification. Here, we tested two sensor fusion configurations by using a Panchromatic (Pan) camera along with either a colour camera or a four-band multi-spectral (MS) camera. We use the Pan camera to benefit from its higher sensitivity and the colour or MS camera to benefit from its spectral properties. The resulting images are then compared to the ones acquired by a high resolution single Bayer-pattern colour camera (here referred to as HRC). We assessed the quality of the output images by performing image classification tests. The outputs prove that the proposed sensor fusion configurations can achieve higher accuracies compared to the images of the single Bayer-pattern colour camera. Therefore, incorporating a Pan camera on-board in the UAV missions and performing image fusion can help achieving higher quality images and accordingly higher accuracy classification results.

  13. Time Series of Images to Improve Tree Species Classification

    Science.gov (United States)

    Miyoshi, G. T.; Imai, N. N.; de Moraes, M. V. A.; Tommaselli, A. M. G.; Näsi, R.

    2017-10-01

    Tree species classification provides valuable information to forest monitoring and management. The high floristic variation of the tree species appears as a challenging issue in the tree species classification because the vegetation characteristics changes according to the season. To help to monitor this complex environment, the imaging spectroscopy has been largely applied since the development of miniaturized sensors attached to Unmanned Aerial Vehicles (UAV). Considering the seasonal changes in forests and the higher spectral and spatial resolution acquired with sensors attached to UAV, we present the use of time series of images to classify four tree species. The study area is an Atlantic Forest area located in the western part of São Paulo State. Images were acquired in August 2015 and August 2016, generating three data sets of images: only with the image spectra of 2015; only with the image spectra of 2016; with the layer stacking of images from 2015 and 2016. Four tree species were classified using Spectral angle mapper (SAM), Spectral information divergence (SID) and Random Forest (RF). The results showed that SAM and SID caused an overfitting of the data whereas RF showed better results and the use of the layer stacking improved the classification achieving a kappa coefficient of 18.26 %.

  14. Silence–breathing–snore classification from snore-related sounds

    International Nuclear Information System (INIS)

    Karunajeewa, Asela S; Abeyratne, Udantha R; Hukins, Craig

    2008-01-01

    Obstructive sleep apnea (OSA) is a highly prevalent disease in which upper airways are collapsed during sleep, leading to serious consequences. Snoring is the earliest symptom of OSA, but its potential in clinical diagnosis is not fully recognized yet. The first task in the automatic analysis of snore-related sounds (SRS) is to segment the SRS data as accurately as possible into three main classes: snoring (voiced non-silence), breathing (unvoiced non-silence) and silence. SRS data are generally contaminated with background noise. In this paper, we present classification performance of a new segmentation algorithm based on pattern recognition. We considered four features derived from SRS to classify samples of SRS into three classes. The features—number of zero crossings, energy of the signal, normalized autocorrelation coefficient at 1 ms delay and the first predictor coefficient of linear predictive coding (LPC) analysis—in combination were able to achieve a classification accuracy of 90.74% in classifying a set of test data. We also investigated the performance of the algorithm when three commonly used noise reduction (NR) techniques in speech processing—amplitude spectral subtraction (ASS), power spectral subtraction (PSS) and short time spectral amplitude (STSA) estimation—are used for noise reduction. We found that noise reduction together with a proper choice of features could improve the classification accuracy to 96.78%, making the automated analysis a possibility

  15. Arc-welding quality assurance by means of embedded fiber sensor and spectral processing combining feature selection and neural networks

    Science.gov (United States)

    Mirapeix, J.; García-Allende, P. B.; Cobo, A.; Conde, O.; López-Higuera, J. M.

    2007-07-01

    A new spectral processing technique designed for its application in the on-line detection and classification of arc-welding defects is presented in this paper. A non-invasive fiber sensor embedded within a TIG torch collects the plasma radiation originated during the welding process. The spectral information is then processed by means of two consecutive stages. A compression algorithm is first applied to the data allowing real-time analysis. The selected spectral bands are then used to feed a classification algorithm, which will be demonstrated to provide an efficient weld defect detection and classification. The results obtained with the proposed technique are compared to a similar processing scheme presented in a previous paper, giving rise to an improvement in the performance of the monitoring system.

  16. An Object-Based Classification of Mangroves Using a Hybrid Decision Tree—Support Vector Machine Approach

    Directory of Open Access Journals (Sweden)

    Benjamin W. Heumann

    2011-11-01

    Full Text Available Mangroves provide valuable ecosystem goods and services such as carbon sequestration, habitat for terrestrial and marine fauna, and coastal hazard mitigation. The use of satellite remote sensing to map mangroves has become widespread as it can provide accurate, efficient, and repeatable assessments. Traditional remote sensing approaches have failed to accurately map fringe mangroves and true mangrove species due to relatively coarse spatial resolution and/or spectral confusion with landward vegetation. This study demonstrates the use of the new Worldview-2 sensor, Object-based image analysis (OBIA, and support vector machine (SVM classification to overcome both of these limitations. An exploratory spectral separability showed that individual mangrove species could not be spectrally separated, but a distinction between true and associate mangrove species could be made. An OBIA classification was used that combined a decision-tree classification with the machine-learning SVM classification. Results showed an overall accuracy greater than 94% (kappa = 0.863 for classifying true mangroves species and other dense coastal vegetation at the object level. There remain serious challenges to accurately mapping fringe mangroves using remote sensing data due to spectral similarity of mangrove and associate species, lack of clear zonation between species, and mixed pixel effects, especially when vegetation is sparse or degraded.

  17. Towards automatic lithological classification from remote sensing data using support vector machines

    Science.gov (United States)

    Yu, Le; Porwal, Alok; Holden, Eun-Jung; Dentith, Michael

    2010-05-01

    Remote sensing data can be effectively used as a mean to build geological knowledge for poorly mapped terrains. Spectral remote sensing data from space- and air-borne sensors have been widely used to geological mapping, especially in areas of high outcrop density in arid regions. However, spectral remote sensing information by itself cannot be efficiently used for a comprehensive lithological classification of an area due to (1) diagnostic spectral response of a rock within an image pixel is conditioned by several factors including the atmospheric effects, spectral and spatial resolution of the image, sub-pixel level heterogeneity in chemical and mineralogical composition of the rock, presence of soil and vegetation cover; (2) only surface information and is therefore highly sensitive to the noise due to weathering, soil cover, and vegetation. Consequently, for efficient lithological classification, spectral remote sensing data needs to be supplemented with other remote sensing datasets that provide geomorphological and subsurface geological information, such as digital topographic model (DEM) and aeromagnetic data. Each of the datasets contain significant information about geology that, in conjunction, can potentially be used for automated lithological classification using supervised machine learning algorithms. In this study, support vector machine (SVM), which is a kernel-based supervised learning method, was applied to automated lithological classification of a study area in northwestern India using remote sensing data, namely, ASTER, DEM and aeromagnetic data. Several digital image processing techniques were used to produce derivative datasets that contained enhanced information relevant to lithological discrimination. A series of SVMs (trained using k-folder cross-validation with grid search) were tested using various combinations of input datasets selected from among 50 datasets including the original 14 ASTER bands and 36 derivative datasets (including 14

  18. CROWN-LEVEL TREE SPECIES CLASSIFICATION USING INTEGRATED AIRBORNE HYPERSPECTRAL AND LIDAR REMOTE SENSING DATA

    Directory of Open Access Journals (Sweden)

    Z. Wang

    2018-05-01

    Full Text Available Mapping tree species is essential for sustainable planning as well as to improve our understanding of the role of different trees as different ecological service. However, crown-level tree species automatic classification is a challenging task due to the spectral similarity among diversified tree species, fine-scale spatial variation, shadow, and underlying objects within a crown. Advanced remote sensing data such as airborne Light Detection and Ranging (LiDAR and hyperspectral imagery offer a great potential opportunity to derive crown spectral, structure and canopy physiological information at the individual crown scale, which can be useful for mapping tree species. In this paper, an innovative approach was developed for tree species classification at the crown level. The method utilized LiDAR data for individual tree crown delineation and morphological structure extraction, and Compact Airborne Spectrographic Imager (CASI hyperspectral imagery for pure crown-scale spectral extraction. Specifically, four steps were include: 1 A weighted mean filtering method was developed to improve the accuracy of the smoothed Canopy Height Model (CHM derived from LiDAR data; 2 The marker-controlled watershed segmentation algorithm was, therefore, also employed to delineate the tree-level canopy from the CHM image in this study, and then individual tree height and tree crown were calculated according to the delineated crown; 3 Spectral features within 3 × 3 neighborhood regions centered on the treetops detected by the treetop detection algorithm were derived from the spectrally normalized CASI imagery; 4 The shape characteristics related to their crown diameters and heights were established, and different crown-level tree species were classified using the combination of spectral and shape characteristics. Analysis of results suggests that the developed classification strategy in this paper (OA = 85.12 %, Kc = 0.90 performed better than Li

  19. Object based image analysis for the classification of the growth stages of Avocado crop, in Michoacán State, Mexico

    Science.gov (United States)

    Gao, Yan; Marpu, Prashanth; Morales Manila, Luis M.

    2014-11-01

    This paper assesses the suitability of 8-band Worldview-2 (WV2) satellite data and object-based random forest algorithm for the classification of avocado growth stages in Mexico. We tested both pixel-based with minimum distance (MD) and maximum likelihood (MLC) and object-based with Random Forest (RF) algorithm for this task. Training samples and verification data were selected by visual interpreting the WV2 images for seven thematic classes: fully grown, middle stage, and early stage of avocado crops, bare land, two types of natural forests, and water body. To examine the contribution of the four new spectral bands of WV2 sensor, all the tested classifications were carried out with and without the four new spectral bands. Classification accuracy assessment results show that object-based classification with RF algorithm obtained higher overall higher accuracy (93.06%) than pixel-based MD (69.37%) and MLC (64.03%) method. For both pixel-based and object-based methods, the classifications with the four new spectral bands (overall accuracy obtained higher accuracy than those without: overall accuracy of object-based RF classification with vs without: 93.06% vs 83.59%, pixel-based MD: 69.37% vs 67.2%, pixel-based MLC: 64.03% vs 36.05%, suggesting that the four new spectral bands in WV2 sensor contributed to the increase of the classification accuracy.

  20. Classification of peacock feather reflectance using principal component analysis similarity factors from multispectral imaging data.

    Science.gov (United States)

    Medina, José M; Díaz, José A; Vukusic, Pete

    2015-04-20

    Iridescent structural colors in biology exhibit sophisticated spatially-varying reflectance properties that depend on both the illumination and viewing angles. The classification of such spectral and spatial information in iridescent structurally colored surfaces is important to elucidate the functional role of irregularity and to improve understanding of color pattern formation at different length scales. In this study, we propose a non-invasive method for the spectral classification of spatial reflectance patterns at the micron scale based on the multispectral imaging technique and the principal component analysis similarity factor (PCASF). We demonstrate the effectiveness of this approach and its component methods by detailing its use in the study of the angle-dependent reflectance properties of Pavo cristatus (the common peacock) feathers, a species of peafowl very well known to exhibit bright and saturated iridescent colors. We show that multispectral reflectance imaging and PCASF approaches can be used as effective tools for spectral recognition of iridescent patterns in the visible spectrum and provide meaningful information for spectral classification of the irregularity of the microstructure in iridescent plumage.

  1. Object-Based Classification as an Alternative Approach to the Traditional Pixel-Based Classification to Identify Potential Habitat of the Grasshopper Sparrow

    Science.gov (United States)

    Jobin, Benoît; Labrecque, Sandra; Grenier, Marcelle; Falardeau, Gilles

    2008-01-01

    The traditional method of identifying wildlife habitat distribution over large regions consists of pixel-based classification of satellite images into a suite of habitat classes used to select suitable habitat patches. Object-based classification is a new method that can achieve the same objective based on the segmentation of spectral bands of the image creating homogeneous polygons with regard to spatial or spectral characteristics. The segmentation algorithm does not solely rely on the single pixel value, but also on shape, texture, and pixel spatial continuity. The object-based classification is a knowledge base process where an interpretation key is developed using ground control points and objects are assigned to specific classes according to threshold values of determined spectral and/or spatial attributes. We developed a model using the eCognition software to identify suitable habitats for the Grasshopper Sparrow, a rare and declining species found in southwestern Québec. The model was developed in a region with known breeding sites and applied on other images covering adjacent regions where potential breeding habitats may be present. We were successful in locating potential habitats in areas where dairy farming prevailed but failed in an adjacent region covered by a distinct Landsat scene and dominated by annual crops. We discuss the added value of this method, such as the possibility to use the contextual information associated to objects and the ability to eliminate unsuitable areas in the segmentation and land cover classification processes, as well as technical and logistical constraints. A series of recommendations on the use of this method and on conservation issues of Grasshopper Sparrow habitat is also provided.

  2. Utility of multispectral imaging for nuclear classification of routine clinical histopathology imagery

    Directory of Open Access Journals (Sweden)

    Harvey Neal R

    2007-07-01

    Full Text Available Abstract Background We present an analysis of the utility of multispectral versus standard RGB imagery for routine H&E stained histopathology images, in particular for pixel-level classification of nuclei. Our multispectral imagery has 29 spectral bands, spaced 10 nm within the visual range of 420–700 nm. It has been hypothesized that the additional spectral bands contain further information useful for classification as compared to the 3 standard bands of RGB imagery. We present analyses of our data designed to test this hypothesis. Results For classification using all available image bands, we find the best performance (equal tradeoff between detection rate and false alarm rate is obtained from either the multispectral or our "ccd" RGB imagery, with an overall increase in performance of 0.79% compared to the next best performing image type. For classification using single image bands, the single best multispectral band (in the red portion of the spectrum gave a performance increase of 0.57%, compared to performance of the single best RGB band (red. Additionally, red bands had the highest coefficients/preference in our classifiers. Principal components analysis of the multispectral imagery indicates only two significant image bands, which is not surprising given the presence of two stains. Conclusion Our results indicate that multispectral imagery for routine H&E stained histopathology provides minimal additional spectral information for a pixel-level nuclear classification task than would standard RGB imagery.

  3. Classification Accuracy Increase Using Multisensor Data Fusion

    Science.gov (United States)

    Makarau, A.; Palubinskas, G.; Reinartz, P.

    2011-09-01

    The practical use of very high resolution visible and near-infrared (VNIR) data is still growing (IKONOS, Quickbird, GeoEye-1, etc.) but for classification purposes the number of bands is limited in comparison to full spectral imaging. These limitations may lead to the confusion of materials such as different roofs, pavements, roads, etc. and therefore may provide wrong interpretation and use of classification products. Employment of hyperspectral data is another solution, but their low spatial resolution (comparing to multispectral data) restrict their usage for many applications. Another improvement can be achieved by fusion approaches of multisensory data since this may increase the quality of scene classification. Integration of Synthetic Aperture Radar (SAR) and optical data is widely performed for automatic classification, interpretation, and change detection. In this paper we present an approach for very high resolution SAR and multispectral data fusion for automatic classification in urban areas. Single polarization TerraSAR-X (SpotLight mode) and multispectral data are integrated using the INFOFUSE framework, consisting of feature extraction (information fission), unsupervised clustering (data representation on a finite domain and dimensionality reduction), and data aggregation (Bayesian or neural network). This framework allows a relevant way of multisource data combination following consensus theory. The classification is not influenced by the limitations of dimensionality, and the calculation complexity primarily depends on the step of dimensionality reduction. Fusion of single polarization TerraSAR-X, WorldView-2 (VNIR or full set), and Digital Surface Model (DSM) data allow for different types of urban objects to be classified into predefined classes of interest with increased accuracy. The comparison to classification results of WorldView-2 multispectral data (8 spectral bands) is provided and the numerical evaluation of the method in comparison to

  4. Atmospheric pressure chemical ionisation mass spectrometry analysis linked with chemometrics for food classification - a case study: geographical provenance and cultivar classification of monovarietal clarified apple juices.

    Science.gov (United States)

    Gan, Heng-Hui; Soukoulis, Christos; Fisk, Ian

    2014-03-01

    In the present work, we have evaluated for first time the feasibility of APCI-MS volatile compound fingerprinting in conjunction with chemometrics (PLS-DA) as a new strategy for rapid and non-destructive food classification. For this purpose 202 clarified monovarietal juices extracted from apples differing in their botanical and geographical origin were used for evaluation of the performance of APCI-MS as a classification tool. For an independent test set PLS-DA analyses of pre-treated spectral data gave 100% and 94.2% correct classification rate for the classification by cultivar and geographical origin, respectively. Moreover, PLS-DA analysis of APCI-MS in conjunction with GC-MS data revealed that masses within the spectral ACPI-MS data set were related with parent ions or fragments of alkyesters, carbonyl compounds (hexanal, trans-2-hexenal) and alcohols (1-hexanol, 1-butanol, cis-3-hexenol) and had significant discriminating power both in terms of cultivar and geographical origin. Copyright © 2013 The Authors. Published by Elsevier Ltd.. All rights reserved.

  5. Hyperspectral image classification using Support Vector Machine

    International Nuclear Information System (INIS)

    Moughal, T A

    2013-01-01

    Classification of land cover hyperspectral images is a very challenging task due to the unfavourable ratio between the number of spectral bands and the number of training samples. The focus in many applications is to investigate an effective classifier in terms of accuracy. The conventional multiclass classifiers have the ability to map the class of interest but the considerable efforts and large training sets are required to fully describe the classes spectrally. Support Vector Machine (SVM) is suggested in this paper to deal with the multiclass problem of hyperspectral imagery. The attraction to this method is that it locates the optimal hyper plane between the class of interest and the rest of the classes to separate them in a new high-dimensional feature space by taking into account only the training samples that lie on the edge of the class distributions known as support vectors and the use of the kernel functions made the classifier more flexible by making it robust against the outliers. A comparative study has undertaken to find an effective classifier by comparing Support Vector Machine (SVM) to the other two well known classifiers i.e. Maximum likelihood (ML) and Spectral Angle Mapper (SAM). At first, the Minimum Noise Fraction (MNF) was applied to extract the best possible features form the hyperspectral imagery and then the resulting subset of the features was applied to the classifiers. Experimental results illustrate that the integration of MNF and SVM technique significantly reduced the classification complexity and improves the classification accuracy.

  6. Original article Personality determinants of motivation to undertake vocational training

    Directory of Open Access Journals (Sweden)

    Dorota Godlewska-Werner

    2014-05-01

    Full Text Available Background Recently, at a time of frequent changes in the economic and socio-economic circumstances, knowledge acquired in the course of formal education is insufficient. Especially, the education system is still criticized for a lack of flexibility and strong resistance to change. Therefore, regular participation in various forms of training is required. Employee education and training are becoming an optimal answer to complex business challenges. The aim of this study was to determine which personality traits are responsible for the strength of motivation to undertake vocational training and other educational forms. Participants and procedure The study included 104 staff members of Polish companies (60 women and 44 men. The study used Cattell’s 16 PF Questionnaire and the scales of readiness to undertake training and further education as a measure of the strength of motivation (Kawecka, Łaguna & Tabor, 2010. Results The study showed that openness to change and tension (primary traits had the greatest impact on the intention and planning to take vocational training. Additionally, the intention and planning to take vocational training were found to be associated with mindedness, independence, self-control, and anxiety (secondary traits. Such traits as rule-consciousness [G], social-boldness [H], abstractedness [M], and apprehension [O] (primary traits, were important in some aspects, which could constitute a background for further research and discussion of the results. Conclusions The obtained results lead to the conclusion that some of the individual differences in personality determine the motivation to undertake vocational training.

  7. Classification of real farm conditions Iberian pigs according to the feeding regime with multivariate models developed by using fatty acids composition or NIR spectral data

    Directory of Open Access Journals (Sweden)

    De Pedro, Emiliano

    2009-07-01

    Full Text Available Multivariate Classification models to classify real farm conditions Iberian pigs, according to the feeding regime were developed by using fatty acids composition or NIR spectral data of liquid fat samples. A total of 121 subcutaneous fat samples were taken from Iberian pigs carcasses belonging to 5 batches reared under different feeding systems. Once the liquid sample was extracted from each subcutaneous fat sample, it was determined the percentage of 11 fatty acids (C14:0, C16:0, C16:1, C17:0, C17:1, C18:0, C18:1, C18:2, C18:3, C20:0 and C20:1. At the same time, Near Infrared (NIR spectrum of each liquid sample was obtained. Linear Discriminant Analysis (LDA was considered as pattern recognition method to develop the multivariate models. Classification errors of the LDA models generated by using NIR spectral data were 0.0% and 1.7% for the model generated by using fatty acids composition. Results confirm the possibility to discriminate Iberian pig liquid samples from animals reared under different feeding regimes on real farm conditions by using NIR spectral data or fatty acids composition. Classification error obtained using models generated from NIR spectral data were lower than those obtained in models based on fatty acids composition.Se han desarrollado modelos multivariantes, generados a partir de la composición en ácidos grasos o datos espectrales NIR, para clasificar según el régimen alimenticio cerdos Ibéricos producidos bajo condiciones no experimentales. Se han empleado 121 muestras de grasa líquida procedentes de grasa subcutánea de canales de cerdos Ibéricos pertenecientes a 5 partidas con regímenes alimenticios diferentes. A dichas muestras líquidas se les determinó el contenido en 11 ácidos grasos (C14:0, C16:0, C16:1, C17:0, C17:1, C18:0, C18:1, C18:2, C18:3, C20:0 and C20:1 y se obtuvo su espectro NIR. Los modelos de clasificación multivariantes se desarrollaron mediante Análisis Discriminante Lineal. Dichos

  8. TESTING OF LAND COVER CLASSIFICATION FROM MULTISPECTRAL AIRBORNE LASER SCANNING DATA

    Directory of Open Access Journals (Sweden)

    K. Bakuła

    2016-06-01

    Full Text Available Multispectral Airborne Laser Scanning provides a new opportunity for airborne data collection. It provides high-density topographic surveying and is also a useful tool for land cover mapping. Use of a minimum of three intensity images from a multiwavelength laser scanner and 3D information included in the digital surface model has the potential for land cover/use classification and a discussion about the application of this type of data in land cover/use mapping has recently begun. In the test study, three laser reflectance intensity images (orthogonalized point cloud acquired in green, near-infrared and short-wave infrared bands, together with a digital surface model, were used in land cover/use classification where six classes were distinguished: water, sand and gravel, concrete and asphalt, low vegetation, trees and buildings. In the tested methods, different approaches for classification were applied: spectral (based only on laser reflectance intensity images, spectral with elevation data as additional input data, and spectro-textural, using morphological granulometry as a method of texture analysis of both types of data: spectral images and the digital surface model. The method of generating the intensity raster was also tested in the experiment. Reference data were created based on visual interpretation of ALS data and traditional optical aerial and satellite images. The results have shown that multispectral ALS data are unlike typical multispectral optical images, and they have a major potential for land cover/use classification. An overall accuracy of classification over 90% was achieved. The fusion of multi-wavelength laser intensity images and elevation data, with the additional use of textural information derived from granulometric analysis of images, helped to improve the accuracy of classification significantly. The method of interpolation for the intensity raster was not very helpful, and using intensity rasters with both first and

  9. Improved land use classification from Landsat and Seasat satellite imagery registered to a common map base

    Science.gov (United States)

    Clark, J.

    1981-01-01

    In the case of Landsat Multispectral Scanner System (MSS) data, ambiguities in spectral signature can arise in urban areas. A study was initiated in the belief that Seasat digital SAR could help provide the spectral separability needed for a more accurate urban land use classification. A description is presented of the results of land use classifications performed on Landsat and preprocessed Seasat imagery that were registered to a common map base. The process of registering imagery and training site boundary coordinates to a common map has been reported by Clark (1980). It is found that preprocessed Seasat imagery provides signatures for urban land uses which are spectrally separable from Landsat signatures. This development appears to significantly improve land use classifications in an urban setting for class 12 (Commercial and Services), class 13 (Industrial), and class 14 (Transportation, Communications, and Utilities).

  10. Combined Kernel-Based BDT-SMO Classification of Hyperspectral Fused Images

    Directory of Open Access Journals (Sweden)

    Fenghua Huang

    2014-01-01

    Full Text Available To solve the poor generalization and flexibility problems that single kernel SVM classifiers have while classifying combined spectral and spatial features, this paper proposed a solution to improve the classification accuracy and efficiency of hyperspectral fused images: (1 different radial basis kernel functions (RBFs are employed for spectral and textural features, and a new combined radial basis kernel function (CRBF is proposed by combining them in a weighted manner; (2 the binary decision tree-based multiclass SMO (BDT-SMO is used in the classification of hyperspectral fused images; (3 experiments are carried out, where the single radial basis function- (SRBF- based BDT-SMO classifier and the CRBF-based BDT-SMO classifier are used, respectively, to classify the land usages of hyperspectral fused images, and genetic algorithms (GA are used to optimize the kernel parameters of the classifiers. The results show that, compared with SRBF, CRBF-based BDT-SMO classifiers display greater classification accuracy and efficiency.

  11. Using an undertaker's data to assess changing patterns of mortality ...

    African Journals Online (AJOL)

    Key informant interviews were done to support the undertaker's data and determine how families bear the burden of burying deceased relatives. Despite a disproportionate increase in deaths in certain age categories and evidence of worsening poverty, funerals remain large and elaborate affairs. Keywords: AIDS, burial ...

  12. Implications of sensor design for coral reef detection: Upscaling ground hyperspectral imagery in spatial and spectral scales

    Science.gov (United States)

    Caras, Tamir; Hedley, John; Karnieli, Arnon

    2017-12-01

    Remote sensing offers a potential tool for large scale environmental surveying and monitoring. However, remote observations of coral reefs are difficult especially due to the spatial and spectral complexity of the target compared to sensor specifications as well as the environmental implications of the water medium above. The development of sensors is driven by technological advances and the desired products. Currently, spaceborne systems are technologically limited to a choice between high spectral resolution and high spatial resolution, but not both. The current study explores the dilemma of whether future sensor design for marine monitoring should prioritise on improving their spatial or spectral resolution. To address this question, a spatially and spectrally resampled ground-level hyperspectral image was used to test two classification elements: (1) how the tradeoff between spatial and spectral resolutions affects classification; and (2) how a noise reduction by majority filter might improve classification accuracy. The studied reef, in the Gulf of Aqaba (Eilat), Israel, is heterogeneous and complex so the local substrate patches are generally finer than currently available imagery. Therefore, the tested spatial resolution was broadly divided into four scale categories from five millimeters to one meter. Spectral resolution resampling aimed to mimic currently available and forthcoming spaceborne sensors such as (1) Environmental Mapping and Analysis Program (EnMAP) that is characterized by 25 bands of 6.5 nm width; (2) VENμS with 12 narrow bands; and (3) the WorldView series with broadband multispectral resolution. Results suggest that spatial resolution should generally be prioritized for coral reef classification because the finer spatial scale tested (pixel size mind, while the focus in this study was on the technologically limited spaceborne design, aerial sensors may presently provide an opportunity to implement the suggested setup.

  13. AN EXTENDED SPECTRAL–SPATIAL CLASSIFICATION APPROACH FOR HYPERSPECTRAL DATA

    Directory of Open Access Journals (Sweden)

    D. Akbari

    2017-11-01

    Full Text Available In this paper an extended classification approach for hyperspectral imagery based on both spectral and spatial information is proposed. The spatial information is obtained by an enhanced marker-based minimum spanning forest (MSF algorithm. Three different methods of dimension reduction are first used to obtain the subspace of hyperspectral data: (1 unsupervised feature extraction methods including principal component analysis (PCA, independent component analysis (ICA, and minimum noise fraction (MNF; (2 supervised feature extraction including decision boundary feature extraction (DBFE, discriminate analysis feature extraction (DAFE, and nonparametric weighted feature extraction (NWFE; (3 genetic algorithm (GA. The spectral features obtained are then fed into the enhanced marker-based MSF classification algorithm. In the enhanced MSF algorithm, the markers are extracted from the classification maps obtained by both SVM and watershed segmentation algorithm. To evaluate the proposed approach, the Pavia University hyperspectral data is tested. Experimental results show that the proposed approach using GA achieves an approximately 8 % overall accuracy higher than the original MSF-based algorithm.

  14. Classification and Recognition of Tomb Information in Hyperspectral Image

    Science.gov (United States)

    Gu, M.; Lyu, S.; Hou, M.; Ma, S.; Gao, Z.; Bai, S.; Zhou, P.

    2018-04-01

    There are a large number of materials with important historical information in ancient tombs. However, in many cases, these substances could become obscure and indistinguishable by human naked eye or true colour camera. In order to classify and identify materials in ancient tomb effectively, this paper applied hyperspectral imaging technology to archaeological research of ancient tomb in Shanxi province. Firstly, the feature bands including the main information at the bottom of the ancient tomb are selected by the Principal Component Analysis (PCA) transformation to realize the data dimension. Then, the image classification was performed using Support Vector Machine (SVM) based on feature bands. Finally, the material at the bottom of ancient tomb is identified by spectral analysis and spectral matching. The results show that SVM based on feature bands can not only ensure the classification accuracy, but also shorten the data processing time and improve the classification efficiency. In the material identification, it is found that the same matter identified in the visible light is actually two different substances. This research result provides a new reference and research idea for archaeological work.

  15. A Biologically Inspired Approach to Frequency Domain Feature Extraction for EEG Classification

    Directory of Open Access Journals (Sweden)

    Nurhan Gursel Ozmen

    2018-01-01

    Full Text Available Classification of electroencephalogram (EEG signal is important in mental decoding for brain-computer interfaces (BCI. We introduced a feature extraction approach based on frequency domain analysis to improve the classification performance on different mental tasks using single-channel EEG. This biologically inspired method extracts the most discriminative spectral features from power spectral densities (PSDs of the EEG signals. We applied our method on a dataset of six subjects who performed five different imagination tasks: (i resting state, (ii mental arithmetic, (iii imagination of left hand movement, (iv imagination of right hand movement, and (v imagination of letter “A.” Pairwise and multiclass classifications were performed in single EEG channel using Linear Discriminant Analysis and Support Vector Machines. Our method produced results (mean classification accuracy of 83.06% for binary classification and 91.85% for multiclassification that are on par with the state-of-the-art methods, using single-channel EEG with low computational cost. Among all task pairs, mental arithmetic versus letter imagination yielded the best result (mean classification accuracy of 90.29%, indicating that this task pair could be the most suitable pair for a binary class BCI. This study contributes to the development of single-channel BCI, as well as finding the best task pair for user defined applications.

  16. A new classification method for MALDI imaging mass spectrometry data acquired on formalin-fixed paraffin-embedded tissue samples.

    Science.gov (United States)

    Boskamp, Tobias; Lachmund, Delf; Oetjen, Janina; Cordero Hernandez, Yovany; Trede, Dennis; Maass, Peter; Casadonte, Rita; Kriegsmann, Jörg; Warth, Arne; Dienemann, Hendrik; Weichert, Wilko; Kriegsmann, Mark

    2017-07-01

    Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) shows a high potential for applications in histopathological diagnosis, and in particular for supporting tumor typing and subtyping. The development of such applications requires the extraction of spectral fingerprints that are relevant for the given tissue and the identification of biomarkers associated with these spectral patterns. We propose a novel data analysis method based on the extraction of characteristic spectral patterns (CSPs) that allow automated generation of classification models for spectral data. Formalin-fixed paraffin embedded (FFPE) tissue samples from N=445 patients assembled on 12 tissue microarrays were analyzed. The method was applied to discriminate primary lung and pancreatic cancer, as well as adenocarcinoma and squamous cell carcinoma of the lung. A classification accuracy of 100% and 82.8%, resp., could be achieved on core level, assessed by cross-validation. The method outperformed the more conventional classification method based on the extraction of individual m/z values in the first application, while achieving a comparable accuracy in the second. LC-MS/MS peptide identification demonstrated that the spectral features present in selected CSPs correspond to peptides relevant for the respective classification. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann. Copyright © 2016 Elsevier B.V. All rights reserved.

  17. a Hyperspectral Image Classification Method Using Isomap and Rvm

    Science.gov (United States)

    Chang, H.; Wang, T.; Fang, H.; Su, Y.

    2018-04-01

    Classification is one of the most significant applications of hyperspectral image processing and even remote sensing. Though various algorithms have been proposed to implement and improve this application, there are still drawbacks in traditional classification methods. Thus further investigations on some aspects, such as dimension reduction, data mining, and rational use of spatial information, should be developed. In this paper, we used a widely utilized global manifold learning approach, isometric feature mapping (ISOMAP), to address the intrinsic nonlinearities of hyperspectral image for dimension reduction. Considering the impropriety of Euclidean distance in spectral measurement, we applied spectral angle (SA) for substitute when constructed the neighbourhood graph. Then, relevance vector machines (RVM) was introduced to implement classification instead of support vector machines (SVM) for simplicity, generalization and sparsity. Therefore, a probability result could be obtained rather than a less convincing binary result. Moreover, taking into account the spatial information of the hyperspectral image, we employ a spatial vector formed by different classes' ratios around the pixel. At last, we combined the probability results and spatial factors with a criterion to decide the final classification result. To verify the proposed method, we have implemented multiple experiments with standard hyperspectral images compared with some other methods. The results and different evaluation indexes illustrated the effectiveness of our method.

  18. SUPPORT VECTOR MACHINE CLASSIFICATION OF OBJECT-BASED DATA FOR CROP MAPPING, USING MULTI-TEMPORAL LANDSAT IMAGERY

    Directory of Open Access Journals (Sweden)

    R. Devadas

    2012-07-01

    Full Text Available Crop mapping and time series analysis of agronomic cycles are critical for monitoring land use and land management practices, and analysing the issues of agro-environmental impacts and climate change. Multi-temporal Landsat data can be used to analyse decadal changes in cropping patterns at field level, owing to its medium spatial resolution and historical availability. This study attempts to develop robust remote sensing techniques, applicable across a large geographic extent, for state-wide mapping of cropping history in Queensland, Australia. In this context, traditional pixel-based classification was analysed in comparison with image object-based classification using advanced supervised machine-learning algorithms such as Support Vector Machine (SVM. For the Darling Downs region of southern Queensland we gathered a set of Landsat TM images from the 2010–2011 cropping season. Landsat data, along with the vegetation index images, were subjected to multiresolution segmentation to obtain polygon objects. Object-based methods enabled the analysis of aggregated sets of pixels, and exploited shape-related and textural variation, as well as spectral characteristics. SVM models were chosen after examining three shape-based parameters, twenty-three textural parameters and ten spectral parameters of the objects. We found that the object-based methods were superior to the pixel-based methods for classifying 4 major landuse/land cover classes, considering the complexities of within field spectral heterogeneity and spectral mixing. Comparative analysis clearly revealed that higher overall classification accuracy (95% was observed in the object-based SVM compared with that of traditional pixel-based classification (89% using maximum likelihood classifier (MLC. Object-based classification also resulted speckle-free images. Further, object-based SVM models were used to classify different broadacre crop types for summer and winter seasons. The influence of

  19. Combining extreme learning machines using support vector machines for breast tissue classification.

    Science.gov (United States)

    Daliri, Mohammad Reza

    2015-01-01

    In this paper, we present a new approach for breast tissue classification using the features derived from electrical impedance spectroscopy. This method is composed of a feature extraction method, feature selection phase and a classification step. The feature extraction phase derives the features from the electrical impedance spectra. The extracted features consist of the impedivity at zero frequency (I0), the phase angle at 500 KHz, the high-frequency slope of phase angle, the impedance distance between spectral ends, the area under spectrum, the normalised area, the maximum of the spectrum, the distance between impedivity at I0 and the real part of the maximum frequency point and the length of the spectral curve. The system uses the information theoretic criterion as a strategy for feature selection and the combining extreme learning machines (ELMs) for the classification phase. The results of several ELMs are combined using the support vector machines classifier, and the result of classification is reported as a measure of the performance of the system. The results indicate that the proposed system achieves high accuracy in classification of breast tissues using the electrical impedance spectroscopy.

  20. Accuracy in mineral identification: image spectral and spatial resolutions and mineral spectral properties

    Directory of Open Access Journals (Sweden)

    L. Pompilio

    2006-06-01

    Full Text Available Problems related to airborne hyperspectral image data are reviewed and the requirements for data analysis applied to mineralogical (rocks and soils interpretation are discussed. The variability of mineral spectral features, including absorption position, shape and depth is considered and interpreted as due to chemical composition, grain size effects and mineral association. It is also shown how this variability can be related to well defined geologic processes. The influence of sensor noise and diffuse atmospheric radiance in classification accuracy is also analyzed.

  1. Decision tree approach for classification of remotely sensed satellite

    Indian Academy of Sciences (India)

    DTC) algorithm for classification of remotely sensed satellite data (Landsat TM) using open source support. The decision tree is constructed by recursively partitioning the spectral distribution of the training dataset using WEKA, open source ...

  2. Classification of natural formations based on their optical characteristics using small volumes of samples

    Science.gov (United States)

    Abramovich, N. S.; Kovalev, A. A.; Plyuta, V. Y.

    1986-02-01

    A computer algorithm has been developed to classify the spectral bands of natural scenes on Earth according to their optical characteristics. The algorithm is written in FORTRAN-IV and can be used in spectral data processing programs requiring small data loads. The spectral classifications of some different types of green vegetable canopies are given in order to illustrate the effectiveness of the algorithm.

  3. Object Classification Using Airborne Multispectral LiDAR Data

    Directory of Open Access Journals (Sweden)

    PAN Suoyan

    2018-02-01

    Full Text Available Airborne multispectral LiDAR system,which obtains surface geometry and spectral data of objects,simultaneously,has become a fast effective,large-scale spatial data acquisition method.Multispectral LiDAR data are characteristics of completeness and consistency of spectrum and spatial geometric information.Support vector machine (SVM,a machine learning method,is capable of classifying objects based on small samples.Therefore,by means of SVM,this paper performs land cover classification using multispectral LiDAR data. First,all independent point cloud with different wavelengths are merged into a single point cloud,where each pixel contains the three-wavelength spectral information.Next,the merged point cloud is converted into range and intensity images.Finally,land-cover classification is performed by means of SVM.All experiments were conducted on the Optech Titan multispectral LiDAR data,containing three individual point cloud collected by 532 nm,1024 nm,and 1550 nm laser beams.Experimental results demonstrate that ①compared to traditional single-wavelength LiDAR data,multispectral LiDAR data provide a promising solution to land use and land cover applications;②SVM is a feasible method for land cover classification of multispectral LiDAR data.

  4. Improving urban land use and land cover classification from high-spatial-resolution hyperspectral imagery using contextual information

    Science.gov (United States)

    Yang, He; Ma, Ben; Du, Qian; Yang, Chenghai

    2010-08-01

    In this paper, we propose approaches to improve the pixel-based support vector machine (SVM) classification for urban land use and land cover (LULC) mapping from airborne hyperspectral imagery with high spatial resolution. Class spatial neighborhood relationship is used to correct the misclassified class pairs, such as roof and trail, road and roof. These classes may be difficult to be separated because they may have similar spectral signatures and their spatial features are not distinct enough to help their discrimination. In addition, misclassification incurred from within-class trivial spectral variation can be corrected by using pixel connectivity information in a local window so that spectrally homogeneous regions can be well preserved. Our experimental results demonstrate the efficiency of the proposed approaches in classification accuracy improvement. The overall performance is competitive to the object-based SVM classification.

  5. EEG BASED COGNITIVE WORKLOAD CLASSIFICATION DURING NASA MATB-II MULTITASKING

    Directory of Open Access Journals (Sweden)

    Sushil Chandra

    2015-06-01

    Full Text Available The objective of this experiment was to determine the best possible input EEG feature for classification of the workload while designing load balancing logic for an automated operator. The input features compared in this study consisted of spectral features of Electroencephalography, objective scoring and subjective scoring. Method utilizes to identify best EEG feature as an input in Neural Network Classifiers for workload classification, to identify channels which could provide classification with the highest accuracy and for identification of EEG feature which could give discrimination among workload level without adding any classifiers. The result had shown Engagement Index is the best feature for neural network classification.

  6. Biologically-inspired data decorrelation for hyper-spectral imaging

    Directory of Open Access Journals (Sweden)

    Ghita Ovidiu

    2011-01-01

    Full Text Available Abstract Hyper-spectral data allows the construction of more robust statistical models to sample the material properties than the standard tri-chromatic color representation. However, because of the large dimensionality and complexity of the hyper-spectral data, the extraction of robust features (image descriptors is not a trivial issue. Thus, to facilitate efficient feature extraction, decorrelation techniques are commonly applied to reduce the dimensionality of the hyper-spectral data with the aim of generating compact and highly discriminative image descriptors. Current methodologies for data decorrelation such as principal component analysis (PCA, linear discriminant analysis (LDA, wavelet decomposition (WD, or band selection methods require complex and subjective training procedures and in addition the compressed spectral information is not directly related to the physical (spectral characteristics associated with the analyzed materials. The major objective of this article is to introduce and evaluate a new data decorrelation methodology using an approach that closely emulates the human vision. The proposed data decorrelation scheme has been employed to optimally minimize the amount of redundant information contained in the highly correlated hyper-spectral bands and has been comprehensively evaluated in the context of non-ferrous material classification

  7. Is spectral reflectance of the face a reliable biometric?

    Science.gov (United States)

    Uzair, Muhammad; Mahmood, Arif; Shafait, Faisal; Nansen, Christian; Mian, Ajmal

    2015-06-15

    Over a decade ago, Pan et al. [IEEE TPAMI 25, 1552 (2003)] performed face recognition using only the spectral reflectance of the face at six points and reported around 95% recognition rate. Since their database is private, no one has been able to replicate these results. Moreover, due to the unavailability of public datasets, there has been no detailed study in the literature on the viability of facial spectral reflectance for person identification. In this study, we introduce a new public database of facial spectral reflectance profiles measured with a high precision spectrometer. For each of the 40 subjects, spectral reflectance was measured at the same six points as Pan et al. [IEEE TPAMI 25, 1552 (2003)] in multiple sessions and with time lapse. Furthermore, we sample the facial spectral reflectance from two public hyperspectral face image datasets and analyzed the data using state of the art face classification techniques. The best performing classifier achieved the maximum rank-1 identification rate of 53.8%. We conclude that facial spectral reflectance alone is not a reliable biometric for unconstrained face recognition.

  8. Classification and mapping of rangeland vegetation physiognomic ...

    African Journals Online (AJOL)

    Plot vegetation species growth form, cover and height data were collected from 450 sampling sites based on eight spectral strata generated using unsupervised image classification. Field data were grouped at four levels of seven, six, three and two vegetation physiognomic classes which were subjected to both ML and ...

  9. Manifold learning based feature extraction for classification of hyper-spectral data

    CSIR Research Space (South Africa)

    Lunga, D

    2013-08-01

    Full Text Available Advances in hyperspectral sensing provide new capability for characterizing spectral signatures in a wide range of physical and biological systems, while inspiring new methods for extracting information from these data. Hyperspectral image data...

  10. On the Use of Complementary Spectral Features for Speaker Recognition

    Directory of Open Access Journals (Sweden)

    Sridhar Krishnan

    2007-12-01

    Full Text Available The most popular features for speaker recognition are Mel frequency cepstral coefficients (MFCCs and linear prediction cepstral coefficients (LPCCs. These features are used extensively because they characterize the vocal tract configuration which is known to be highly speaker-dependent. In this work, several features are introduced that can characterize the vocal system in order to complement the traditional features and produce better speaker recognition models. The spectral centroid (SC, spectral bandwidth (SBW, spectral band energy (SBE, spectral crest factor (SCF, spectral flatness measure (SFM, Shannon entropy (SE, and Renyi entropy (RE were utilized for this purpose. This work demonstrates that these features are robust in noisy conditions by simulating some common distortions that are found in the speakers' environment and a typical telephone channel. Babble noise, additive white Gaussian noise (AWGN, and a bandpass channel with 1 dB of ripple were used to simulate these noisy conditions. The results show significant improvements in classification performance for all noise conditions when these features were used to complement the MFCC and ΔMFCC features. In particular, the SC and SCF improved performance in almost all noise conditions within the examined SNR range (10–40 dB. For example, in cases where there was only one source of distortion, classification improvements of up to 8% and 10% were achieved under babble noise and AWGN, respectively, using the SCF feature.

  11. Land-cover classification with an expert classification algorithm using digital aerial photographs

    Directory of Open Access Journals (Sweden)

    José L. de la Cruz

    2010-05-01

    Full Text Available The purpose of this study was to evaluate the usefulness of the spectral information of digital aerial sensors in determining land-cover classification using new digital techniques. The land covers that have been evaluated are the following, (1 bare soil, (2 cereals, including maize (Zea mays L., oats (Avena sativa L., rye (Secale cereale L., wheat (Triticum aestivum L. and barley (Hordeun vulgare L., (3 high protein crops, such as peas (Pisum sativum L. and beans (Vicia faba L., (4 alfalfa (Medicago sativa L., (5 woodlands and scrublands, including holly oak (Quercus ilex L. and common retama (Retama sphaerocarpa L., (6 urban soil, (7 olive groves (Olea europaea L. and (8 burnt crop stubble. The best result was obtained using an expert classification algorithm, achieving a reliability rate of 95%. This result showed that the images of digital airborne sensors hold considerable promise for the future in the field of digital classifications because these images contain valuable information that takes advantage of the geometric viewpoint. Moreover, new classification techniques reduce problems encountered using high-resolution images; while reliabilities are achieved that are better than those achieved with traditional methods.

  12. Improving discrimination of savanna tree species through a multiple endmember spectral-angle-mapper (SAM) approach: canopy level analysis

    CSIR Research Space (South Africa)

    Cho, Moses A

    2010-11-01

    Full Text Available sensing. The objectives of this paper were to (i) evaluate the classification performance of a multiple-endmember spectral angle mapper (SAM) classification approach (conventionally known as the nearest neighbour) in discriminating ten common African...

  13. Spectral properties of 441 radio pulsars

    Science.gov (United States)

    Jankowski, F.; van Straten, W.; Keane, E. F.; Bailes, M.; Barr, E. D.; Johnston, S.; Kerr, M.

    2018-02-01

    We present a study of the spectral properties of 441 pulsars observed with the Parkes radio telescope near the centre frequencies of 728, 1382 and 3100 MHz. The observations at 728 and 3100 MHz were conducted simultaneously using the dual-band 10-50 cm receiver. These high-sensitivity, multifrequency observations provide a systematic and uniform sample of pulsar flux densities. We combine our measurements with spectral data from the literature in order to derive the spectral properties of these pulsars. Using techniques from robust regression and information theory, we classify the observed spectra in an objective, robust and unbiased way into five morphological classes: simple or broken power law, power law with either low- or high-frequency cut-off and log-parabolic spectrum. While about 79 per cent of the pulsars that could be classified have simple power-law spectra, we find significant deviations in 73 pulsars, 35 of which have curved spectra, 25 with a spectral break and 10 with a low-frequency turn-over. We identify 11 gigahertz-peaked spectrum (GPS) pulsars, with 3 newly identified in this work and 8 confirmations of known GPS pulsars; 3 others show tentative evidence of GPS, but require further low-frequency measurements to support this classification. The weighted mean spectral index of all pulsars with simple power-law spectra is -1.60 ± 0.03. The observed spectral indices are well described by a shifted log-normal distribution. The strongest correlations of spectral index are with spin-down luminosity, magnetic field at the light-cylinder and spin-down rate. We also investigate the physical origin of the observed spectral features and determine emission altitudes for three pulsars.

  14. An Object-Oriented Classification Method on High Resolution Satellite Data

    National Research Council Canada - National Science Library

    Xiaoxia, Sun; Jixian, Zhang; Zhengjun, Liu

    2004-01-01

    .... Thereby only the spectral information is used for the classification. High spatial resolution sensors involves a general increase of spatial information and the accuracy of results may decrease on a per-pixel basis...

  15. Utility of BRDF Models for Estimating Optimal View Angles in Classification of Remotely Sensed Images

    Science.gov (United States)

    Valdez, P. F.; Donohoe, G. W.

    1997-01-01

    Statistical classification of remotely sensed images attempts to discriminate between surface cover types on the basis of the spectral response recorded by a sensor. It is well known that surfaces reflect incident radiation as a function of wavelength producing a spectral signature specific to the material under investigation. Multispectral and hyperspectral sensors sample the spectral response over tens and even hundreds of wavelength bands to capture the variation of spectral response with wavelength. Classification algorithms then exploit these differences in spectral response to distinguish between materials of interest. Sensors of this type, however, collect detailed spectral information from one direction (usually nadir); consequently, do not consider the directional nature of reflectance potentially detectable at different sensor view angles. Improvements in sensor technology have resulted in remote sensing platforms capable of detecting reflected energy across wavelengths (spectral signatures) and from multiple view angles (angular signatures) in the fore and aft directions. Sensors of this type include: the moderate resolution imaging spectroradiometer (MODIS), the multiangle imaging spectroradiometer (MISR), and the airborne solid-state array spectroradiometer (ASAS). A goal of this paper, then, is to explore the utility of Bidirectional Reflectance Distribution Function (BRDF) models in the selection of optimal view angles for the classification of remotely sensed images by employing a strategy of searching for the maximum difference between surface BRDFs. After a brief discussion of directional reflect ante in Section 2, attention is directed to the Beard-Maxwell BRDF model and its use in predicting the bidirectional reflectance of a surface. The selection of optimal viewing angles is addressed in Section 3, followed by conclusions and future work in Section 4.

  16. A semi-supervised classification algorithm using the TAD-derived background as training data

    Science.gov (United States)

    Fan, Lei; Ambeau, Brittany; Messinger, David W.

    2013-05-01

    In general, spectral image classification algorithms fall into one of two categories: supervised and unsupervised. In unsupervised approaches, the algorithm automatically identifies clusters in the data without a priori information about those clusters (except perhaps the expected number of them). Supervised approaches require an analyst to identify training data to learn the characteristics of the clusters such that they can then classify all other pixels into one of the pre-defined groups. The classification algorithm presented here is a semi-supervised approach based on the Topological Anomaly Detection (TAD) algorithm. The TAD algorithm defines background components based on a mutual k-Nearest Neighbor graph model of the data, along with a spectral connected components analysis. Here, the largest components produced by TAD are used as regions of interest (ROI's),or training data for a supervised classification scheme. By combining those ROI's with a Gaussian Maximum Likelihood (GML) or a Minimum Distance to the Mean (MDM) algorithm, we are able to achieve a semi supervised classification method. We test this classification algorithm against data collected by the HyMAP sensor over the Cooke City, MT area and University of Pavia scene.

  17. Testing the Potential of Vegetation Indices for Land Use/cover Classification Using High Resolution Data

    Science.gov (United States)

    Karakacan Kuzucu, A.; Bektas Balcik, F.

    2017-11-01

    Accurate and reliable land use/land cover (LULC) information obtained by remote sensing technology is necessary in many applications such as environmental monitoring, agricultural management, urban planning, hydrological applications, soil management, vegetation condition study and suitability analysis. But this information still remains a challenge especially in heterogeneous landscapes covering urban and rural areas due to spectrally similar LULC features. In parallel with technological developments, supplementary data such as satellite-derived spectral indices have begun to be used as additional bands in classification to produce data with high accuracy. The aim of this research is to test the potential of spectral vegetation indices combination with supervised classification methods and to extract reliable LULC information from SPOT 7 multispectral imagery. The Normalized Difference Vegetation Index (NDVI), the Ratio Vegetation Index (RATIO), the Soil Adjusted Vegetation Index (SAVI) were the three vegetation indices used in this study. The classical maximum likelihood classifier (MLC) and support vector machine (SVM) algorithm were applied to classify SPOT 7 image. Catalca is selected region located in the north west of the Istanbul in Turkey, which has complex landscape covering artificial surface, forest and natural area, agricultural field, quarry/mining area, pasture/scrubland and water body. Accuracy assessment of all classified images was performed through overall accuracy and kappa coefficient. The results indicated that the incorporation of these three different vegetation indices decrease the classification accuracy for the MLC and SVM classification. In addition, the maximum likelihood classification slightly outperformed the support vector machine classification approach in both overall accuracy and kappa statistics.

  18. An investigation of nurse educator's perceptions and experiences of undertaking clinical practice.

    Science.gov (United States)

    Williams, Angela; Taylor, Cathy

    2008-11-01

    Educational policy (DOH, 1999. Making a difference: strengthening the nursing, midwifery and health visiting contribution to health and healthcare. Department of Health, London; UKCC, 1999. Fitness for Practice. United Kingdom Central Council for Nursing, Midwifery and Health Visiting, London; Nursing and Midwifery Council, 2006. Standards to support learning and assessment in practice. Nursing and Midwifery Council, London) and current nursing literature (Griscti, O., Jacono, B., Jacono, J., 2005. The nurse educator's clinical role. Journal of Advanced Nursing 50 (1), 84-92; Owen, S., Ferguson, K., Baguley, I., 2005. The clinical activity of mental health nurse lecturers. Journal of Psychiatric and Mental Health Nursing 12, 310-316), place increasing emphasis on nurse educators undertaking clinical practice to facilitate their clinical confidence and competence. This study investigated nurse educators' perceptions and experiences of undertaking clinical practice. A qualitative design and descriptive, exploratory approach were used. A purposive sample of 11 nurse educators in one nursing department, took part in two focus group interviews, one with 5 and the other with 6 respondents, to identify and discuss their perceptions and experiences of undertaking clinical practice. A process of thematic content analysis revealed three broad themes relating to the meaning and importance of clinical practice, perceived benefits and barriers which are examined and discussed. The paper concludes that despite policy recommendations, barriers highlighted in this study such as insufficient time, heavy workload and a lack of valuing of the clinical role have been raised over the past few decades. The effect of undertaking clinical practice, particularly on the quality of teaching is argued to be valuable armoury in the battle to secure sufficient resources to support engagement in clinical practice. Financial and organisational commitment; valuing of clinical practice and research

  19. CLASSIFICATION AND RECOGNITION OF TOMB INFORMATION IN HYPERSPECTRAL IMAGE

    Directory of Open Access Journals (Sweden)

    M. Gu

    2018-04-01

    Full Text Available There are a large number of materials with important historical information in ancient tombs. However, in many cases, these substances could become obscure and indistinguishable by human naked eye or true colour camera. In order to classify and identify materials in ancient tomb effectively, this paper applied hyperspectral imaging technology to archaeological research of ancient tomb in Shanxi province. Firstly, the feature bands including the main information at the bottom of the ancient tomb are selected by the Principal Component Analysis (PCA transformation to realize the data dimension. Then, the image classification was performed using Support Vector Machine (SVM based on feature bands. Finally, the material at the bottom of ancient tomb is identified by spectral analysis and spectral matching. The results show that SVM based on feature bands can not only ensure the classification accuracy, but also shorten the data processing time and improve the classification efficiency. In the material identification, it is found that the same matter identified in the visible light is actually two different substances. This research result provides a new reference and research idea for archaeological work.

  20. Experimental study on multi-sub-classifier for land cover classification: a case study in Shangri-La, China

    Science.gov (United States)

    Wang, Yan-ying; Wang, Jin-liang; Wang, Ping; Hu, Wen-yin; Su, Shao-hua

    2015-12-01

    High accuracy remote sensed image classification technology is a long-term and continuous pursuit goal of remote sensing applications. In order to evaluate single classification algorithm accuracy, take Landsat TM image as data source, Northwest Yunnan as study area, seven types of land cover classification like Maximum Likelihood Classification has been tested, the results show that: (1)the overall classification accuracy of Maximum Likelihood Classification(MLC), Artificial Neural Network Classification(ANN), Minimum Distance Classification(MinDC) is higher, which is 82.81% and 82.26% and 66.41% respectively; the overall classification accuracy of Parallel Hexahedron Classification(Para), Spectral Information Divergence Classification(SID), Spectral Angle Classification(SAM) is low, which is 37.29%, 38.37, 53.73%, respectively. (2) from each category classification accuracy: although the overall accuracy of the Para is the lowest, it is much higher on grasslands, wetlands, forests, airport land, which is 89.59%, 94.14%, and 89.04%, respectively; the SAM, SID are good at forests classification with higher overall classification accuracy, which is 89.8% and 87.98%, respectively. Although the overall classification accuracy of ANN is very high, the classification accuracy of road, rural residential land and airport land is very low, which is 10.59%, 11% and 11.59% respectively. Other classification methods have their advantages and disadvantages. These results show that, under the same conditions, the same images with different classification methods to classify, there will be a classifier to some features has higher classification accuracy, a classifier to other objects has high classification accuracy, and therefore, we may select multi sub-classifier integration to improve the classification accuracy.

  1. The qualitative interview and challenges for clinicians undertaking research: a personal reflection.

    Science.gov (United States)

    Fisher, Karin

    2011-01-01

    Drawing on my doctoral experience the aim of this article is to present my transition from practitioner to novice researcher and the challenges I encountered when undertaking qualitative in-depth interviews. The contents of my research diary were coded for words, sentences and paragraphs and were then grouped into themes and subsequently organised into concepts and categories. The analysis identified one core category: 'changing states: learning to become a researcher'. The related categories included 'guessing responses', 'confusing boundaries' and 'revealing hidden concepts'. These concepts provide a description of how I learnt to become a researcher and became a changed state. The paper provides practitioners with practical examples of my transition from practitioner to novice researcher. I offer some tips for practitioners who wish to undertake research in their clinical role.

  2. Classification of the Correct Quranic Letters Pronunciation of Male and Female Reciters

    Science.gov (United States)

    Khairuddin, Safiah; Ahmad, Salmiah; Embong, Abdul Halim; Nur Wahidah Nik Hashim, Nik; Altamas, Tareq M. K.; Nuratikah Syd Badaruddin, Syarifah; Shahbudin Hassan, Surul

    2017-11-01

    Recitation of the Holy Quran with the correct Tajweed is essential for every Muslim. Islam has encouraged Quranic education since early age as the recitation of the Quran correctly will represent the correct meaning of the words of Allah. It is important to recite the Quranic verses according to its characteristics (sifaat) and from its point of articulations (makhraj). This paper presents the identification and classification analysis of Quranic letters pronunciation for both male and female reciters, to obtain the unique representation of each letter by male as compared to female expert reciters. Linear Discriminant Analysis (LDA) was used as the classifier to classify the data with Formants and Power Spectral Density (PSD) as the acoustic features. The result shows that linear classifier of PSD with band 1 and band 2 power spectral combinations gives a high percentage of classification accuracy for most of the Quranic letters. It is also shown that the pronunciation by male reciters gives better result in the classification of the Quranic letters.

  3. Urban Image Classification: Per-Pixel Classifiers, Sub-Pixel Analysis, Object-Based Image Analysis, and Geospatial Methods. 10; Chapter

    Science.gov (United States)

    Myint, Soe W.; Mesev, Victor; Quattrochi, Dale; Wentz, Elizabeth A.

    2013-01-01

    Remote sensing methods used to generate base maps to analyze the urban environment rely predominantly on digital sensor data from space-borne platforms. This is due in part from new sources of high spatial resolution data covering the globe, a variety of multispectral and multitemporal sources, sophisticated statistical and geospatial methods, and compatibility with GIS data sources and methods. The goal of this chapter is to review the four groups of classification methods for digital sensor data from space-borne platforms; per-pixel, sub-pixel, object-based (spatial-based), and geospatial methods. Per-pixel methods are widely used methods that classify pixels into distinct categories based solely on the spectral and ancillary information within that pixel. They are used for simple calculations of environmental indices (e.g., NDVI) to sophisticated expert systems to assign urban land covers. Researchers recognize however, that even with the smallest pixel size the spectral information within a pixel is really a combination of multiple urban surfaces. Sub-pixel classification methods therefore aim to statistically quantify the mixture of surfaces to improve overall classification accuracy. While within pixel variations exist, there is also significant evidence that groups of nearby pixels have similar spectral information and therefore belong to the same classification category. Object-oriented methods have emerged that group pixels prior to classification based on spectral similarity and spatial proximity. Classification accuracy using object-based methods show significant success and promise for numerous urban 3 applications. Like the object-oriented methods that recognize the importance of spatial proximity, geospatial methods for urban mapping also utilize neighboring pixels in the classification process. The primary difference though is that geostatistical methods (e.g., spatial autocorrelation methods) are utilized during both the pre- and post-classification

  4. Classifications of PSN J03034759+0024146 and CSS141123:091002+521856

    Science.gov (United States)

    Shivvers, I.; Filippenko, A. V.

    2014-11-01

    We report the spectral classification of two optical transients. Spectra of these objects (range 330-1000 nm) were obtained on November 26 UT with the 3-m Shane reflector (+ Kast) at Lick Observatory.

  5. Classification of Clean and Dirty Pighouse Surfaces Based on Spectral Reflectance

    DEFF Research Database (Denmark)

    Blanke, Mogens; Braithwaite, Ian David; Zhang, Guo-Qiang

    2004-01-01

    of designing a vision based system to locate dirty areas and subsequently direct a cleaning robot to remove dirt. Novel results include the characterisation of the spectral reflectance of real surfaces and dirt in a pig house and the design of illumination to obtain discrimination of clean from dirty areas...

  6. Classifications of objects on hyperspectral images

    DEFF Research Database (Denmark)

    Kucheryavskiy, Sergey

    . In the present work a classification method that combines classic image classification approach and MIA is proposed. The basic idea is to group all pixels and calculate spectral properties of the pixel group to be used further as a vector of predictors for calibration and class prediction. The grouping can...... be done with mathematical morphology methods applied to a score image where objects are well separated. In the case of small overlapping a watershed transformation can be applied to disjoint the objects. The method has been tested on several simulated and real cases and showed good results and significant...... improvements in comparison with a standard MIA approach. The results as well as method details will be reported....

  7. Comparing and optimizing land use classification in a Himalayan area using parametric and non parametric approaches

    NARCIS (Netherlands)

    Sterk, G.; Sameer Saran,; Raju, P.L.N.; Amit, Bharti

    2007-01-01

    Supervised classification is one of important tasks in remote sensing image interpretation, in which the image pixels are classified to various predefined land use/land cover classes based on the spectral reflectance values in different bands. In reality some classes may have very close spectral

  8. A classification of spectral populations observed in HF radar backscatter from the E region auroral electrojets

    Directory of Open Access Journals (Sweden)

    S. E. Milan

    2001-02-01

    Full Text Available Observations of HF radar backscatter from the auroral electrojet E region indicate the presence of five major spectral populations, as opposed to the two predominant spectral populations, types I and II, observed in the VHF regime. The Doppler shift, spectral width, backscatter power, and flow angle dependencies of these five populations are investigated and described. Two of these populations are identified with type I and type II spectral classes, and hence, are thought to be generated by the two-stream and gradient drift instabilities, respectively. The remaining three populations occur over a range of velocities which can greatly exceed the ion acoustic speed, the usual limiting velocity in VHF radar observations of the E region. The generation of these spectral populations is discussed in terms of electron density gradients in the electrojet region and recent non-linear theories of E region irregularity generation.Key words. Ionosphere (ionospheric irregularities

  9. Classification of ion mobility spectra by functional groups using neural networks

    Science.gov (United States)

    Bell, S.; Nazarov, E.; Wang, Y. F.; Eiceman, G. A.

    1999-01-01

    Neural networks were trained using whole ion mobility spectra from a standardized database of 3137 spectra for 204 chemicals at various concentrations. Performance of the network was measured by the success of classification into ten chemical classes. Eleven stages for evaluation of spectra and of spectral pre-processing were employed and minimums established for response thresholds and spectral purity. After optimization of the database, network, and pre-processing routines, the fraction of successful classifications by functional group was 0.91 throughout a range of concentrations. Network classification relied on a combination of features, including drift times, number of peaks, relative intensities, and other factors apparently including peak shape. The network was opportunistic, exploiting different features within different chemical classes. Application of neural networks in a two-tier design where chemicals were first identified by class and then individually eliminated all but one false positive out of 161 test spectra. These findings establish that ion mobility spectra, even with low resolution instrumentation, contain sufficient detail to permit the development of automated identification systems.

  10. Who should be undertaking population-based surveys in humanitarian emergencies?

    Directory of Open Access Journals (Sweden)

    Spiegel Paul B

    2007-06-01

    Full Text Available Abstract Background Timely and accurate data are necessary to prioritise and effectively respond to humanitarian emergencies. 30-by-30 cluster surveys are commonly used in humanitarian emergencies because of their purported simplicity and reasonable validity and precision. Agencies have increasingly used 30-by-30 cluster surveys to undertake measurements beyond immunisation coverage and nutritional status. Methodological errors in cluster surveys have likely occurred for decades in humanitarian emergencies, often with unknown or unevaluated consequences. Discussion Most surveys in humanitarian emergencies are done by non-governmental organisations (NGOs. Some undertake good quality surveys while others have an already overburdened staff with limited epidemiological skills. Manuals explaining cluster survey methodology are available and in use. However, it is debatable as to whether using standardised, 'cookbook' survey methodologies are appropriate. Coordination of surveys is often lacking. If a coordinating body is established, as recommended, it is questionable whether it should have sole authority to release surveys due to insufficient independence. Donors should provide sufficient funding for personnel, training, and survey implementation, and not solely for direct programme implementation. Summary A dedicated corps of trained epidemiologists needs to be identified and made available to undertake surveys in humanitarian emergencies. NGOs in the field may need to form an alliance with certain specialised agencies or pool technically capable personnel. If NGOs continue to do surveys by themselves, a simple training manual with sample survey questionnaires, methodology, standardised files for data entry and analysis, and manual for interpretation should be developed and modified locally for each situation. At the beginning of an emergency, a central coordinating body should be established that has sufficient authority to set survey standards

  11. A classification of spectral populations observed in HF radar backscatter from the E region auroral electrojets

    Directory of Open Access Journals (Sweden)

    S. E. Milan

    Full Text Available Observations of HF radar backscatter from the auroral electrojet E region indicate the presence of five major spectral populations, as opposed to the two predominant spectral populations, types I and II, observed in the VHF regime. The Doppler shift, spectral width, backscatter power, and flow angle dependencies of these five populations are investigated and described. Two of these populations are identified with type I and type II spectral classes, and hence, are thought to be generated by the two-stream and gradient drift instabilities, respectively. The remaining three populations occur over a range of velocities which can greatly exceed the ion acoustic speed, the usual limiting velocity in VHF radar observations of the E region. The generation of these spectral populations is discussed in terms of electron density gradients in the electrojet region and recent non-linear theories of E region irregularity generation.

    Key words. Ionosphere (ionospheric irregularities

  12. Optimized extreme learning machine for urban land cover classification using hyperspectral imagery

    Science.gov (United States)

    Su, Hongjun; Tian, Shufang; Cai, Yue; Sheng, Yehua; Chen, Chen; Najafian, Maryam

    2017-12-01

    This work presents a new urban land cover classification framework using the firefly algorithm (FA) optimized extreme learning machine (ELM). FA is adopted to optimize the regularization coefficient C and Gaussian kernel σ for kernel ELM. Additionally, effectiveness of spectral features derived from an FA-based band selection algorithm is studied for the proposed classification task. Three sets of hyperspectral databases were recorded using different sensors, namely HYDICE, HyMap, and AVIRIS. Our study shows that the proposed method outperforms traditional classification algorithms such as SVM and reduces computational cost significantly.

  13. Assessing and monitoring of urban vegetation using multiple endmember spectral mixture analysis

    Science.gov (United States)

    Zoran, M. A.; Savastru, R. S.; Savastru, D. M.

    2013-08-01

    During last years urban vegetation with significant health, biological and economical values had experienced dramatic changes due to urbanization and human activities in the metropolitan area of Bucharest in Romania. We investigated the utility of remote sensing approaches of multiple endmember spectral mixture analysis (MESMA) applied to IKONOS and Landsat TM/ETM satellite data for estimating fractional cover of urban/periurban forest, parks, agricultural vegetation areas. Because of the spectral heterogeneity of same physical features of urban vegetation increases with the increase of image resolution, the traditional spectral information-based statistical method may not be useful to classify land cover dynamics from high resolution imageries like IKONOS. So we used hierarchy tree classification method in classification and MESMA for vegetation land cover dynamics assessment based on available IKONOS high-resolution imagery of Bucharest town. This study employs thirty two endmembers and six hundred and sixty spectral models to identify all Earth's features (vegetation, water, soil, impervious) and shade in the Bucharest area. The mean RMS error for the selected vegetation land cover classes range from 0.0027 to 0.018. The Pearson correlation between the fraction outputs from MESMA and reference data from all IKONOS images 1m panchromatic resolution data for urban/periurban vegetation were ranging in the domain 0.7048 - 0.8287. The framework in this study can be applied to other urban vegetation areas in Romania.

  14. Signal classification using global dynamical models, Part I: Theory

    International Nuclear Information System (INIS)

    Kadtke, J.; Kremliovsky, M.

    1996-01-01

    Detection and classification of signals is one of the principal areas of signal processing, and the utilization of nonlinear information has long been considered as a way of improving performance beyond standard linear (e.g. spectral) techniques. Here, we develop a method for using global models of chaotic dynamical systems theory to define a signal classification processing chain, which is sensitive to nonlinear correlations in the data. We use it to demonstrate classification in high noise regimes (negative SNR), and argue that classification probabilities can be directly computed from ensemble statistics in the model coefficient space. We also develop a modification for non-stationary signals (i.e. transients) using non-autonomous ODEs. In Part II of this paper, we demonstrate the analysis on actual open ocean acoustic data from marine biologics. copyright 1996 American Institute of Physics

  15. IRIS COLOUR CLASSIFICATION SCALES--THEN AND NOW.

    Science.gov (United States)

    Grigore, Mariana; Avram, Alina

    2015-01-01

    Eye colour is one of the most obvious phenotypic traits of an individual. Since the first documented classification scale developed in 1843, there have been numerous attempts to classify the iris colour. In the past centuries, iris colour classification scales has had various colour categories and mostly relied on comparison of an individual's eye with painted glass eyes. Once photography techniques were refined, standard iris photographs replaced painted eyes, but this did not solve the problem of painted/ printed colour variability in time. Early clinical scales were easy to use, but lacked objectivity and were not standardised or statistically tested for reproducibility. The era of automated iris colour classification systems came with the technological development. Spectrophotometry, digital analysis of high-resolution iris images, hyper spectral analysis of the human real iris and the dedicated iris colour analysis software, all accomplished an objective, accurate iris colour classification, but are quite expensive and limited in use to research environment. Iris colour classification systems evolved continuously due to their use in a wide range of studies, especially in the fields of anthropology, epidemiology and genetics. Despite the wide range of the existing scales, up until present there has been no generally accepted iris colour classification scale.

  16. Oro-facial pain and temporomandibular disorders classification systems: A critical appraisal and future directions.

    Science.gov (United States)

    Klasser, G D; Manfredini, D; Goulet, J-P; De Laat, A

    2018-03-01

    It is a difficult undertaking to design a classification system for any disease entity, let alone for oro-facial pain (OFP) and more specifically for temporomandibular disorders (TMD). A further complication of this task is that both physical and psychosocial variables must be included. To augment this process, a two-step systematic review, adhering to PRISMA guidelines, of the classification systems published during the last 20 years for OFP and TMD was performed. The first search step identified 190 potential citations which ultimately resulted in only 17 articles being included for in-depth analysis and review. The second step resulted in only 5 articles being selected for inclusion in this review. Five additional articles and four classification guidelines/criteria were also included due to expansion of the search criteria. Thus, in total, 14 documents comprising articles and guidelines/criteria (8 proposals of classification systems for OFP; 6 for TMD) were selected for inclusion in the systematic review. For each, a discussion as to their advantages, strengths and limitations was provided. Suggestions regarding the future direction for improving the classification process with the use of ontological principles rather than taxonomy are discussed. Furthermore, the potential for expanding the scope of axes included in existing classification systems, to include genetic, epigenetic and neurobiological variables, is explored. It is therefore recommended that future classification system proposals be based on combined approaches aiming to provide archetypal treatment-oriented classifications. © 2017 John Wiley & Sons Ltd.

  17. Spectral unmixing of urban land cover using a generic library approach

    Science.gov (United States)

    Degerickx, Jeroen; Lordache, Marian-Daniel; Okujeni, Akpona; Hermy, Martin; van der Linden, Sebastian; Somers, Ben

    2016-10-01

    Remote sensing based land cover classification in urban areas generally requires the use of subpixel classification algorithms to take into account the high spatial heterogeneity. These spectral unmixing techniques often rely on spectral libraries, i.e. collections of pure material spectra (endmembers, EM), which ideally cover the large EM variability typically present in urban scenes. Despite the advent of several (semi-) automated EM detection algorithms, the collection of such image-specific libraries remains a tedious and time-consuming task. As an alternative, we suggest the use of a generic urban EM library, containing material spectra under varying conditions, acquired from different locations and sensors. This approach requires an efficient EM selection technique, capable of only selecting those spectra relevant for a specific image. In this paper, we evaluate and compare the potential of different existing library pruning algorithms (Iterative Endmember Selection and MUSIC) using simulated hyperspectral (APEX) data of the Brussels metropolitan area. In addition, we develop a new hybrid EM selection method which is shown to be highly efficient in dealing with both imagespecific and generic libraries, subsequently yielding more robust land cover classification results compared to existing methods. Future research will include further optimization of the proposed algorithm and additional tests on both simulated and real hyperspectral data.

  18. Hyperspectral small animal fluorescence imaging: spectral selection imaging

    Science.gov (United States)

    Leavesley, Silas; Jiang, Yanan; Patsekin, Valery; Hall, Heidi; Vizard, Douglas; Robinson, J. Paul

    2008-02-01

    Molecular imaging is a rapidly growing area of research, fueled by needs in pharmaceutical drug-development for methods for high-throughput screening, pre-clinical and clinical screening for visualizing tumor growth and drug targeting, and a growing number of applications in the molecular biology fields. Small animal fluorescence imaging employs fluorescent probes to target molecular events in vivo, with a large number of molecular targeting probes readily available. The ease at which new targeting compounds can be developed, the short acquisition times, and the low cost (compared to microCT, MRI, or PET) makes fluorescence imaging attractive. However, small animal fluorescence imaging suffers from high optical scattering, absorption, and autofluorescence. Much of these problems can be overcome through multispectral imaging techniques, which collect images at different fluorescence emission wavelengths, followed by analysis, classification, and spectral deconvolution methods to isolate signals from fluorescence emission. We present an alternative to the current method, using hyperspectral excitation scanning (spectral selection imaging), a technique that allows excitation at any wavelength in the visible and near-infrared wavelength range. In many cases, excitation imaging may be more effective at identifying specific fluorescence signals because of the higher complexity of the fluorophore excitation spectrum. Because the excitation is filtered and not the emission, the resolution limit and image shift imposed by acousto-optic tunable filters have no effect on imager performance. We will discuss design of the imager, optimizing the imager for use in small animal fluorescence imaging, and application of spectral analysis and classification methods for identifying specific fluorescence signals.

  19. A DIMENSION REDUCTION-BASED METHOD FOR CLASSIFICATION OF HYPERSPECTRAL AND LIDAR DATA

    Directory of Open Access Journals (Sweden)

    B. Abbasi

    2015-12-01

    Full Text Available The existence of various natural objects such as grass, trees, and rivers along with artificial manmade features such as buildings and roads, make it difficult to classify ground objects. Consequently using single data or simple classification approach cannot improve classification results in object identification. Also, using of a variety of data from different sensors; increase the accuracy of spatial and spectral information. In this paper, we proposed a classification algorithm on joint use of hyperspectral and Lidar (Light Detection and Ranging data based on dimension reduction. First, some feature extraction techniques are applied to achieve more information from Lidar and hyperspectral data. Also Principal component analysis (PCA and Minimum Noise Fraction (MNF have been utilized to reduce the dimension of spectral features. The number of 30 features containing the most information of the hyperspectral images is considered for both PCA and MNF. In addition, Normalized Difference Vegetation Index (NDVI has been measured to highlight the vegetation. Furthermore, the extracted features from Lidar data calculated based on relation between every pixel of data and surrounding pixels in local neighbourhood windows. The extracted features are based on the Grey Level Co-occurrence Matrix (GLCM matrix. In second step, classification is operated in all features which obtained by MNF, PCA, NDVI and GLCM and trained by class samples. After this step, two classification maps are obtained by SVM classifier with MNF+NDVI+GLCM features and PCA+NDVI+GLCM features, respectively. Finally, the classified images are fused together to create final classification map by decision fusion based majority voting strategy.

  20. The Infrared Telescope Facility (IRTF) Spectral Library: Cool Stars

    Science.gov (United States)

    Rayner, John T.; Cushing, Michael C.; Vacca, William D.

    2009-12-01

    We present a 0.8-5 μm spectral library of 210 cool stars observed at a resolving power of R ≡ λ/Δλ ~ 2000 with the medium-resolution infrared spectrograph, SpeX, at the 3.0 m NASA Infrared Telescope Facility (IRTF) on Mauna Kea, Hawaii. The stars have well-established MK spectral classifications and are mostly restricted to near-solar metallicities. The sample not only contains the F, G, K, and M spectral types with luminosity classes between I and V, but also includes some AGB, carbon, and S stars. In contrast to some other spectral libraries, the continuum shape of the spectra is measured and preserved in the data reduction process. The spectra are absolutely flux calibrated using the Two Micron All Sky Survey photometry. Potential uses of the library include studying the physics of cool stars, classifying and studying embedded young clusters and optically obscured regions of the Galaxy, evolutionary population synthesis to study unresolved stellar populations in optically obscured regions of galaxies and synthetic photometry. The library is available in digital form from the IRTF Web site.

  1. Using ecological zones to increase the detail of Landsat classifications

    Science.gov (United States)

    Fox, L., III; Mayer, K. E.

    1981-01-01

    Changes in classification detail of forest species descriptions were made for Landsat data on 2.2 million acres in northwestern California. Because basic forest canopy structures may exhibit very similar E-M energy reflectance patterns in different environmental regions, classification labels based on Landsat spectral signatures alone become very generalized when mapping large heterogeneous ecological regions. By adding a seven ecological zone stratification, a 167% improvement in classification detail was made over the results achieved without it. The seven zone stratification is a less costly alternative to the inclusion of complex collateral information, such as terrain data and soil type, into the Landsat data base when making inventories of areas greater than 500,000 acres.

  2. Hyperspectral image classification based on local binary patterns and PCANet

    Science.gov (United States)

    Yang, Huizhen; Gao, Feng; Dong, Junyu; Yang, Yang

    2018-04-01

    Hyperspectral image classification has been well acknowledged as one of the challenging tasks of hyperspectral data processing. In this paper, we propose a novel hyperspectral image classification framework based on local binary pattern (LBP) features and PCANet. In the proposed method, linear prediction error (LPE) is first employed to select a subset of informative bands, and LBP is utilized to extract texture features. Then, spectral and texture features are stacked into a high dimensional vectors. Next, the extracted features of a specified position are transformed to a 2-D image. The obtained images of all pixels are fed into PCANet for classification. Experimental results on real hyperspectral dataset demonstrate the effectiveness of the proposed method.

  3. Deep multi-scale convolutional neural network for hyperspectral image classification

    Science.gov (United States)

    Zhang, Feng-zhe; Yang, Xia

    2018-04-01

    In this paper, we proposed a multi-scale convolutional neural network for hyperspectral image classification task. Firstly, compared with conventional convolution, we utilize multi-scale convolutions, which possess larger respective fields, to extract spectral features of hyperspectral image. We design a deep neural network with a multi-scale convolution layer which contains 3 different convolution kernel sizes. Secondly, to avoid overfitting of deep neural network, dropout is utilized, which randomly sleeps neurons, contributing to improve the classification accuracy a bit. In addition, new skills like ReLU in deep learning is utilized in this paper. We conduct experiments on University of Pavia and Salinas datasets, and obtained better classification accuracy compared with other methods.

  4. The IACOB project. V. Spectroscopic parameters of the O-type stars in the modern grid of standards for spectral classification

    Science.gov (United States)

    Holgado, G.; Simón-Díaz, S.; Barbá, R. H.; Puls, J.; Herrero, A.; Castro, N.; Garcia, M.; Maíz Apellániz, J.; Negueruela, I.; Sabín-Sanjulián, C.

    2018-06-01

    Context. The IACOB and OWN surveys are two ambitious, complementary observational projects which have made available a large multi-epoch spectroscopic database of optical high resolution spectra of Galactic massive O-type stars. Aims: Our aim is to study the full sample of (more than 350) O stars surveyed by the IACOB and OWN projects. As a first step towards this aim, we have performed the quantitative spectroscopic analysis of a subsample of 128 stars included in the modern grid of O-type standards for spectral classification. The sample comprises stars with spectral types in the range O3-O9.7 and covers all luminosity classes. Methods: We used the semi-automatized IACOB-BROAD and IACOB-GBAT/FASTWIND tools to determine the complete set of spectroscopic parameters that can be obtained from the optical spectrum of O-type stars. A quality flag was assigned to the outcome of the IACOB-GBAT/FASTWIND analysis for each star, based on a visual evaluation of how the synthetic spectrum of the best fitting FASTWIND model reproduces the observed spectrum. We also benefitted from the multi-epoch character of the IACOB and OWN surveys to perform a spectroscopic variability study of the complete sample, providing two different flags for each star accounting for spectroscopic binarity as well as variability of the main wind diagnostic lines. Results: We obtain - for the first time in a homogeneous and complete manner - the full set of spectroscopic parameters of the "anchors" of the spectral classification system in the O star domain. We provide a general overview of the stellar and wind parameters of this reference sample, as well as updated recipes for the SpT-Teff and SpT-log g calibrations for Galactic O-type stars. We also propose a distance-independent test for the wind-momentum luminosity relationship. We evaluate the reliability of our semi-automatized analysis strategy using a subsample of 40 stars extensively studied in the literature, and find a fairly good agreement

  5. Land Cover and Land Use Classification with TWOPAC: towards Automated Processing for Pixel- and Object-Based Image Classification

    Directory of Open Access Journals (Sweden)

    Stefan Dech

    2012-09-01

    Full Text Available We present a novel and innovative automated processing environment for the derivation of land cover (LC and land use (LU information. This processing framework named TWOPAC (TWinned Object and Pixel based Automated classification Chain enables the standardized, independent, user-friendly, and comparable derivation of LC and LU information, with minimized manual classification labor. TWOPAC allows classification of multi-spectral and multi-temporal remote sensing imagery from different sensor types. TWOPAC enables not only pixel-based classification, but also allows classification based on object-based characteristics. Classification is based on a Decision Tree approach (DT for which the well-known C5.0 code has been implemented, which builds decision trees based on the concept of information entropy. TWOPAC enables automatic generation of the decision tree classifier based on a C5.0-retrieved ascii-file, as well as fully automatic validation of the classification output via sample based accuracy assessment.Envisaging the automated generation of standardized land cover products, as well as area-wide classification of large amounts of data in preferably a short processing time, standardized interfaces for process control, Web Processing Services (WPS, as introduced by the Open Geospatial Consortium (OGC, are utilized. TWOPAC’s functionality to process geospatial raster or vector data via web resources (server, network enables TWOPAC’s usability independent of any commercial client or desktop software and allows for large scale data processing on servers. Furthermore, the components of TWOPAC were built-up using open source code components and are implemented as a plug-in for Quantum GIS software for easy handling of the classification process from the user’s perspective.

  6. Generative Adversarial Networks-Based Semi-Supervised Learning for Hyperspectral Image Classification

    Directory of Open Access Journals (Sweden)

    Zhi He

    2017-10-01

    Full Text Available Classification of hyperspectral image (HSI is an important research topic in the remote sensing community. Significant efforts (e.g., deep learning have been concentrated on this task. However, it is still an open issue to classify the high-dimensional HSI with a limited number of training samples. In this paper, we propose a semi-supervised HSI classification method inspired by the generative adversarial networks (GANs. Unlike the supervised methods, the proposed HSI classification method is semi-supervised, which can make full use of the limited labeled samples as well as the sufficient unlabeled samples. Core ideas of the proposed method are twofold. First, the three-dimensional bilateral filter (3DBF is adopted to extract the spectral-spatial features by naturally treating the HSI as a volumetric dataset. The spatial information is integrated into the extracted features by 3DBF, which is propitious to the subsequent classification step. Second, GANs are trained on the spectral-spatial features for semi-supervised learning. A GAN contains two neural networks (i.e., generator and discriminator trained in opposition to one another. The semi-supervised learning is achieved by adding samples from the generator to the features and increasing the dimension of the classifier output. Experimental results obtained on three benchmark HSI datasets have confirmed the effectiveness of the proposed method , especially with a limited number of labeled samples.

  7. Assessing therapeutic relevance of biologically interesting, ampholytic substances based on their physicochemical and spectral characteristics with chemometric tools

    Science.gov (United States)

    Judycka, U.; Jagiello, K.; Bober, L.; Błażejowski, J.; Puzyn, T.

    2018-06-01

    Chemometric tools were applied to investigate the biological behaviour of ampholytic substances in relation to their physicochemical and spectral properties. Results of the Principal Component Analysis suggest that size of molecules and their electronic and spectral characteristics are the key properties required to predict therapeutic relevance of the compounds examined. These properties were used for developing the structure-activity classification model. The classification model allows assessing the therapeutic behaviour of ampholytic substances on the basis of solely values of descriptors that can be obtained computationally. Thus, the prediction is possible without necessity of carrying out time-consuming and expensive laboratory tests, which is its main advantage.

  8. Preliminary Research on Grassland Fine-classification Based on MODIS

    International Nuclear Information System (INIS)

    Hu, Z W; Zhang, S; Yu, X Y; Wang, X S

    2014-01-01

    Grassland ecosystem is important for climatic regulation, maintaining the soil and water. Research on the grassland monitoring method could provide effective reference for grassland resource investigation. In this study, we used the vegetation index method for grassland classification. There are several types of climate in China. Therefore, we need to use China's Main Climate Zone Maps and divide the study region into four climate zones. Based on grassland classification system of the first nation-wide grass resource survey in China, we established a new grassland classification system which is only suitable for this research. We used MODIS images as the basic data resources, and use the expert classifier method to perform grassland classification. Based on the 1:1,000,000 Grassland Resource Map of China, we obtained the basic distribution of all the grassland types and selected 20 samples evenly distributed in each type, then used NDVI/EVI product to summarize different spectral features of different grassland types. Finally, we introduced other classification auxiliary data, such as elevation, accumulate temperature (AT), humidity index (HI) and rainfall. China's nation-wide grassland classification map is resulted by merging the grassland in different climate zone. The overall classification accuracy is 60.4%. The result indicated that expert classifier is proper for national wide grassland classification, but the classification accuracy need to be improved

  9. DETAILED CLASSIFICATION OF SWIFT 'S GAMMA-RAY BURSTS

    International Nuclear Information System (INIS)

    Horvath, I.; Veres, P.; Bagoly, Z.; Balazs, L. G.; De Ugarte Postigo, A.; Meszaros, A.

    2010-01-01

    Earlier classification analyses found three types of gamma-ray bursts (short, long, and intermediate in duration) in the BATSE sample. Recent works have shown that these three groups are also present in the RHESSI and BeppoSAX databases. The duration distribution analysis of the bursts observed by the Swift satellite also favors the three-component model. In this paper, we extend the analysis of the Swift data with spectral information. We show, using the spectral hardness and duration simultaneously, that the maximum likelihood method favors the three-component against the two-component model. The likelihood also shows that a fourth component is not needed.

  10. Support Vector Machines for Hyperspectral Remote Sensing Classification

    Science.gov (United States)

    Gualtieri, J. Anthony; Cromp, R. F.

    1998-01-01

    The Support Vector Machine provides a new way to design classification algorithms which learn from examples (supervised learning) and generalize when applied to new data. We demonstrate its success on a difficult classification problem from hyperspectral remote sensing, where we obtain performances of 96%, and 87% correct for a 4 class problem, and a 16 class problem respectively. These results are somewhat better than other recent results on the same data. A key feature of this classifier is its ability to use high-dimensional data without the usual recourse to a feature selection step to reduce the dimensionality of the data. For this application, this is important, as hyperspectral data consists of several hundred contiguous spectral channels for each exemplar. We provide an introduction to this new approach, and demonstrate its application to classification of an agriculture scene.

  11. Tile-Based Semisupervised Classification of Large-Scale VHR Remote Sensing Images

    Directory of Open Access Journals (Sweden)

    Haikel Alhichri

    2018-01-01

    Full Text Available This paper deals with the problem of the classification of large-scale very high-resolution (VHR remote sensing (RS images in a semisupervised scenario, where we have a limited training set (less than ten training samples per class. Typical pixel-based classification methods are unfeasible for large-scale VHR images. Thus, as a practical and efficient solution, we propose to subdivide the large image into a grid of tiles and then classify the tiles instead of classifying pixels. Our proposed method uses the power of a pretrained convolutional neural network (CNN to first extract descriptive features from each tile. Next, a neural network classifier (composed of 2 fully connected layers is trained in a semisupervised fashion and used to classify all remaining tiles in the image. This basically presents a coarse classification of the image, which is sufficient for many RS application. The second contribution deals with the employment of the semisupervised learning to improve the classification accuracy. We present a novel semisupervised approach which exploits both the spectral and spatial relationships embedded in the remaining unlabelled tiles. In particular, we embed a spectral graph Laplacian in the hidden layer of the neural network. In addition, we apply regularization of the output labels using a spatial graph Laplacian and the random Walker algorithm. Experimental results obtained by testing the method on two large-scale images acquired by the IKONOS2 sensor reveal promising capabilities of this method in terms of classification accuracy even with less than ten training samples per class.

  12. Spectral Target Detection using Schroedinger Eigenmaps

    Science.gov (United States)

    Dorado-Munoz, Leidy P.

    Applications of optical remote sensing processes include environmental monitoring, military monitoring, meteorology, mapping, surveillance, etc. Many of these tasks include the detection of specific objects or materials, usually few or small, which are surrounded by other materials that clutter the scene and hide the relevant information. This target detection process has been boosted lately by the use of hyperspectral imagery (HSI) since its high spectral dimension provides more detailed spectral information that is desirable in data exploitation. Typical spectral target detectors rely on statistical or geometric models to characterize the spectral variability of the data. However, in many cases these parametric models do not fit well HSI data that impacts the detection performance. On the other hand, non-linear transformation methods, mainly based on manifold learning algorithms, have shown a potential use in HSI transformation, dimensionality reduction and classification. In target detection, non-linear transformation algorithms are used as preprocessing techniques that transform the data to a more suitable lower dimensional space, where the statistical or geometric detectors are applied. One of these non-linear manifold methods is the Schroedinger Eigenmaps (SE) algorithm that has been introduced as a technique for semi-supervised classification. The core tool of the SE algorithm is the Schroedinger operator that includes a potential term that encodes prior information about the materials present in a scene, and enables the embedding to be steered in some convenient directions in order to cluster similar pixels together. A completely novel target detection methodology based on SE algorithm is proposed for the first time in this thesis. The proposed methodology does not just include the transformation of the data to a lower dimensional space but also includes the definition of a detector that capitalizes on the theory behind SE. The fact that target pixels and

  13. IRIS COLOUR CLASSIFICATION SCALES – THEN AND NOW

    Science.gov (United States)

    Grigore, Mariana; Avram, Alina

    2015-01-01

    Eye colour is one of the most obvious phenotypic traits of an individual. Since the first documented classification scale developed in 1843, there have been numerous attempts to classify the iris colour. In the past centuries, iris colour classification scales has had various colour categories and mostly relied on comparison of an individual’s eye with painted glass eyes. Once photography techniques were refined, standard iris photographs replaced painted eyes, but this did not solve the problem of painted/ printed colour variability in time. Early clinical scales were easy to use, but lacked objectivity and were not standardised or statistically tested for reproducibility. The era of automated iris colour classification systems came with the technological development. Spectrophotometry, digital analysis of high-resolution iris images, hyper spectral analysis of the human real iris and the dedicated iris colour analysis software, all accomplished an objective, accurate iris colour classification, but are quite expensive and limited in use to research environment. Iris colour classification systems evolved continuously due to their use in a wide range of studies, especially in the fields of anthropology, epidemiology and genetics. Despite the wide range of the existing scales, up until present there has been no generally accepted iris colour classification scale. PMID:27373112

  14. Classification of subsurface objects using singular values derived from signal frames

    Science.gov (United States)

    Chambers, David H; Paglieroni, David W

    2014-05-06

    The classification system represents a detected object with a feature vector derived from the return signals acquired by an array of N transceivers operating in multistatic mode. The classification system generates the feature vector by transforming the real-valued return signals into complex-valued spectra, using, for example, a Fast Fourier Transform. The classification system then generates a feature vector of singular values for each user-designated spectral sub-band by applying a singular value decomposition (SVD) to the N.times.N square complex-valued matrix formed from sub-band samples associated with all possible transmitter-receiver pairs. The resulting feature vector of singular values may be transformed into a feature vector of singular value likelihoods and then subjected to a multi-category linear or neural network classifier for object classification.

  15. [Research on Spectral Polarization Imaging System Based on Static Modulation].

    Science.gov (United States)

    Zhao, Hai-bo; Li, Huan; Lin, Xu-ling; Wang, Zheng

    2015-04-01

    The main disadvantages of traditional spectral polarization imaging system are: complex structure, with moving parts, low throughput. A novel method of spectral polarization imaging system is discussed, which is based on static polarization intensity modulation combined with Savart polariscope interference imaging. The imaging system can obtain real-time information of spectral and four Stokes polarization messages. Compared with the conventional methods, the advantages of the imaging system are compactness, low mass and no moving parts, no electrical control, no slit and big throughput. The system structure and the basic theory are introduced. The experimental system is established in the laboratory. The experimental system consists of reimaging optics, polarization intensity module, interference imaging module, and CCD data collecting and processing module. The spectral range is visible and near-infrared (480-950 nm). The white board and the plane toy are imaged by using the experimental system. The ability of obtaining spectral polarization imaging information is verified. The calibration system of static polarization modulation is set up. The statistical error of polarization degree detection is less than 5%. The validity and feasibility of the basic principle is proved by the experimental result. The spectral polarization data captured by the system can be applied to object identification, object classification and remote sensing detection.

  16. Land Cover Classification Using ALOS Imagery For Penang, Malaysia

    International Nuclear Information System (INIS)

    Sim, C K; Abdullah, K; MatJafri, M Z; Lim, H S

    2014-01-01

    This paper presents the potential of integrating optical and radar remote sensing data to improve automatic land cover mapping. The analysis involved standard image processing, and consists of spectral signature extraction and application of a statistical decision rule to identify land cover categories. A maximum likelihood classifier is utilized to determine different land cover categories. Ground reference data from sites throughout the study area are collected for training and validation. The land cover information was extracted from the digital data using PCI Geomatica 10.3.2 software package. The variations in classification accuracy due to a number of radar imaging processing techniques are studied. The relationship between the processing window and the land classification is also investigated. The classification accuracies from the optical and radar feature combinations are studied. Our research finds that fusion of radar and optical significantly improved classification accuracies. This study indicates that the land cover/use can be mapped accurately by using this approach

  17. Selective ablation of Copper-Indium-Diselenide solar cells monitored by laser-induced breakdown spectroscopy and classification methods

    Energy Technology Data Exchange (ETDEWEB)

    Diego-Vallejo, David [Technische Universität Berlin, Institute of Optics and Atomic Physics, Straße des 17, Juni 135, 10623 Berlin (Germany); Laser- und Medizin- Technologie Berlin GmbH (LMTB), Applied Laser Technology, Fabeckstr. 60-62, 14195 Berlin (Germany); Ashkenasi, David, E-mail: d.ashkenasi@lmtb.de [Laser- und Medizin- Technologie Berlin GmbH (LMTB), Applied Laser Technology, Fabeckstr. 60-62, 14195 Berlin (Germany); Lemke, Andreas [Laser- und Medizin- Technologie Berlin GmbH (LMTB), Applied Laser Technology, Fabeckstr. 60-62, 14195 Berlin (Germany); Eichler, Hans Joachim [Technische Universität Berlin, Institute of Optics and Atomic Physics, Straße des 17, Juni 135, 10623 Berlin (Germany); Laser- und Medizin- Technologie Berlin GmbH (LMTB), Applied Laser Technology, Fabeckstr. 60-62, 14195 Berlin (Germany)

    2013-09-01

    Laser-induced breakdown spectroscopy (LIBS) and two classification methods, i.e. linear correlation and artificial neural networks (ANN), are used to monitor P1, P2 and P3 scribing steps of Copper-Indium-Diselenide (CIS) solar cells. Narrow channels featuring complete removal of desired layers with minimum damage on the underlying film are expected to enhance efficiency of solar cells. The monitoring technique is intended to determine that enough material has been removed to reach the desired layer based on the analysis of plasma emission acquired during multiple pass laser scribing. When successful selective scribing is achieved, a high degree of similarity between test and reference spectra has to be identified by classification methods in order to stop the scribing procedure and avoid damaging the bottom layer. Performance of linear correlation and artificial neural networks is compared and evaluated for two spectral bandwidths. By using experimentally determined combinations of classifier and analyzed spectral band for each step, classification performance achieves errors of 7, 1 and 4% for steps P1, P2 and P3, respectively. The feasibility of using plasma emission for the supervision of processing steps of solar cell manufacturing is demonstrated. This method has the potential to be implemented as an online monitoring procedure assisting the production of solar cells. - Highlights: • LIBS and two classification methods were used to monitor CIS solar cells processing. • Selective ablation of thin-film solar cells was improved with inspection system. • Customized classification method and analyzed spectral band enhanced performance.

  18. A Comparative Study of Landsat TM and SPOT HRG Images for Vegetation Classification in the Brazilian Amazon

    Science.gov (United States)

    Lu, Dengsheng; Batistella, Mateus; de Miranda, Evaristo E.; Moran, Emilio

    2009-01-01

    Complex forest structure and abundant tree species in the moist tropical regions often cause difficulties in classifying vegetation classes with remotely sensed data. This paper explores improvement in vegetation classification accuracies through a comparative study of different image combinations based on the integration of Landsat Thematic Mapper (TM) and SPOT High Resolution Geometric (HRG) instrument data, as well as the combination of spectral signatures and textures. A maximum likelihood classifier was used to classify the different image combinations into thematic maps. This research indicated that data fusion based on HRG multispectral and panchromatic data slightly improved vegetation classification accuracies: a 3.1 to 4.6 percent increase in the kappa coefficient compared with the classification results based on original HRG or TM multispectral images. A combination of HRG spectral signatures and two textural images improved the kappa coefficient by 6.3 percent compared with pure HRG multispectral images. The textural images based on entropy or second-moment texture measures with a window size of 9 pixels × 9 pixels played an important role in improving vegetation classification accuracy. Overall, optical remote-sensing data are still insufficient for accurate vegetation classifications in the Amazon basin. PMID:19789716

  19. A Comparative Study of Landsat TM and SPOT HRG Images for Vegetation Classification in the Brazilian Amazon.

    Science.gov (United States)

    Lu, Dengsheng; Batistella, Mateus; de Miranda, Evaristo E; Moran, Emilio

    2008-01-01

    Complex forest structure and abundant tree species in the moist tropical regions often cause difficulties in classifying vegetation classes with remotely sensed data. This paper explores improvement in vegetation classification accuracies through a comparative study of different image combinations based on the integration of Landsat Thematic Mapper (TM) and SPOT High Resolution Geometric (HRG) instrument data, as well as the combination of spectral signatures and textures. A maximum likelihood classifier was used to classify the different image combinations into thematic maps. This research indicated that data fusion based on HRG multispectral and panchromatic data slightly improved vegetation classification accuracies: a 3.1 to 4.6 percent increase in the kappa coefficient compared with the classification results based on original HRG or TM multispectral images. A combination of HRG spectral signatures and two textural images improved the kappa coefficient by 6.3 percent compared with pure HRG multispectral images. The textural images based on entropy or second-moment texture measures with a window size of 9 pixels × 9 pixels played an important role in improving vegetation classification accuracy. Overall, optical remote-sensing data are still insufficient for accurate vegetation classifications in the Amazon basin.

  20. A Robust Classification of Galaxy Spectra: Dealing with Noisy and Incomplete Data

    Science.gov (United States)

    Connolly, A. J.; Szalay, A. S.

    1999-05-01

    Over the next few years new spectroscopic surveys (from the optical surveys of the Sloan Digital Sky Survey and the 2dF Galaxy Survey through to space-based ultraviolet satellites such as GALEX) will provide the opportunity and challenge of understanding how galaxies of different spectral type evolve with redshift. Techniques have been developed to classify galaxies based on their continuum and line spectra. Some of the most promising of these have used the Karhunen & Loève transform (or principal component analysis) to separate galaxies into distinct classes. Their limitation has been that they assume that the spectral coverage and quality of the spectra are constant for all galaxies within a given sample. In this paper we develop a general formalism that accounts for the missing data within the observed spectra (such as the removal of sky lines or the effect of sampling different intrinsic rest-wavelength ranges due to the redshift of a galaxy). We demonstrate that by correcting for these gaps we can recover an almost redshift-independent classification scheme. From this classification we can derive an optimal interpolation that reconstructs the underlying galaxy spectral energy distributions in the regions of missing data. This provides a simple and effective mechanism for building galaxy spectral energy distributions directly from data that may be noisy, incomplete, or drawn from a number of different sources.

  1. 20 CFR 703.205 - Filing of Agreement and Undertaking; deposit of security.

    Science.gov (United States)

    2010-04-01

    ...— (1) Deposit with the Branch indemnity bonds or letters of credit in the amount fixed by the Office... and payable from the proceeds of the deposited security; (b) Give security in the amount fixed in the... 20 Employees' Benefits 3 2010-04-01 2010-04-01 false Filing of Agreement and Undertaking; deposit...

  2. An ensemble classification approach for improved Land use/cover change detection

    Science.gov (United States)

    Chellasamy, M.; Ferré, T. P. A.; Humlekrog Greve, M.; Larsen, R.; Chinnasamy, U.

    2014-11-01

    Change Detection (CD) methods based on post-classification comparison approaches are claimed to provide potentially reliable results. They are considered to be most obvious quantitative method in the analysis of Land Use Land Cover (LULC) changes which provides from - to change information. But, the performance of post-classification comparison approaches highly depends on the accuracy of classification of individual images used for comparison. Hence, we present a classification approach that produce accurate classified results which aids to obtain improved change detection results. Machine learning is a part of broader framework in change detection, where neural networks have drawn much attention. Neural network algorithms adaptively estimate continuous functions from input data without mathematical representation of output dependence on input. A common practice for classification is to use Multi-Layer-Perceptron (MLP) neural network with backpropogation learning algorithm for prediction. To increase the ability of learning and prediction, multiple inputs (spectral, texture, topography, and multi-temporal information) are generally stacked to incorporate diversity of information. On the other hand literatures claims backpropagation algorithm to exhibit weak and unstable learning in use of multiple inputs, while dealing with complex datasets characterized by mixed uncertainty levels. To address the problem of learning complex information, we propose an ensemble classification technique that incorporates multiple inputs for classification unlike traditional stacking of multiple input data. In this paper, we present an Endorsement Theory based ensemble classification that integrates multiple information, in terms of prediction probabilities, to produce final classification results. Three different input datasets are used in this study: spectral, texture and indices, from SPOT-4 multispectral imagery captured on 1998 and 2003. Each SPOT image is classified

  3. THE INFRARED TELESCOPE FACILITY (IRTF) SPECTRAL LIBRARY: COOL STARS

    International Nuclear Information System (INIS)

    Rayner, John T.; Cushing, Michael C.; Vacca, William D.

    2009-01-01

    We present a 0.8-5 μm spectral library of 210 cool stars observed at a resolving power of R ≡ λ/Δλ ∼ 2000 with the medium-resolution infrared spectrograph, SpeX, at the 3.0 m NASA Infrared Telescope Facility (IRTF) on Mauna Kea, Hawaii. The stars have well-established MK spectral classifications and are mostly restricted to near-solar metallicities. The sample not only contains the F, G, K, and M spectral types with luminosity classes between I and V, but also includes some AGB, carbon, and S stars. In contrast to some other spectral libraries, the continuum shape of the spectra is measured and preserved in the data reduction process. The spectra are absolutely flux calibrated using the Two Micron All Sky Survey photometry. Potential uses of the library include studying the physics of cool stars, classifying and studying embedded young clusters and optically obscured regions of the Galaxy, evolutionary population synthesis to study unresolved stellar populations in optically obscured regions of galaxies and synthetic photometry. The library is available in digital form from the IRTF Web site.

  4. Changes in the functions of undertakings in electricity supply

    International Nuclear Information System (INIS)

    Oberlack, H.W.

    1976-01-01

    For the electricity supply industry also it is necessary, by means of more intensive publicity work, to achieve the general realisation that neither new laws nor intervention of the state are required for dealing in the interests of the consumer with the problems arising, from great changes in all fields of business enterprise. It is more important for the electricity supply undertakings (EVU), by means of executive power and the administration of justice, to be put a position to carry out in the most efficient manner the functions entrusted to them by the Federal Government under the Power Supply Law and the energy programme. (orig.) [de

  5. Probability Density Components Analysis: A New Approach to Treatment and Classification of SAR Images

    Directory of Open Access Journals (Sweden)

    Osmar Abílio de Carvalho Júnior

    2014-04-01

    Full Text Available Speckle noise (salt and pepper is inherent to synthetic aperture radar (SAR, which causes a usual noise-like granular aspect and complicates the image classification. In SAR image analysis, the spatial information might be a particular benefit for denoising and mapping classes characterized by a statistical distribution of the pixel intensities from a complex and heterogeneous spectral response. This paper proposes the Probability Density Components Analysis (PDCA, a new alternative that combines filtering and frequency histogram to improve the classification procedure for the single-channel synthetic aperture radar (SAR images. This method was tested on L-band SAR data from the Advanced Land Observation System (ALOS Phased-Array Synthetic-Aperture Radar (PALSAR sensor. The study area is localized in the Brazilian Amazon rainforest, northern Rondônia State (municipality of Candeias do Jamari, containing forest and land use patterns. The proposed algorithm uses a moving window over the image, estimating the probability density curve in different image components. Therefore, a single input image generates an output with multi-components. Initially the multi-components should be treated by noise-reduction methods, such as maximum noise fraction (MNF or noise-adjusted principal components (NAPCs. Both methods enable reducing noise as well as the ordering of multi-component data in terms of the image quality. In this paper, the NAPC applied to multi-components provided large reductions in the noise levels, and the color composites considering the first NAPC enhance the classification of different surface features. In the spectral classification, the Spectral Correlation Mapper and Minimum Distance were used. The results obtained presented as similar to the visual interpretation of optical images from TM-Landsat and Google Maps.

  6. Modified DCTNet for audio signals classification

    Science.gov (United States)

    Xian, Yin; Pu, Yunchen; Gan, Zhe; Lu, Liang; Thompson, Andrew

    2016-10-01

    In this paper, we investigate DCTNet for audio signal classification. Its output feature is related to Cohen's class of time-frequency distributions. We introduce the use of adaptive DCTNet (A-DCTNet) for audio signals feature extraction. The A-DCTNet applies the idea of constant-Q transform, with its center frequencies of filterbanks geometrically spaced. The A-DCTNet is adaptive to different acoustic scales, and it can better capture low frequency acoustic information that is sensitive to human audio perception than features such as Mel-frequency spectral coefficients (MFSC). We use features extracted by the A-DCTNet as input for classifiers. Experimental results show that the A-DCTNet and Recurrent Neural Networks (RNN) achieve state-of-the-art performance in bird song classification rate, and improve artist identification accuracy in music data. They demonstrate A-DCTNet's applicability to signal processing problems.

  7. The Possibilities of Using the Terrestrial Scanning Data for Classification of Rocks in Limestone Mine “Czatkowice”

    Directory of Open Access Journals (Sweden)

    Toś Cezary

    2015-02-01

    Full Text Available This paper presents results of a research of potential utilisation of the intensity of laser beam reflection recorded by ground-based lasers, for an initial classification of rock formations within the Czatkowice Limestone Quarry. As part of the research, spectrometric analysis in visible (VIS, near-infrared (NIR and Short-wavelength infrared (SWIR bands was carried out for rock samples typical for the Czatkowice Quarry. Moreover, the rock samples were scanned using equipment working within different wavelengths. The reflected intensity of the laser beam recorded for each rock sample with several different scanners were analysed to assess their potential use for rock classification. The results of this analysis were then compared with spectral curves of each sample. The relationship between the intensity of the laser beam reflection and the spectral curves can be used for selection of most suitable scanner for rock classification.

  8. The taxonomic history of the Ricciaceae (1937-1995 and a classification of sub-Saharan Ricciae

    Directory of Open Access Journals (Sweden)

    S. M. Perold

    1995-10-01

    Full Text Available The taxonomic history of the Ricciaceae (1937-1995 is reviewed. Over the years many attempts have been made to subdivide and rearrange the taxa in this large and puzzling family, but consensus has still not been reached. Hepaticologists are therefore urged to undertake more revisions worldwide, using modem methods of investigation to aid them in. hopefully, coming to an eventual agreement in defining the limits of subgenera, sections, species and subspecies in the Ricciae. In addition, a classification of sub-Saharan Ricciaceae. partially based on informal groups, is herein proposed.

  9. Deep learning for tumor classification in imaging mass spectrometry.

    Science.gov (United States)

    Behrmann, Jens; Etmann, Christian; Boskamp, Tobias; Casadonte, Rita; Kriegsmann, Jörg; Maaß, Peter

    2018-04-01

    Tumor classification using imaging mass spectrometry (IMS) data has a high potential for future applications in pathology. Due to the complexity and size of the data, automated feature extraction and classification steps are required to fully process the data. Since mass spectra exhibit certain structural similarities to image data, deep learning may offer a promising strategy for classification of IMS data as it has been successfully applied to image classification. Methodologically, we propose an adapted architecture based on deep convolutional networks to handle the characteristics of mass spectrometry data, as well as a strategy to interpret the learned model in the spectral domain based on a sensitivity analysis. The proposed methods are evaluated on two algorithmically challenging tumor classification tasks and compared to a baseline approach. Competitiveness of the proposed methods is shown on both tasks by studying the performance via cross-validation. Moreover, the learned models are analyzed by the proposed sensitivity analysis revealing biologically plausible effects as well as confounding factors of the considered tasks. Thus, this study may serve as a starting point for further development of deep learning approaches in IMS classification tasks. https://gitlab.informatik.uni-bremen.de/digipath/Deep_Learning_for_Tumor_Classification_in_IMS. jbehrmann@uni-bremen.de or christianetmann@uni-bremen.de. Supplementary data are available at Bioinformatics online.

  10. Crown-level tree species classification from AISA hyperspectral imagery using an innovative pixel-weighting approach

    Science.gov (United States)

    Liu, Haijian; Wu, Changshan

    2018-06-01

    Crown-level tree species classification is a challenging task due to the spectral similarity among different tree species. Shadow, underlying objects, and other materials within a crown may decrease the purity of extracted crown spectra and further reduce classification accuracy. To address this problem, an innovative pixel-weighting approach was developed for tree species classification at the crown level. The method utilized high density discrete LiDAR data for individual tree delineation and Airborne Imaging Spectrometer for Applications (AISA) hyperspectral imagery for pure crown-scale spectra extraction. Specifically, three steps were included: 1) individual tree identification using LiDAR data, 2) pixel-weighted representative crown spectra calculation using hyperspectral imagery, with which pixel-based illuminated-leaf fractions estimated using a linear spectral mixture analysis (LSMA) were employed as weighted factors, and 3) representative spectra based tree species classification was performed through applying a support vector machine (SVM) approach. Analysis of results suggests that the developed pixel-weighting approach (OA = 82.12%, Kc = 0.74) performed better than treetop-based (OA = 70.86%, Kc = 0.58) and pixel-majority methods (OA = 72.26, Kc = 0.62) in terms of classification accuracy. McNemar tests indicated the differences in accuracy between pixel-weighting and treetop-based approaches as well as that between pixel-weighting and pixel-majority approaches were statistically significant.

  11. Land-cover classification in a moist tropical region of Brazil with Landsat TM imagery.

    Science.gov (United States)

    Li, Guiying; Lu, Dengsheng; Moran, Emilio; Hetrick, Scott

    2011-01-01

    This research aims to improve land-cover classification accuracy in a moist tropical region in Brazil by examining the use of different remote sensing-derived variables and classification algorithms. Different scenarios based on Landsat Thematic Mapper (TM) spectral data and derived vegetation indices and textural images, and different classification algorithms - maximum likelihood classification (MLC), artificial neural network (ANN), classification tree analysis (CTA), and object-based classification (OBC), were explored. The results indicated that a combination of vegetation indices as extra bands into Landsat TM multispectral bands did not improve the overall classification performance, but the combination of textural images was valuable for improving vegetation classification accuracy. In particular, the combination of both vegetation indices and textural images into TM multispectral bands improved overall classification accuracy by 5.6% and kappa coefficient by 6.25%. Comparison of the different classification algorithms indicated that CTA and ANN have poor classification performance in this research, but OBC improved primary forest and pasture classification accuracies. This research indicates that use of textural images or use of OBC are especially valuable for improving the vegetation classes such as upland and liana forest classes having complex stand structures and having relatively large patch sizes.

  12. Integration of heterogeneous features for remote sensing scene classification

    Science.gov (United States)

    Wang, Xin; Xiong, Xingnan; Ning, Chen; Shi, Aiye; Lv, Guofang

    2018-01-01

    Scene classification is one of the most important issues in remote sensing (RS) image processing. We find that features from different channels (shape, spectral, texture, etc.), levels (low-level and middle-level), or perspectives (local and global) could provide various properties for RS images, and then propose a heterogeneous feature framework to extract and integrate heterogeneous features with different types for RS scene classification. The proposed method is composed of three modules (1) heterogeneous features extraction, where three heterogeneous feature types, called DS-SURF-LLC, mean-Std-LLC, and MS-CLBP, are calculated, (2) heterogeneous features fusion, where the multiple kernel learning (MKL) is utilized to integrate the heterogeneous features, and (3) an MKL support vector machine classifier for RS scene classification. The proposed method is extensively evaluated on three challenging benchmark datasets (a 6-class dataset, a 12-class dataset, and a 21-class dataset), and the experimental results show that the proposed method leads to good classification performance. It produces good informative features to describe the RS image scenes. Moreover, the integration of heterogeneous features outperforms some state-of-the-art features on RS scene classification tasks.

  13. Spectral and correlation analysis of soft X-ray signals from the Joint European Torus tokamak

    International Nuclear Information System (INIS)

    Karlsson, J.; Pazsit, I.

    1997-01-01

    Tomographic methods applied to soft X-rays emitted from a fusion plasma have long been used to diagnose and interpret magnetohydrodynamic and other plasma activities. However, fluctuation analysis has recently been proposed as a complementary method to tomography. The novelty of the suggested method is that the various modes can be determined without tomographic inversion. This paper reports on the results of correlation and spectral analysis of soft X-ray data. The seven measurements analyzed were made by the Joint European Torus (JET) Joint Undertaking using their old soft X-ray measurement system. Auto power spectral densities and phase relations were evaluated from the measured signals as functions of the lines of sight. The fundamental mode m=n=1 was identified in several measurements. The corresponding frequency and toroidal rotation velocity were determined. Higher order modes were also observed and identified. Furthermore, simple model calculations were performed and the results compared with evaluated auto-spectra. (orig.)

  14. Use of UAV-Borne Spectrometer for Land Cover Classification

    Directory of Open Access Journals (Sweden)

    Sowmya Natesan

    2018-04-01

    Full Text Available Unmanned aerial vehicles (UAV are being used for low altitude remote sensing for thematic land classification using visible light and multi-spectral sensors. The objective of this work was to investigate the use of UAV equipped with a compact spectrometer for land cover classification. The UAV platform used was a DJI Flamewheel F550 hexacopter equipped with GPS and Inertial Measurement Unit (IMU navigation sensors, and a Raspberry Pi processor and camera module. The spectrometer used was the FLAME-NIR, a near-infrared spectrometer for hyperspectral measurements. RGB images and spectrometer data were captured simultaneously. As spectrometer data do not provide continuous terrain coverage, the locations of their ground elliptical footprints were determined from the bundle adjustment solution of the captured images. For each of the spectrometer ground ellipses, the land cover signature at the footprint location was determined to enable the characterization, identification, and classification of land cover elements. To attain a continuous land cover classification map, spatial interpolation was carried out from the irregularly distributed labeled spectrometer points. The accuracy of the classification was assessed using spatial intersection with the object-based image classification performed using the RGB images. Results show that in homogeneous land cover, like water, the accuracy of classification is 78% and in mixed classes, like grass, trees and manmade features, the average accuracy is 50%, thus, indicating the contribution of hyperspectral measurements of low altitude UAV-borne spectrometers to improve land cover classification.

  15. River floodplain vegetation classification using multi-temporal high-resolution colour infrared UAV imagery.

    NARCIS (Netherlands)

    van Iersel, W.K.; Straatsma, M.W.; Addink, E.A.; Middelkoop, H.

    2016-01-01

    To evaluate floodplain functioning, monitoring of its vegetation is essential. Although airborne imagery is widely applied for this purpose, classification accuracy (CA) remains low for grassland (< 88%) and herbaceous vegetation (<57%) due to the spectral and structural similarity of these

  16. A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification

    Science.gov (United States)

    Zhang, Ce; Pan, Xin; Li, Huapeng; Gardiner, Andy; Sargent, Isabel; Hare, Jonathon; Atkinson, Peter M.

    2018-06-01

    The contextual-based convolutional neural network (CNN) with deep architecture and pixel-based multilayer perceptron (MLP) with shallow structure are well-recognized neural network algorithms, representing the state-of-the-art deep learning method and the classical non-parametric machine learning approach, respectively. The two algorithms, which have very different behaviours, were integrated in a concise and effective way using a rule-based decision fusion approach for the classification of very fine spatial resolution (VFSR) remotely sensed imagery. The decision fusion rules, designed primarily based on the classification confidence of the CNN, reflect the generally complementary patterns of the individual classifiers. In consequence, the proposed ensemble classifier MLP-CNN harvests the complementary results acquired from the CNN based on deep spatial feature representation and from the MLP based on spectral discrimination. Meanwhile, limitations of the CNN due to the adoption of convolutional filters such as the uncertainty in object boundary partition and loss of useful fine spatial resolution detail were compensated. The effectiveness of the ensemble MLP-CNN classifier was tested in both urban and rural areas using aerial photography together with an additional satellite sensor dataset. The MLP-CNN classifier achieved promising performance, consistently outperforming the pixel-based MLP, spectral and textural-based MLP, and the contextual-based CNN in terms of classification accuracy. This research paves the way to effectively address the complicated problem of VFSR image classification.

  17. Urban Classification Techniques Using the Fusion of LiDAR and Spectral Data

    Science.gov (United States)

    2012-09-01

    37 D. MASK CREATION .......................................................................................39 viii 1. LiDAR-based Masks...in Quick Terrain Modeler 2. WorldView-2 The image used in this project was collected by WorldView-2 on November 8, 2011 at Zulu time 19:34:42...OBSERVATIONS A. PROCESS OVERVIEW The focus of this thesis was to create a robust technique for fusing LiDAR and spectral imagery for creation of a

  18. Archiving Spectral Libraries in the Planetary Data System

    Science.gov (United States)

    Slavney, S.; Guinness, E. A.; Scholes, D.; Zastrow, A.

    2017-12-01

    Spectral libraries are becoming popular candidates for archiving in PDS. With the increase in the number of individual investigators funded by programs such as NASA's PDART, the PDS Geosciences Node is receiving many requests for support from proposers wishing to archive various forms of laboratory spectra. To accommodate the need for a standardized approach to archiving spectra, the Geosciences Node has designed the PDS Spectral Library Data Dictionary, which contains PDS4 classes and attributes specifically for labeling spectral data, including a classification scheme for samples. The Reflectance Experiment Laboratory (RELAB) at Brown University, which has long been a provider of spectroscopy equipment and services to the science community, has provided expert input into the design of the dictionary. Together the Geosciences Node and RELAB are preparing the whole of the RELAB Spectral Library, consisting of many thousands of spectra collected over the years, to be archived in PDS. An online interface for searching, displaying, and downloading selected spectra is planned, using the Spectral Library metadata recorded in the PDS labels. The data dictionary and online interface will be extended to include spectral libraries submitted by other data providers. The Spectral Library Data Dictionary is now available from PDS at https://pds.nasa.gov/pds4/schema/released/. It can be used in PDS4 labels for reflectance spectra as well as for Raman, XRF, XRD, LIBS, and other types of spectra. Ancillary data such as images, chemistry, and abundance data are also supported. To help generate PDS4-compliant labels for spectra, the Geosciences Node provides a label generation program called MakeLabels (http://pds-geosciences.wustl.edu/tools/makelabels.html) which creates labels from a template, and which can be used for any kind of PDS4 label. For information, contact the Geosciences Node at geosci@wunder.wustl.edu.

  19. Knowledge Discovery in Spectral Data by Means of Complex Networks

    Directory of Open Access Journals (Sweden)

    Stefano Boccaletti

    2013-03-01

    Full Text Available In the last decade, complex networks have widely been applied to the study of many natural and man-made systems, and to the extraction of meaningful information from the interaction structures created by genes and proteins. Nevertheless, less attention has been devoted to metabonomics, due to the lack of a natural network representation of spectral data. Here we define a technique for reconstructing networks from spectral data sets, where nodes represent spectral bins, and pairs of them are connected when their intensities follow a pattern associated with a disease. The structural analysis of the resulting network can then be used to feed standard data-mining algorithms, for instance for the classification of new (unlabeled subjects. Furthermore, we show how the structure of the network is resilient to the presence of external additive noise, and how it can be used to extract relevant knowledge about the development of the disease.

  20. Classification of IRAS asteroids

    International Nuclear Information System (INIS)

    Tedesco, E.F.; Matson, D.L.; Veeder, G.J.

    1989-01-01

    Albedos and spectral reflectances are essential for classifying asteroids. For example, classes E, M and P are indistinguishable without albedo data. Colorometric data are available for about 1000 asteroids but, prior to IRAS, albedo data was available for only about 200. IRAS broke this bottleneck by providing albedo data on nearly 2000 asteroids. Hence, excepting absolute magnitudes, the albedo and size are now the most common asteroid physical parameters known. In this chapter the authors present the results of analyses of IRAS-derived asteroid albedos, discuss their application to asteroid classification, and mention several studies which might be done to exploit further this data set

  1. Image Classification Workflow Using Machine Learning Methods

    Science.gov (United States)

    Christoffersen, M. S.; Roser, M.; Valadez-Vergara, R.; Fernández-Vega, J. A.; Pierce, S. A.; Arora, R.

    2016-12-01

    Recent increases in the availability and quality of remote sensing datasets have fueled an increasing number of scientifically significant discoveries based on land use classification and land use change analysis. However, much of the software made to work with remote sensing data products, specifically multispectral images, is commercial and often prohibitively expensive. The free to use solutions that are currently available come bundled up as small parts of much larger programs that are very susceptible to bugs and difficult to install and configure. What is needed is a compact, easy to use set of tools to perform land use analysis on multispectral images. To address this need, we have developed software using the Python programming language with the sole function of land use classification and land use change analysis. We chose Python to develop our software because it is relatively readable, has a large body of relevant third party libraries such as GDAL and Spectral Python, and is free to install and use on Windows, Linux, and Macintosh operating systems. In order to test our classification software, we performed a K-means unsupervised classification, Gaussian Maximum Likelihood supervised classification, and a Mahalanobis Distance based supervised classification. The images used for testing were three Landsat rasters of Austin, Texas with a spatial resolution of 60 meters for the years of 1984 and 1999, and 30 meters for the year 2015. The testing dataset was easily downloaded using the Earth Explorer application produced by the USGS. The software should be able to perform classification based on any set of multispectral rasters with little to no modification. Our software makes the ease of land use classification using commercial software available without an expensive license.

  2. CLASSIFICATION OF CROPLANDS THROUGH FUSION OF OPTICAL AND SAR TIME SERIES DATA

    Directory of Open Access Journals (Sweden)

    S. Park

    2016-06-01

    Full Text Available Many satellite sensors including Landsat series have been extensively used for land cover classification. Studies have been conducted to mitigate classification problems associated with the use of single data (e.g., such as cloud contamination through multi-sensor data fusion and the use of time series data. This study investigated two areas with different environment and climate conditions: one in South Korea and the other in US. Cropland classification was conducted by using multi-temporal Landsat 5, Radarsat-1 and digital elevation models (DEM based on two machine learning approaches (i.e., random forest and support vector machines. Seven classification scenarios were examined and evaluated through accuracy assessment. Results show that SVM produced the best performance (overall accuracy of 93.87% when using all temporal and spectral data as input variables. Normalized Difference Water Index (NDWI, SAR backscattering, and Normalized Difference Vegetation Index (NDVI were identified as more contributing variables than the others for cropland classification.

  3. ANALYZING SPECTRAL CHARACTERISTICS OF SHADOW AREA FROM ADS-40 HIGH RADIOMETRIC RESOLUTION AERIAL IMAGES

    Directory of Open Access Journals (Sweden)

    Y.-T. Hsieh

    2016-06-01

    Full Text Available The shadows in optical remote sensing images are regarded as image nuisances in numerous applications. The classification and interpretation of shadow area in a remote sensing image are a challenge, because of the reduction or total loss of spectral information in those areas. In recent years, airborne multispectral aerial image devices have been developed 12-bit or higher radiometric resolution data, including Leica ADS-40, Intergraph DMC. The increased radiometric resolution of digital imagery provides more radiometric details of potential use in classification or interpretation of land cover of shadow areas. Therefore, the objectives of this study are to analyze the spectral properties of the land cover in the shadow areas by ADS-40 high radiometric resolution aerial images, and to investigate the spectral and vegetation index differences between the various shadow and non-shadow land covers. According to research findings of spectral analysis of ADS-40 image: (i The DN values in shadow area are much lower than in nonshadow area; (ii DN values received from shadowed areas that will also be affected by different land cover, and it shows the possibility of land cover property retrieval as in nonshadow area; (iii The DN values received from shadowed regions decrease in the visible band from short to long wavelengths due to scattering; (iv The shadow area NIR of vegetation category also shows a strong reflection; (v Generally, vegetation indexes (NDVI still have utility to classify the vegetation and non-vegetation in shadow area. The spectral data of high radiometric resolution images (ADS-40 is potential for the extract land cover information of shadow areas.

  4. REVISITING THE LONG/SOFT-SHORT/HARD CLASSIFICATION OF GAMMA-RAY BURSTS IN THE FERMI ERA

    International Nuclear Information System (INIS)

    Zhang Fuwen; Yan Jingzhi; Wei Daming; Shao Lang

    2012-01-01

    We perform a statistical analysis of the temporal and spectral properties of the latest Fermi gamma-ray bursts (GRBs) to revisit the classification of GRBs. We find that the bimodalities of duration and the energy ratio (E peak /Fluence) and the anti-correlation between spectral hardness (hardness ratio (HR), peak energy, and spectral index) and duration (T 90 ) support the long/soft-short/hard classification scheme for Fermi GRBs. The HR-T 90 anti-correlation strongly depends on the spectral shape of GRBs and energy bands, and the bursts with the curved spectra in the typical BATSE energy bands show a tighter anti-correlation than those with the power-law spectra in the typical BAT energy bands. This might explain why the HR-T 90 correlation is not evident for those GRB samples detected by instruments like Swift with a narrower/softer energy bandpass. We also analyze the intrinsic energy correlation for the GRBs with measured redshifts and well-defined peak energies. The current sample suggests E p,rest = 2455 × (E iso /10 52 ) 0.59 for short GRBs, significantly different from that for long GRBs. However, both the long and short GRBs comply with the same E p,rest -L iso correlation.

  5. Nonparametric Collective Spectral Density Estimation and Clustering

    KAUST Repository

    Maadooliat, Mehdi; Sun, Ying; Chen, Tianbo

    2017-01-01

    In this paper, we develop a method for the simultaneous estimation of spectral density functions (SDFs) for a collection of stationary time series that share some common features. Due to the similarities among the SDFs, the log-SDF can be represented using a common set of basis functions. The basis shared by the collection of the log-SDFs is estimated as a low-dimensional manifold of a large space spanned by a pre-specified rich basis. A collective estimation approach pools information and borrows strength across the SDFs to achieve better estimation efficiency. Also, each estimated spectral density has a concise representation using the coefficients of the basis expansion, and these coefficients can be used for visualization, clustering, and classification purposes. The Whittle pseudo-maximum likelihood approach is used to fit the model and an alternating blockwise Newton-type algorithm is developed for the computation. A web-based shiny App found at

  6. Nonparametric Collective Spectral Density Estimation and Clustering

    KAUST Repository

    Maadooliat, Mehdi

    2017-04-12

    In this paper, we develop a method for the simultaneous estimation of spectral density functions (SDFs) for a collection of stationary time series that share some common features. Due to the similarities among the SDFs, the log-SDF can be represented using a common set of basis functions. The basis shared by the collection of the log-SDFs is estimated as a low-dimensional manifold of a large space spanned by a pre-specified rich basis. A collective estimation approach pools information and borrows strength across the SDFs to achieve better estimation efficiency. Also, each estimated spectral density has a concise representation using the coefficients of the basis expansion, and these coefficients can be used for visualization, clustering, and classification purposes. The Whittle pseudo-maximum likelihood approach is used to fit the model and an alternating blockwise Newton-type algorithm is developed for the computation. A web-based shiny App found at

  7. Co-founding ant queens prevent disease by performing prophylactic undertaking behaviour.

    Science.gov (United States)

    Pull, Christopher D; Cremer, Sylvia

    2017-10-13

    Social insects form densely crowded societies in environments with high pathogen loads, but have evolved collective defences that mitigate the impact of disease. However, colony-founding queens lack this protection and suffer high rates of mortality. The impact of pathogens may be exacerbated in species where queens found colonies together, as healthy individuals may contract pathogens from infectious co-founders. Therefore, we tested whether ant queens avoid founding colonies with pathogen-exposed conspecifics and how they might limit disease transmission from infectious individuals. Using Lasius niger queens and a naturally infecting fungal pathogen Metarhizium brunneum, we observed that queens were equally likely to found colonies with another pathogen-exposed or sham-treated queen. However, when one queen died, the surviving individual performed biting, burial and removal of the corpse. These undertaking behaviours were performed prophylactically, i.e. targeted equally towards non-infected and infected corpses, as well as carried out before infected corpses became infectious. Biting and burial reduced the risk of the queens contracting and dying from disease from an infectious corpse of a dead co-foundress. We show that co-founding ant queens express undertaking behaviours that, in mature colonies, are performed exclusively by workers. Such infection avoidance behaviours act before the queens can contract the disease and will therefore improve the overall chance of colony founding success in ant queens.

  8. Classification of O Stars in the Yellow-Green: The Exciting Star VES 735

    Science.gov (United States)

    Kerton, C. R.; Ballantyne, D. R.; Martin, P. G.

    1999-05-01

    Acquiring data for spectral classification of heavily reddened stars using traditional criteria in the blue-violet region of the spectrum can be prohibitively time consuming using small to medium sized telescopes. One such star is the Vatican Observatory emission-line star VES 735, which we have found excites the H II region KR 140. In order to classify VES 735, we have constructed an atlas of stellar spectra of O stars in the yellow-green (4800-5420 Å). We calibrate spectral type versus the line ratio He I lambda4922:He II lambda5411, showing that this ratio should be useful for the classification of heavily reddened O stars associated with H II regions. Application to VES 735 shows that the spectral type is O8.5. The absolute magnitude suggests luminosity class V. Comparison of the rate of emission of ionizing photons and the bolometric luminosity of VES 735, inferred from radio and infrared measurements of the KR 140 region, to recent stellar models gives consistent evidence for a main-sequence star of mass 25 M_solar and age less than a few million years with a covering factor 0.4-0.5 by the nebular material. Spectra taken in the red (6500-6700 Å) show that the stellar Hα emission is double-peaked about the systemic velocity and slightly variable. Hβ is in absorption, so that the emission-line classification is ``(e)''. However, unlike the case of the more well-known O(e) star zeta Oph, the emission from VES 735 appears to be long-lived rather than episodic.

  9. Using spectrotemporal indices to improve the fruit-tree crop classification accuracy

    Science.gov (United States)

    Peña, M. A.; Liao, R.; Brenning, A.

    2017-06-01

    This study assesses the potential of spectrotemporal indices derived from satellite image time series (SITS) to improve the classification accuracy of fruit-tree crops. Six major fruit-tree crop types in the Aconcagua Valley, Chile, were classified by applying various linear discriminant analysis (LDA) techniques on a Landsat-8 time series of nine images corresponding to the 2014-15 growing season. As features we not only used the complete spectral resolution of the SITS, but also all possible normalized difference indices (NDIs) that can be constructed from any two bands of the time series, a novel approach to derive features from SITS. Due to the high dimensionality of this "enhanced" feature set we used the lasso and ridge penalized variants of LDA (PLDA). Although classification accuracies yielded by the standard LDA applied on the full-band SITS were good (misclassification error rate, MER = 0.13), they were further improved by 23% (MER = 0.10) with ridge PLDA using the enhanced feature set. The most important bands to discriminate the crops of interest were mainly concentrated on the first two image dates of the time series, corresponding to the crops' greenup stage. Despite the high predictor weights provided by the red and near infrared bands, typically used to construct greenness spectral indices, other spectral regions were also found important for the discrimination, such as the shortwave infrared band at 2.11-2.19 μm, sensitive to foliar water changes. These findings support the usefulness of spectrotemporal indices in the context of SITS-based crop type classifications, which until now have been mainly constructed by the arithmetic combination of two bands of the same image date in order to derive greenness temporal profiles like those from the normalized difference vegetation index.

  10. Assisted reproductive technologies in Ghana : Transnational undertakings, local practices and ‘more affordable’ IVF

    NARCIS (Netherlands)

    Gerrits, T.

    The article sketches the origins and development of IVF in Ghana as a highly transnational undertaking. Movements are from and to Africa, involving human beings (providers and users), and also refer to other entities such as technologies, skills and knowledge. None of these movements are paid for

  11. [Cotton identification and extraction using near infrared sensor and object-oriented spectral segmentation technique].

    Science.gov (United States)

    Deng, Jin-Song; Shi, Yuan-Yuan; Chen, Li-Su; Wang, Ke; Zhu, Jin-Xia

    2009-07-01

    The real-time, effective and reliable method of identifying crop is the foundation of scientific management for crop in the precision agriculture. It is also one of the key techniques for the precision agriculture. However, this expectation cannot be fulfilled by the traditional pixel-based information extraction method with respect to complicated image processing and accurate objective identification. In the present study, visible-near infrared image of cotton was acquired using high-resolution sensor. Object-oriented segmentation technique was performed on the image to produce image objects and spatial/spectral features of cotton. Afterwards, nearest neighbor classifier integrated the spectral, shape and topologic information of image objects to precisely identify cotton according to various features. Finally, 300 random samples and an error matrix were applied to undertake the accuracy assessment of identification. Although errors and confusion exist, this method shows satisfying results with an overall accuracy of 96.33% and a KAPPA coefficient of 0.926 7, which can meet the demand of automatic management and decision-making in precision agriculture.

  12. A Closer Look at Deep Learning Neural Networks with Low-level Spectral Periodicity Features

    DEFF Research Database (Denmark)

    Sturm, Bob L.; Kereliuk, Corey; Pikrakis, Aggelos

    2014-01-01

    Systems built using deep learning neural networks trained on low-level spectral periodicity features (DeSPerF) reproduced the most “ground truth” of the systems submitted to the MIREX 2013 task, “Audio Latin Genre Classification.” To answer why this was the case, we take a closer look...

  13. Accurate crop classification using hierarchical genetic fuzzy rule-based systems

    Science.gov (United States)

    Topaloglou, Charalampos A.; Mylonas, Stelios K.; Stavrakoudis, Dimitris G.; Mastorocostas, Paris A.; Theocharis, John B.

    2014-10-01

    This paper investigates the effectiveness of an advanced classification system for accurate crop classification using very high resolution (VHR) satellite imagery. Specifically, a recently proposed genetic fuzzy rule-based classification system (GFRBCS) is employed, namely, the Hierarchical Rule-based Linguistic Classifier (HiRLiC). HiRLiC's model comprises a small set of simple IF-THEN fuzzy rules, easily interpretable by humans. One of its most important attributes is that its learning algorithm requires minimum user interaction, since the most important learning parameters affecting the classification accuracy are determined by the learning algorithm automatically. HiRLiC is applied in a challenging crop classification task, using a SPOT5 satellite image over an intensively cultivated area in a lake-wetland ecosystem in northern Greece. A rich set of higher-order spectral and textural features is derived from the initial bands of the (pan-sharpened) image, resulting in an input space comprising 119 features. The experimental analysis proves that HiRLiC compares favorably to other interpretable classifiers of the literature, both in terms of structural complexity and classification accuracy. Its testing accuracy was very close to that obtained by complex state-of-the-art classification systems, such as the support vector machines (SVM) and random forest (RF) classifiers. Nevertheless, visual inspection of the derived classification maps shows that HiRLiC is characterized by higher generalization properties, providing more homogeneous classifications that the competitors. Moreover, the runtime requirements for producing the thematic map was orders of magnitude lower than the respective for the competitors.

  14. Spectral gamuts and spectral gamut mapping

    Science.gov (United States)

    Rosen, Mitchell R.; Derhak, Maxim W.

    2006-01-01

    All imaging devices have two gamuts: the stimulus gamut and the response gamut. The response gamut of a print engine is typically described in CIE colorimetry units, a system derived to quantify human color response. More fundamental than colorimetric gamuts are spectral gamuts, based on radiance, reflectance or transmittance units. Spectral gamuts depend on the physics of light or on how materials interact with light and do not involve the human's photoreceptor integration or brain processing. Methods for visualizing a spectral gamut raise challenges as do considerations of how to utilize such a data-set for producing superior color reproductions. Recent work has described a transformation of spectra reduced to 6-dimensions called LabPQR. LabPQR was designed as a hybrid space with three explicit colorimetric axes and three additional spectral reconstruction axes. In this paper spectral gamuts are discussed making use of LabPQR. Also, spectral gamut mapping is considered in light of the colorimetric-spectral duality of the LabPQR space.

  15. Quantitative method to assess caries via fluorescence imaging from the perspective of autofluorescence spectral analysis

    Science.gov (United States)

    Chen, Q. G.; Zhu, H. H.; Xu, Y.; Lin, B.; Chen, H.

    2015-08-01

    A quantitative method to discriminate caries lesions for a fluorescence imaging system is proposed in this paper. The autofluorescence spectral investigation of 39 teeth samples classified by the International Caries Detection and Assessment System levels was performed at 405 nm excitation. The major differences in the different caries lesions focused on the relative spectral intensity range of 565-750 nm. The spectral parameter, defined as the ratio of wavebands at 565-750 nm to the whole spectral range, was calculated. The image component ratio R/(G + B) of color components was statistically computed by considering the spectral parameters (e.g. autofluorescence, optical filter, and spectral sensitivity) in our fluorescence color imaging system. Results showed that the spectral parameter and image component ratio presented a linear relation. Therefore, the image component ratio was graded as 1.62 to quantitatively classify sound, early decay, established decay, and severe decay tissues, respectively. Finally, the fluorescence images of caries were experimentally obtained, and the corresponding image component ratio distribution was compared with the classification result. A method to determine the numerical grades of caries using a fluorescence imaging system was proposed. This method can be applied to similar imaging systems.

  16. Random Forest Classification of Wetland Landcovers from Multi-Sensor Data in the Arid Region of Xinjiang, China

    Directory of Open Access Journals (Sweden)

    Shaohong Tian

    2016-11-01

    Full Text Available The wetland classification from remotely sensed data is usually difficult due to the extensive seasonal vegetation dynamics and hydrological fluctuation. This study presents a random forest classification approach for the retrieval of the wetland landcover in the arid regions by fusing the Pléiade-1B data with multi-date Landsat-8 data. The segmentation of the Pléiade-1B multispectral image data was performed based on an object-oriented approach, and the geometric and spectral features were extracted for the segmented image objects. The normalized difference vegetation index (NDVI series data were also calculated from the multi-date Landsat-8 data, reflecting vegetation phenological changes in its growth cycle. The feature set extracted from the two sensors data was optimized and employed to create the random forest model for the classification of the wetland landcovers in the Ertix River in northern Xinjiang, China. Comparison with other classification methods such as support vector machine and artificial neural network classifiers indicates that the random forest classifier can achieve accurate classification with an overall accuracy of 93% and the Kappa coefficient of 0.92. The classification accuracy of the farming lands and water bodies that have distinct boundaries with the surrounding land covers was improved 5%–10% by making use of the property of geometric shapes. To remove the difficulty in the classification that was caused by the similar spectral features of the vegetation covers, the phenological difference and the textural information of co-occurrence gray matrix were incorporated into the classification, and the main wetland vegetation covers in the study area were derived from the two sensors data. The inclusion of phenological information in the classification enables the classification errors being reduced down, and the overall accuracy was improved approximately 10%. The results show that the proposed random forest

  17. Alexnet Feature Extraction and Multi-Kernel Learning for Objectoriented Classification

    Science.gov (United States)

    Ding, L.; Li, H.; Hu, C.; Zhang, W.; Wang, S.

    2018-04-01

    In view of the fact that the deep convolutional neural network has stronger ability of feature learning and feature expression, an exploratory research is done on feature extraction and classification for high resolution remote sensing images. Taking the Google image with 0.3 meter spatial resolution in Ludian area of Yunnan Province as an example, the image segmentation object was taken as the basic unit, and the pre-trained AlexNet deep convolution neural network model was used for feature extraction. And the spectral features, AlexNet features and GLCM texture features are combined with multi-kernel learning and SVM classifier, finally the classification results were compared and analyzed. The results show that the deep convolution neural network can extract more accurate remote sensing image features, and significantly improve the overall accuracy of classification, and provide a reference value for earthquake disaster investigation and remote sensing disaster evaluation.

  18. ALEXNET FEATURE EXTRACTION AND MULTI-KERNEL LEARNING FOR OBJECTORIENTED CLASSIFICATION

    Directory of Open Access Journals (Sweden)

    L. Ding

    2018-04-01

    Full Text Available In view of the fact that the deep convolutional neural network has stronger ability of feature learning and feature expression, an exploratory research is done on feature extraction and classification for high resolution remote sensing images. Taking the Google image with 0.3 meter spatial resolution in Ludian area of Yunnan Province as an example, the image segmentation object was taken as the basic unit, and the pre-trained AlexNet deep convolution neural network model was used for feature extraction. And the spectral features, AlexNet features and GLCM texture features are combined with multi-kernel learning and SVM classifier, finally the classification results were compared and analyzed. The results show that the deep convolution neural network can extract more accurate remote sensing image features, and significantly improve the overall accuracy of classification, and provide a reference value for earthquake disaster investigation and remote sensing disaster evaluation.

  19. Evaluation of Spectral and Prosodic Features of Speech Affected by Orthodontic Appliances Using the Gmm Classifier

    Science.gov (United States)

    Přibil, Jiří; Přibilová, Anna; Ďuračkoá, Daniela

    2014-01-01

    The paper describes our experiment with using the Gaussian mixture models (GMM) for classification of speech uttered by a person wearing orthodontic appliances. For the GMM classification, the input feature vectors comprise the basic and the complementary spectral properties as well as the supra-segmental parameters. Dependence of classification correctness on the number of the parameters in the input feature vector and on the computation complexity is also evaluated. In addition, an influence of the initial setting of the parameters for GMM training process was analyzed. Obtained recognition results are compared visually in the form of graphs as well as numerically in the form of tables and confusion matrices for tested sentences uttered using three configurations of orthodontic appliances.

  20. Efficient spectral estimation by MUSIC and ESPRIT with application to sparse FFT

    Directory of Open Access Journals (Sweden)

    Daniel ePotts

    2016-02-01

    Full Text Available In spectral estimation, one has to determine all parameters of an exponential sum for finitely many (noisysampled data of this exponential sum.Frequently used methods for spectral estimation are MUSIC (MUltiple SIgnal Classification and ESPRIT (Estimation of Signal Parameters viaRotational Invariance Technique.For a trigonometric polynomial of large sparsity, we present a new sparse fast Fourier transform byshifted sampling and using MUSIC resp. ESPRIT, where the ESPRIT based method has lower computational cost.Later this technique is extended to a new reconstruction of a multivariate trigonometric polynomial of large sparsity for given (noisy values sampled on a reconstructing rank-1 lattice. Numerical experiments illustrate thehigh performance of these procedures.

  1. Assessment of damage in composite laminates through dynamic, full-spectral interrogation of fiber Bragg grating sensors

    International Nuclear Information System (INIS)

    Propst, A; Peters, K; Zikry, M A; Schultz, S; Kunzler, W; Zhu, Z; Wirthlin, M; Selfridge, R

    2010-01-01

    In this study, we demonstrate the full-spectral interrogation of a fiber Bragg grating (FBG) sensor at 535 Hz. The sensor is embedded in a woven, graphite fiber–epoxy composite laminate subjected to multiple low-velocity impacts. The measurement of unique, time dependent spectral features from the FBG sensor permits classification of the laminate lifetime into five regimes. These damage regimes compare well with previous analysis of the same material system using combined global and local FBG sensor information. Observed transient spectral features include peak splitting, wide spectral broadening and a strong single peak at the end of the impact event. Such features could not be measured through peak wavelength interrogation of the FBG sensor. Cross-correlation of the measured spectra with the original embedded FBG spectrum permitted rapid visualization of average strains and the presence of transverse compressive strain on the optical fiber, but smeared out the details of the spectral profile

  2. Evolving spectral transformations for multitemporal information extraction using evolutionary computation

    Science.gov (United States)

    Momm, Henrique; Easson, Greg

    2011-01-01

    Remote sensing plays an important role in assessing temporal changes in land features. The challenge often resides in the conversion of large quantities of raw data into actionable information in a timely and cost-effective fashion. To address this issue, research was undertaken to develop an innovative methodology integrating biologically-inspired algorithms with standard image classification algorithms to improve information extraction from multitemporal imagery. Genetic programming was used as the optimization engine to evolve feature-specific candidate solutions in the form of nonlinear mathematical expressions of the image spectral channels (spectral indices). The temporal generalization capability of the proposed system was evaluated by addressing the task of building rooftop identification from a set of images acquired at different dates in a cross-validation approach. The proposed system generates robust solutions (kappa values > 0.75 for stage 1 and > 0.4 for stage 2) despite the statistical differences between the scenes caused by land use and land cover changes coupled with variable environmental conditions, and the lack of radiometric calibration between images. Based on our results, the use of nonlinear spectral indices enhanced the spectral differences between features improving the clustering capability of standard classifiers and providing an alternative solution for multitemporal information extraction.

  3. Multispectral LiDAR Data for Land Cover Classification of Urban Areas

    Directory of Open Access Journals (Sweden)

    Salem Morsy

    2017-04-01

    Full Text Available Airborne Light Detection And Ranging (LiDAR systems usually operate at a monochromatic wavelength measuring the range and the strength of the reflected energy (intensity from objects. Recently, multispectral LiDAR sensors, which acquire data at different wavelengths, have emerged. This allows for recording of a diversity of spectral reflectance from objects. In this context, we aim to investigate the use of multispectral LiDAR data in land cover classification using two different techniques. The first is image-based classification, where intensity and height images are created from LiDAR points and then a maximum likelihood classifier is applied. The second is point-based classification, where ground filtering and Normalized Difference Vegetation Indices (NDVIs computation are conducted. A dataset of an urban area located in Oshawa, Ontario, Canada, is classified into four classes: buildings, trees, roads and grass. An overall accuracy of up to 89.9% and 92.7% is achieved from image classification and 3D point classification, respectively. A radiometric correction model is also applied to the intensity data in order to remove the attenuation due to the system distortion and terrain height variation. The classification process is then repeated, and the results demonstrate that there are no significant improvements achieved in the overall accuracy.

  4. Multispectral LiDAR Data for Land Cover Classification of Urban Areas.

    Science.gov (United States)

    Morsy, Salem; Shaker, Ahmed; El-Rabbany, Ahmed

    2017-04-26

    Airborne Light Detection And Ranging (LiDAR) systems usually operate at a monochromatic wavelength measuring the range and the strength of the reflected energy (intensity) from objects. Recently, multispectral LiDAR sensors, which acquire data at different wavelengths, have emerged. This allows for recording of a diversity of spectral reflectance from objects. In this context, we aim to investigate the use of multispectral LiDAR data in land cover classification using two different techniques. The first is image-based classification, where intensity and height images are created from LiDAR points and then a maximum likelihood classifier is applied. The second is point-based classification, where ground filtering and Normalized Difference Vegetation Indices (NDVIs) computation are conducted. A dataset of an urban area located in Oshawa, Ontario, Canada, is classified into four classes: buildings, trees, roads and grass. An overall accuracy of up to 89.9% and 92.7% is achieved from image classification and 3D point classification, respectively. A radiometric correction model is also applied to the intensity data in order to remove the attenuation due to the system distortion and terrain height variation. The classification process is then repeated, and the results demonstrate that there are no significant improvements achieved in the overall accuracy.

  5. An undertaking planning game for the electricity supply industry

    International Nuclear Information System (INIS)

    Troescher, H.

    1977-01-01

    Planning games have been found satisfactory in many field in political and economic life. In particular the more convenient access to electronic calculators has made a contrinution to their wider use. It is therefore surprising that the first planning game which has become known for the electricity supply industry was first published in the year 1975. This is the planning game for the Bernischen Kraftwerke AG, which is based on a simplified model of a small electricity supply undertaking (EVU). This planning game was adapted in the RWE to the conditions in larger EVU and a few additional model components were added. Besides the general points of view on planning games for EVU the author deals with the extended planning game which is termed in the article PEW. (orig.) [de

  6. T-ray relevant frequencies for osteosarcoma classification

    Science.gov (United States)

    Withayachumnankul, W.; Ferguson, B.; Rainsford, T.; Findlay, D.; Mickan, S. P.; Abbott, D.

    2006-01-01

    We investigate the classification of the T-ray response of normal human bone cells and human osteosarcoma cells, grown in culture. Given the magnitude and phase responses within a reliable spectral range as features for input vectors, a trained support vector machine can correctly classify the two cell types to some extent. Performance of the support vector machine is deteriorated by the curse of dimensionality, resulting from the comparatively large number of features in the input vectors. Feature subset selection methods are used to select only an optimal number of relevant features for inputs. As a result, an improvement in generalization performance is attainable, and the selected frequencies can be used for further describing different mechanisms of the cells, responding to T-rays. We demonstrate a consistent classification accuracy of 89.6%, while the only one fifth of the original features are retained in the data set.

  7. Spectral autofluorescence imaging of the retina for drusen detection

    Science.gov (United States)

    Foubister, James J.; Gorman, Alistair; Harvey, Andy; Hemert, Jano van

    2018-02-01

    The presence and characteristics of drusen in retinal images, namely their size, location, and distribution, can be used to aid in the diagnosis and monitoring of Age Related Macular Degeneration (AMD); one of the leading causes for blindness in the elderly population. Current imaging techniques are effective at determining the presence and number of drusen, but fail when it comes to classifying their size and form. These distinctions are important for correctly characterising the disease, especially in the early stages where the development of just one larger drusen can indicate progression. Another challenge for automated detection is in distinguishing them from other retinal features, such as cotton wool spots. We describe the development of a multi-spectral scanning-laser ophthalmoscope that records images of retinal autofluorescence (AF) in four spectral bands. This will offer the potential to detect drusen with improved contrast based on spectral discrimination for automated classification. The resulting improved specificity and sensitivity for their detection offers more reliable characterisation of AMD. We present proof of principle images prior to further system optimisation and clinical trials for assessment of enhanced detection of drusen.

  8. Tree Classification with Fused Mobile Laser Scanning and Hyperspectral Data

    Science.gov (United States)

    Puttonen, Eetu; Jaakkola, Anttoni; Litkey, Paula; Hyyppä, Juha

    2011-01-01

    Mobile Laser Scanning data were collected simultaneously with hyperspectral data using the Finnish Geodetic Institute Sensei system. The data were tested for tree species classification. The test area was an urban garden in the City of Espoo, Finland. Point clouds representing 168 individual tree specimens of 23 tree species were determined manually. The classification of the trees was done using first only the spatial data from point clouds, then with only the spectral data obtained with a spectrometer, and finally with the combined spatial and hyperspectral data from both sensors. Two classification tests were performed: the separation of coniferous and deciduous trees, and the identification of individual tree species. All determined tree specimens were used in distinguishing coniferous and deciduous trees. A subset of 133 trees and 10 tree species was used in the tree species classification. The best classification results for the fused data were 95.8% for the separation of the coniferous and deciduous classes. The best overall tree species classification succeeded with 83.5% accuracy for the best tested fused data feature combination. The respective results for paired structural features derived from the laser point cloud were 90.5% for the separation of the coniferous and deciduous classes and 65.4% for the species classification. Classification accuracies with paired hyperspectral reflectance value data were 90.5% for the separation of coniferous and deciduous classes and 62.4% for different species. The results are among the first of their kind and they show that mobile collected fused data outperformed single-sensor data in both classification tests and by a significant margin. PMID:22163894

  9. Application of In-Segment Multiple Sampling in Object-Based Classification

    Directory of Open Access Journals (Sweden)

    Nataša Đurić

    2014-12-01

    Full Text Available When object-based analysis is applied to very high-resolution imagery, pixels within the segments reveal large spectral inhomogeneity; their distribution can be considered complex rather than normal. When normality is violated, the classification methods that rely on the assumption of normally distributed data are not as successful or accurate. It is hard to detect normality violations in small samples. The segmentation process produces segments that vary highly in size; samples can be very big or very small. This paper investigates whether the complexity within the segment can be addressed using multiple random sampling of segment pixels and multiple calculations of similarity measures. In order to analyze the effect sampling has on classification results, statistics and probability value equations of non-parametric two-sample Kolmogorov-Smirnov test and parametric Student’s t-test are selected as similarity measures in the classification process. The performance of both classifiers was assessed on a WorldView-2 image for four land cover classes (roads, buildings, grass and trees and compared to two commonly used object-based classifiers—k-Nearest Neighbor (k-NN and Support Vector Machine (SVM. Both proposed classifiers showed a slight improvement in the overall classification accuracies and produced more accurate classification maps when compared to the ground truth image.

  10. Lidar-based individual tree species classification using convolutional neural network

    Science.gov (United States)

    Mizoguchi, Tomohiro; Ishii, Akira; Nakamura, Hiroyuki; Inoue, Tsuyoshi; Takamatsu, Hisashi

    2017-06-01

    Terrestrial lidar is commonly used for detailed documentation in the field of forest inventory investigation. Recent improvements of point cloud processing techniques enabled efficient and precise computation of an individual tree shape parameters, such as breast-height diameter, height, and volume. However, tree species are manually specified by skilled workers to date. Previous works for automatic tree species classification mainly focused on aerial or satellite images, and few works have been reported for classification techniques using ground-based sensor data. Several candidate sensors can be considered for classification, such as RGB or multi/hyper spectral cameras. Above all candidates, we use terrestrial lidar because it can obtain high resolution point cloud in the dark forest. We selected bark texture for the classification criteria, since they clearly represent unique characteristics of each tree and do not change their appearance under seasonable variation and aged deterioration. In this paper, we propose a new method for automatic individual tree species classification based on terrestrial lidar using Convolutional Neural Network (CNN). The key component is the creation step of a depth image which well describe the characteristics of each species from a point cloud. We focus on Japanese cedar and cypress which cover the large part of domestic forest. Our experimental results demonstrate the effectiveness of our proposed method.

  11. Hybrid image classification technique for land-cover mapping in the Arctic tundra, North Slope, Alaska

    Science.gov (United States)

    Chaudhuri, Debasish

    Remotely sensed image classification techniques are very useful to understand vegetation patterns and species combination in the vast and mostly inaccessible arctic region. Previous researches that were done for mapping of land cover and vegetation in the remote areas of northern Alaska have considerably low accuracies compared to other biomes. The unique arctic tundra environment with short growing season length, cloud cover, low sun angles, snow and ice cover hinders the effectiveness of remote sensing studies. The majority of image classification research done in this area as reported in the literature used traditional unsupervised clustering technique with Landsat MSS data. It was also emphasized by previous researchers that SPOT/HRV-XS data lacked the spectral resolution to identify the small arctic tundra vegetation parcels. Thus, there is a motivation and research need to apply a new classification technique to develop an updated, detailed and accurate vegetation map at a higher spatial resolution i.e. SPOT-5 data. Traditional classification techniques in remotely sensed image interpretation are based on spectral reflectance values with an assumption of the training data being normally distributed. Hence it is difficult to add ancillary data in classification procedures to improve accuracy. The purpose of this dissertation was to develop a hybrid image classification approach that effectively integrates ancillary information into the classification process and combines ISODATA clustering, rule-based classifier and the Multilayer Perceptron (MLP) classifier which uses artificial neural network (ANN). The main goal was to find out the best possible combination or sequence of classifiers for typically classifying tundra type vegetation that yields higher accuracy than the existing classified vegetation map from SPOT data. Unsupervised ISODATA clustering and rule-based classification techniques were combined to produce an intermediate classified map which was

  12. Classification of tree species based on longwave hyperspectral data from leaves, a case study for a tropical dry forest

    Science.gov (United States)

    Harrison, D.; Rivard, B.; Sánchez-Azofeifa, A.

    2018-04-01

    Remote sensing of the environment has utilized the visible, near and short-wave infrared (IR) regions of the electromagnetic (EM) spectrum to characterize vegetation health, vigor and distribution. However, relatively little research has focused on the use of the longwave infrared (LWIR, 8.0-12.5 μm) region for studies of vegetation. In this study LWIR leaf reflectance spectra were collected in the wet seasons (May through December) of 2013 and 2014 from twenty-six tree species located in a high species diversity environment, a tropical dry forest in Costa Rica. A continuous wavelet transformation (CWT) was applied to all spectra to minimize noise and broad amplitude variations attributable to non-compositional effects. Species discrimination was then explored with Random Forest classification and accuracy improved was observed with preprocessing of reflectance spectra with continuous wavelet transformation. Species were found to share common spectral features that formed the basis for five spectral types that were corroborated with linear discriminate analysis. The source of most of the observed spectral features is attributed to cell wall or cuticle compounds (cellulose, cutin, matrix glycan, silica and oleanolic acid). Spectral types could be advantageous for the analysis of airborne hyperspectral data because cavity effects will lower the spectral contrast thus increasing the reliance of classification efforts on dominant spectral features. Spectral types specifically derived from leaf level data are expected to support the labeling of spectral classes derived from imagery. The results of this study and that of Ribeiro Da Luz (2006), Ribeiro Da Luz and Crowley (2007, 2010), Ullah et al. (2012) and Rock et al. (2016) have now illustrated success in tree species discrimination across a range of ecosystems using leaf-level spectral observations. With advances in LWIR sensors and concurrent improvements in their signal to noise, applications to large-scale species

  13. Potential of Spectral Reflectance as Postharvest Classification Tool for Flower Development of Calla Lily (Zantedeschia aethiopica (L. Spreng. Potencial de la Reflectancia Espectral como Herramienta para la Clasificación Poscosecha del Desarrollo Floral en Cala (Zantedeschia aethiopica (L. Spreng.

    Directory of Open Access Journals (Sweden)

    Antonio J . Steidle Neto

    2009-12-01

    Full Text Available Unsuitable postharvest management is one of the most serious problems that floriculture has to face. An option for reducing postharvest losses is to use automatic systems for flower sorting and classification, which yield consistent results, reduce costs and speed up these tasks. The objective of this work was show the potential of spectral reflectance to distinguish different postharvest development stages of calla lily flowers, Zantedeschia aethiopica (L. Spreng., aiming the use of this technology within automatic systems for flower classification. The measuring equipment was a spectrometer connected to a portable computer and configured for reflectance data acquisition in the 400 to 1000 nm range. Based on the results, it was verified a differentiation between the spectral reflectance curves of calla lily flowers, with gradual decreases on the measured values according to the increase of the senescence stages. Thus, the spectral reflectance has potential to be used in the development of automatic systems for postharvest classification of calla lily flowers.El manejo poscosecha inadecuado es uno de los problemas más serios que la floricultura tiene que enfrentar. Una opción para reducir las pérdidas poscosecha es emplear sistemas automáticos para ordenar y clasificar las flores, los cuales permiten resultados consistentes, reducen gastos y aceleran estas tareas. El objetivo de este trabajo fue demostrar el potencial de la reflectancia espectral para discriminar las diferentes fases de desarrollo poscosecha de flores de cala (Zantedeschia aethiopica [L.] Spreng., visando el uso de esta tecnología en sistemas automáticos para la clasificación de flores. El equipo de medición fue un espectrómetro conectado a un computador portátil y configurado para la adquisición de datos de reflectancia comprendidos en la región espectral de 400 a 1000 nm. Con base en los resultados, se constató una diferencia entre las curvas de reflectancia

  14. Quantitative method to assess caries via fluorescence imaging from the perspective of autofluorescence spectral analysis

    International Nuclear Information System (INIS)

    Chen, Q G; Xu, Y; Zhu, H H; Chen, H; Lin, B

    2015-01-01

    A quantitative method to discriminate caries lesions for a fluorescence imaging system is proposed in this paper. The autofluorescence spectral investigation of 39 teeth samples classified by the International Caries Detection and Assessment System levels was performed at 405 nm excitation. The major differences in the different caries lesions focused on the relative spectral intensity range of 565–750 nm. The spectral parameter, defined as the ratio of wavebands at 565–750 nm to the whole spectral range, was calculated. The image component ratio R/(G + B) of color components was statistically computed by considering the spectral parameters (e.g. autofluorescence, optical filter, and spectral sensitivity) in our fluorescence color imaging system. Results showed that the spectral parameter and image component ratio presented a linear relation. Therefore, the image component ratio was graded as <0.66, 0.66–1.06, 1.06–1.62, and >1.62 to quantitatively classify sound, early decay, established decay, and severe decay tissues, respectively. Finally, the fluorescence images of caries were experimentally obtained, and the corresponding image component ratio distribution was compared with the classification result. A method to determine the numerical grades of caries using a fluorescence imaging system was proposed. This method can be applied to similar imaging systems. (paper)

  15. Comparison of Aerosol Classification From Airborne High Spectral Resolution Lidar and the CALIPSO Vertical Feature Mask

    Science.gov (United States)

    Burton, Sharon P.; Ferrare, Rich A.; Omar, Ali H.; Vaughan, Mark A.; Rogers, Raymond R.; Hostetler, Chris a.; Hair, Johnathan W.; Obland, Michael D.; Butler, Carolyn F.; Cook, Anthony L.; hide

    2012-01-01

    Knowledge of aerosol composition and vertical distribution is crucial for assessing the impact of aerosols on climate. In addition, aerosol classification is a key input to CALIOP aerosol retrievals, since CALIOP requires an inference of the lidar ratio in order to estimate the effects of aerosol extinction and backscattering. In contrast, the NASA airborne HSRL-1 directly measures both aerosol extinction and backscatter, and therefore the lidar ratio (extinction-to-backscatter ratio). Four aerosol intensive properties from HSRL-1 are combined to infer aerosol type. Aerosol classification results from HSRL-1 are used here to validate the CALIOP aerosol type inferences.

  16. An Object-Based Classification Approach for Mapping Migrant Housing in the Mega-Urban Area of the Pearl River Delta (China

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    Sebastian D’Oleire-Oltmanns

    2011-08-01

    Full Text Available Urban areas develop on formal and informal levels. Informal development is often highly dynamic, leading to a lag of spatial information about urban structure types. In this work, an object-based remote sensing approach will be presented to map the migrant housing urban structure type in the Pearl River Delta, China. SPOT5 data were utilized for the classification (auxiliary data, particularly up-to-date cadastral data, were not available. A hierarchically structured classification process was used to create (spectral independence from single satellite scenes and to arrive at a transferrable classification process. Using the presented classification approach, an overall classification accuracy of migrant housing of 68.0% is attained.

  17. The capabilities and scope-of-practice requirements of advanced life support practitioners undertaking critical care transfers: A Delphi study

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    Monique Venter

    2016-11-01

    Full Text Available Background. Critical care transfers (CCT refer to the high level of care given during transport (via ambulance, helicopter or fixed-wing aircraft of patients who are of high acuity. In South Africa (SA, advanced life support (ALS paramedics undertake CCTs. The scope of ALS in SA has no extended protocol regarding procedures or medications in terms of dealing with these CCTs. Aim. The aim of this study was to obtain the opinions of several experts in fields pertaining to critical care and transport and to gain consensus on the skills and scope-of-practice requirements of paramedics undertaking CCTs in the SA setting. Methods. A modified Delphi study consisting of three rounds was undertaken using an online survey platform. A heterogeneous sample (n=7, consisting of specialists in the fields of anaesthesiology, emergency medicine, internal medicine, critical care, critical care transport and paediatrics, was asked to indicate whether, in their opinion, selected procedures and medications were needed within the scope of practice of paramedics undertaking CCTs. Results. After three rounds, consensus was obtained in 70% (57/81 of procedures and medications. Many of these items are not currently within the scope of paramedics’ training. The panel felt that paramedics undertaking these transfers should have additional postgraduate training that is specific to critical care. Conclusion. Major discrepancies exist between the current scope of paramedic practice and the suggested required scope of practice for CCTs. An extended scope of practice and additional training should be considered for these practitioners.

  18. A systematic review of definitions and classification systems of adjacent segment pathology.

    Science.gov (United States)

    Kraemer, Paul; Fehlings, Michael G; Hashimoto, Robin; Lee, Michael J; Anderson, Paul A; Chapman, Jens R; Raich, Annie; Norvell, Daniel C

    2012-10-15

    Systematic review. To undertake a systematic review to determine how "adjacent segment degeneration," "adjacent segment disease," or clinical pathological processes that serve as surrogates for adjacent segment pathology are classified and defined in the peer-reviewed literature. Adjacent segment degeneration and adjacent segment disease are terms referring to degenerative changes known to occur after reconstructive spine surgery, most commonly at an immediately adjacent functional spinal unit. These can include disc degeneration, instability, spinal stenosis, facet degeneration, and deformity. The true incidence and clinical impact of degenerative changes at the adjacent segment is unclear because there is lack of a universally accepted classification system that rigorously addresses clinical and radiological issues. A systematic review of the English language literature was undertaken and articles were classified using the Grades of Recommendation Assessment, Development, and Evaluation criteria. RESULTS.: Seven classification systems of spinal degeneration, including degeneration at the adjacent segment, were identified. None have been evaluated for reliability or validity specific to patients with degeneration at the adjacent segment. The ways in which terms related to adjacent segment "degeneration" or "disease" are defined in the peer-reviewed literature are highly variable. On the basis of the systematic review presented in this article, no formal classification system for either cervical or thoracolumbar adjacent segment disorders currently exists. No recommendations regarding the use of current classification of degeneration at any segments can be made based on the available literature. A new comprehensive definition for adjacent segment pathology (ASP, the now preferred terminology) has been proposed in this Focus Issue, which reflects the diverse pathology observed at functional spinal units adjacent to previous spinal reconstruction and balances

  19. Comparison and classification of all-optical CDMA systems for future telecommunication networks

    Science.gov (United States)

    Iversen, Kay; Hampicke, Dirk

    1995-12-01

    This paper shows the state of the art in fiber optical code-division multiple-access (CDMA). Recent work in this area for both, systems and sequences is reviewed and analyzed. For that purpose a classification of systems, corresponding to the manner of signal processing and a classification of known (0,1)-sequences are presented. It is shown that due to the limits by currently available device technology especially two techniques are promising for implementation in broadband telecommunication networks: spectral encoding with integrated optical filters and CDMA in combination with wavelength multiple access schemes. Further an overview about some important experiments in this field is given.

  20. Optical perception for detection of cutaneous T-cell lymphoma by multi-spectral imaging

    International Nuclear Information System (INIS)

    Hsiao, Yu-Ping; Wang, Hsiang-Chen; Chen, Shih-Hua; Tsai, Chung-Hung; Yang, Jen-Hung

    2014-01-01

    In this study, the spectrum of each picture element of the patient’s skin image was obtained by multi-spectral imaging technology. Spectra of normal or pathological skin were collected from 15 patients. Principal component analysis and principal component scores of skin spectra were employed to distinguish the spectral characteristics with different diseases. Finally, skin regions with suspected cutaneous T-cell lymphoma (CTCL) lesions were successfully predicted by evaluation and classification of the spectra of pathological skin. The sensitivity and specificity of this technique were 89.65% and 95.18% after the analysis of about 109 patients. The probability of atopic dermatitis and psoriasis patients misinterpreted as CTCL were 5.56% and 4.54%, respectively. (paper)

  1. Hyperspectral Image Classification With Markov Random Fields and a Convolutional Neural Network

    Science.gov (United States)

    Cao, Xiangyong; Zhou, Feng; Xu, Lin; Meng, Deyu; Xu, Zongben; Paisley, John

    2018-05-01

    This paper presents a new supervised classification algorithm for remotely sensed hyperspectral image (HSI) which integrates spectral and spatial information in a unified Bayesian framework. First, we formulate the HSI classification problem from a Bayesian perspective. Then, we adopt a convolutional neural network (CNN) to learn the posterior class distributions using a patch-wise training strategy to better use the spatial information. Next, spatial information is further considered by placing a spatial smoothness prior on the labels. Finally, we iteratively update the CNN parameters using stochastic gradient decent (SGD) and update the class labels of all pixel vectors using an alpha-expansion min-cut-based algorithm. Compared with other state-of-the-art methods, the proposed classification method achieves better performance on one synthetic dataset and two benchmark HSI datasets in a number of experimental settings.

  2. Land Cover Classification from Multispectral Data Using Computational Intelligence Tools: A Comparative Study

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    André Mora

    2017-11-01

    Full Text Available This article discusses how computational intelligence techniques are applied to fuse spectral images into a higher level image of land cover distribution for remote sensing, specifically for satellite image classification. We compare a fuzzy-inference method with two other computational intelligence methods, decision trees and neural networks, using a case study of land cover classification from satellite images. Further, an unsupervised approach based on k-means clustering has been also taken into consideration for comparison. The fuzzy-inference method includes training the classifier with a fuzzy-fusion technique and then performing land cover classification using reinforcement aggregation operators. To assess the robustness of the four methods, a comparative study including three years of land cover maps for the district of Mandimba, Niassa province, Mozambique, was undertaken. Our results show that the fuzzy-fusion method performs similarly to decision trees, achieving reliable classifications; neural networks suffer from overfitting; while k-means clustering constitutes a promising technique to identify land cover types from unknown areas.

  3. Multi-Temporal Land Cover Classification with Long Short-Term Memory Neural Networks

    Science.gov (United States)

    Rußwurm, M.; Körner, M.

    2017-05-01

    Land cover classification (LCC) is a central and wide field of research in earth observation and has already put forth a variety of classification techniques. Many approaches are based on classification techniques considering observation at certain points in time. However, some land cover classes, such as crops, change their spectral characteristics due to environmental influences and can thus not be monitored effectively with classical mono-temporal approaches. Nevertheless, these temporal observations should be utilized to benefit the classification process. After extensive research has been conducted on modeling temporal dynamics by spectro-temporal profiles using vegetation indices, we propose a deep learning approach to utilize these temporal characteristics for classification tasks. In this work, we show how long short-term memory (LSTM) neural networks can be employed for crop identification purposes with SENTINEL 2A observations from large study areas and label information provided by local authorities. We compare these temporal neural network models, i.e., LSTM and recurrent neural network (RNN), with a classical non-temporal convolutional neural network (CNN) model and an additional support vector machine (SVM) baseline. With our rather straightforward LSTM variant, we exceeded state-of-the-art classification performance, thus opening promising potential for further research.

  4. MULTI-TEMPORAL LAND COVER CLASSIFICATION WITH LONG SHORT-TERM MEMORY NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    M. Rußwurm

    2017-05-01

    Full Text Available Land cover classification (LCC is a central and wide field of research in earth observation and has already put forth a variety of classification techniques. Many approaches are based on classification techniques considering observation at certain points in time. However, some land cover classes, such as crops, change their spectral characteristics due to environmental influences and can thus not be monitored effectively with classical mono-temporal approaches. Nevertheless, these temporal observations should be utilized to benefit the classification process. After extensive research has been conducted on modeling temporal dynamics by spectro-temporal profiles using vegetation indices, we propose a deep learning approach to utilize these temporal characteristics for classification tasks. In this work, we show how long short-term memory (LSTM neural networks can be employed for crop identification purposes with SENTINEL 2A observations from large study areas and label information provided by local authorities. We compare these temporal neural network models, i.e., LSTM and recurrent neural network (RNN, with a classical non-temporal convolutional neural network (CNN model and an additional support vector machine (SVM baseline. With our rather straightforward LSTM variant, we exceeded state-of-the-art classification performance, thus opening promising potential for further research.

  5. Exploiting High Resolution Multi-Seasonal Textural Measures and Spectral Information for Reedbed Mapping

    Directory of Open Access Journals (Sweden)

    Alex Okiemute Onojeghuo

    2016-02-01

    Full Text Available Reedbeds across the UK are amongst the most important habitats for rare and endangered birds, wildlife and organisms. However, over the past century, this valued wetland habitat has experienced a drastic reduction in quality and spatial coverage due to pressures from human related activities. To this end, conservation organisations across the UK have been charged with the task of conserving and expanding this threatened habitat. With this backdrop, the study aimed to develop a methodology for accurate reedbed mapping through the combined use of multi-seasonal texture measures and spectral information contained in high resolution QuickBird satellite imagery. The key objectives were to determine the most effective single-date (autumn or summer and multi-seasonal QuickBird imagery suitable for reedbed mapping over the study area; to evaluate the effectiveness of combining multi-seasonal texture measures and spectral information for reedbed mapping using a variety of combinations; and to evaluate the most suitable classification technique for reedbed mapping from three selected classification techniques, namely maximum likelihood classifier, spectral angular mapper and artificial neural network. Using two selected grey-level co-occurrence textural measures (entropy and angular second moment, a series of experiments were conducted using varied combinations of single-date and multi-seasonal QuickBird imagery. Overall, the results indicate the multi-seasonal pansharpened multispectral bands (eight layers combined with all eight grey level co-occurrence matrix texture measures (entropy and angular second moment computed using windows 3 × 3 and 7 × 7 produced the optimal reedbed (76.5% and overall classification (78.1% accuracies using the maximum likelihood classifier technique. Using the optimal 16 layer multi-seasonal pansharpened multispectral and texture combined image dataset, a total reedbed area of 9.8 hectares was successfully mapped over the

  6. UNLABELED SELECTED SAMPLES IN FEATURE EXTRACTION FOR CLASSIFICATION OF HYPERSPECTRAL IMAGES WITH LIMITED TRAINING SAMPLES

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    A. Kianisarkaleh

    2015-12-01

    Full Text Available Feature extraction plays a key role in hyperspectral images classification. Using unlabeled samples, often unlimitedly available, unsupervised and semisupervised feature extraction methods show better performance when limited number of training samples exists. This paper illustrates the importance of selecting appropriate unlabeled samples that used in feature extraction methods. Also proposes a new method for unlabeled samples selection using spectral and spatial information. The proposed method has four parts including: PCA, prior classification, posterior classification and sample selection. As hyperspectral image passes these parts, selected unlabeled samples can be used in arbitrary feature extraction methods. The effectiveness of the proposed unlabeled selected samples in unsupervised and semisupervised feature extraction is demonstrated using two real hyperspectral datasets. Results show that through selecting appropriate unlabeled samples, the proposed method can improve the performance of feature extraction methods and increase classification accuracy.

  7. Statistical analysis of spectral data: a methodology for designing an intelligent monitoring system for the diabetic foot

    Science.gov (United States)

    Liu, Chanjuan; van Netten, Jaap J.; Klein, Marvin E.; van Baal, Jeff G.; Bus, Sicco A.; van der Heijden, Ferdi

    2013-12-01

    Early detection of (pre-)signs of ulceration on a diabetic foot is valuable for clinical practice. Hyperspectral imaging is a promising technique for detection and classification of such (pre-)signs. However, the number of the spectral bands should be limited to avoid overfitting, which is critical for pixel classification with hyperspectral image data. The goal was to design a detector/classifier based on spectral imaging (SI) with a small number of optical bandpass filters. The performance and stability of the design were also investigated. The selection of the bandpass filters boils down to a feature selection problem. A dataset was built, containing reflectance spectra of 227 skin spots from 64 patients, measured with a spectrometer. Each skin spot was annotated manually by clinicians as "healthy" or a specific (pre-)sign of ulceration. Statistical analysis on the data set showed the number of required filters is between 3 and 7, depending on additional constraints on the filter set. The stability analysis revealed that shot noise was the most critical factor affecting the classification performance. It indicated that this impact could be avoided in future SI systems with a camera sensor whose saturation level is higher than 106, or by postimage processing.

  8. A Method of Spatial Mapping and Reclassification for High-Spatial-Resolution Remote Sensing Image Classification

    Directory of Open Access Journals (Sweden)

    Guizhou Wang

    2013-01-01

    Full Text Available This paper presents a new classification method for high-spatial-resolution remote sensing images based on a strategic mechanism of spatial mapping and reclassification. The proposed method includes four steps. First, the multispectral image is classified by a traditional pixel-based classification method (support vector machine. Second, the panchromatic image is subdivided by watershed segmentation. Third, the pixel-based multispectral image classification result is mapped to the panchromatic segmentation result based on a spatial mapping mechanism and the area dominant principle. During the mapping process, an area proportion threshold is set, and the regional property is defined as unclassified if the maximum area proportion does not surpass the threshold. Finally, unclassified regions are reclassified based on spectral information using the minimum distance to mean algorithm. Experimental results show that the classification method for high-spatial-resolution remote sensing images based on the spatial mapping mechanism and reclassification strategy can make use of both panchromatic and multispectral information, integrate the pixel- and object-based classification methods, and improve classification accuracy.

  9. Ontology-based classification of remote sensing images using spectral rules

    Science.gov (United States)

    Andrés, Samuel; Arvor, Damien; Mougenot, Isabelle; Libourel, Thérèse; Durieux, Laurent

    2017-05-01

    Earth Observation data is of great interest for a wide spectrum of scientific domain applications. An enhanced access to remote sensing images for "domain" experts thus represents a great advance since it allows users to interpret remote sensing images based on their domain expert knowledge. However, such an advantage can also turn into a major limitation if this knowledge is not formalized, and thus is difficult for it to be shared with and understood by other users. In this context, knowledge representation techniques such as ontologies should play a major role in the future of remote sensing applications. We implemented an ontology-based prototype to automatically classify Landsat images based on explicit spectral rules. The ontology is designed in a very modular way in order to achieve a generic and versatile representation of concepts we think of utmost importance in remote sensing. The prototype was tested on four subsets of Landsat images and the results confirmed the potential of ontologies to formalize expert knowledge and classify remote sensing images.

  10. Let your fingers do the walking: A simple spectral signature model for "remote" fossil prospecting.

    Science.gov (United States)

    Conroy, Glenn C; Emerson, Charles W; Anemone, Robert L; Townsend, K E Beth

    2012-07-01

    Even with the most meticulous planning, and utilizing the most experienced fossil-hunters, fossil prospecting in remote and/or extensive areas can be time-consuming, expensive, logistically challenging, and often hit or miss. While nothing can predict or guarantee with 100% assurance that fossils will be found in any particular location, any procedures or techniques that might increase the odds of success would be a major benefit to the field. Here we describe, and test, one such technique that we feel has great potential for increasing the probability of finding fossiliferous sediments - a relatively simple spectral signature model using the spatial analysis and image classification functions of ArcGIS(®)10 that creates interactive thematic land cover maps that can be used for "remote" fossil prospecting. Our test case is the extensive Eocene sediments of the Uinta Basin, Utah - a fossil prospecting area encompassing ∼1200 square kilometers. Using Landsat 7 ETM+ satellite imagery, we "trained" the spatial analysis and image classification algorithms using the spectral signatures of known fossil localities discovered in the Uinta Basin prior to 2005 and then created interactive probability models highlighting other regions in the Basin having a high probability of containing fossiliferous sediments based on their spectral signatures. A fortuitous "post-hoc" validation of our model presented itself. Our model identified several paleontological "hotspots", regions that, while not producing any fossil localities prior to 2005, had high probabilities of being fossiliferous based on the similarities of their spectral signatures to those of previously known fossil localities. Subsequent fieldwork found fossils in all the regions predicted by the model. Copyright © 2012 Elsevier Ltd. All rights reserved.

  11. Estimating workload using EEG spectral power and ERPs in the n-back task

    Science.gov (United States)

    Brouwer, Anne-Marie; Hogervorst, Maarten A.; van Erp, Jan B. F.; Heffelaar, Tobias; Zimmerman, Patrick H.; Oostenveld, Robert

    2012-08-01

    Previous studies indicate that both electroencephalogram (EEG) spectral power (in particular the alpha and theta band) and event-related potentials (ERPs) (in particular the P300) can be used as a measure of mental work or memory load. We compare their ability to estimate workload level in a well-controlled task. In addition, we combine both types of measures in a single classification model to examine whether this results in higher classification accuracy than either one alone. Participants watched a sequence of visually presented letters and indicated whether or not the current letter was the same as the one (n instances) before. Workload was varied by varying n. We developed different classification models using ERP features, frequency power features or a combination (fusion). Training and testing of the models simulated an online workload estimation situation. All our ERP, power and fusion models provide classification accuracies between 80% and 90% when distinguishing between the highest and the lowest workload condition after 2 min. For 32 out of 35 participants, classification was significantly higher than chance level after 2.5 s (or one letter) as estimated by the fusion model. Differences between the models are rather small, though the fusion model performs better than the other models when only short data segments are available for estimating workload.

  12. The zCOSMOS redshift survey : The three-dimensional classification cube and bimodality in galaxy physical properties

    NARCIS (Netherlands)

    Mignoli, M.; Zamorani, G.; Scodeggio, M.; Cimatti, A.; Halliday, C.; Lilly, S. J.; Pozzetti, L.; Vergani, D.; Carollo, C. M.; Contini, T.; Le Fevre, O.; Mainieri, V.; Renzini, A.; Bardelli, S.; Bolzonella, M.; Bongiorno, A.; Caputi, K.; Coppa, G.; Cucciati, O.; de la Torre, S.; de Ravel, L.; Franzetti, P.; Garilli, B.; Iovino, A.; Kampczyk, P.; Kneib, J. -P.; Knobel, C.; Kovac, K.; Lamareille, F.; Le Borgne, J. -F.; Le Brun, V.; Maier, C.; Pello, R.; Peng, Y.; Montero, E. Perez; Ricciardelli, E.; Scarlata, C.; Silverman, J. D.; Tanaka, M.; Tasca, L.; Tresse, L.; Zucca, E.; Abbas, U.; Bottini, D.; Capak, P.; Cappi, A.; Cassata, P.; Fumana, M.; Guzzo, L.; Leauthaud, A.; Maccagni, D.; Marinoni, C.; McCracken, H. J.; Memeo, P.; Meneux, B.; Oesch, P.; Porciani, C.; Scaramella, R.; Scoville, N.

    Aims: We investigate the relationships between three main optical galaxy observables (spectral properties, colors, and morphology), exploiting the data set provided by the COSMOS/zCOSMOS survey. The purpose of this paper is to define a simple galaxy classification cube, with a carefully selected

  13. [Road Extraction in Remote Sensing Images Based on Spectral and Edge Analysis].

    Science.gov (United States)

    Zhao, Wen-zhi; Luo, Li-qun; Guo, Zhou; Yue, Jun; Yu, Xue-ying; Liu, Hui; Wei, Jing

    2015-10-01

    Roads are typically man-made objects in urban areas. Road extraction from high-resolution images has important applications for urban planning and transportation development. However, due to the confusion of spectral characteristic, it is difficult to distinguish roads from other objects by merely using traditional classification methods that mainly depend on spectral information. Edge is an important feature for the identification of linear objects (e. g. , roads). The distribution patterns of edges vary greatly among different objects. It is crucial to merge edge statistical information into spectral ones. In this study, a new method that combines spectral information and edge statistical features has been proposed. First, edge detection is conducted by using self-adaptive mean-shift algorithm on the panchromatic band, which can greatly reduce pseudo-edges and noise effects. Then, edge statistical features are obtained from the edge statistical model, which measures the length and angle distribution of edges. Finally, by integrating the spectral and edge statistical features, SVM algorithm is used to classify the image and roads are ultimately extracted. A series of experiments are conducted and the results show that the overall accuracy of proposed method is 93% comparing with only 78% overall accuracy of the traditional. The results demonstrate that the proposed method is efficient and valuable for road extraction, especially on high-resolution images.

  14. The VLT-FLAMES Tarantula Survey. XVIII. Classifications and radial velocities of the B-type stars

    NARCIS (Netherlands)

    Evans, C.J.; Kennedy, M.B.; Dufton, P.L.; Howarth, I.D.; Walborn, N.R.; Markova, N.; Clark, J.S.; de Mink, S.E.; de Koter, A.; Dunstall, P.R.; Hénault-Brunet, V.; Maíz Apellániz, J.; McEvoy, C.M.; Sana, H.; Simón-Díaz, S.; Taylor, W.D.; Vink, J.S.

    2015-01-01

    We present spectral classifications for 438 B-type stars observed as part of the VLT-FLAMES Tarantula Survey (VFTS) in the 30 Doradus region of the Large Magellanic Cloud. Radial velocities are provided for 307 apparently single stars, and for 99 targets with radial-velocity variations which are

  15. RESEARCH ON REMOTE SENSING GEOLOGICAL INFORMATION EXTRACTION BASED ON OBJECT ORIENTED CLASSIFICATION

    Directory of Open Access Journals (Sweden)

    H. Gao

    2018-04-01

    Full Text Available The northern Tibet belongs to the Sub cold arid climate zone in the plateau. It is rarely visited by people. The geological working conditions are very poor. However, the stratum exposures are good and human interference is very small. Therefore, the research on the automatic classification and extraction of remote sensing geological information has typical significance and good application prospect. Based on the object-oriented classification in Northern Tibet, using the Worldview2 high-resolution remote sensing data, combined with the tectonic information and image enhancement, the lithological spectral features, shape features, spatial locations and topological relations of various geological information are excavated. By setting the threshold, based on the hierarchical classification, eight kinds of geological information were classified and extracted. Compared with the existing geological maps, the accuracy analysis shows that the overall accuracy reached 87.8561 %, indicating that the classification-oriented method is effective and feasible for this study area and provides a new idea for the automatic extraction of remote sensing geological information.

  16. Monitoring of Water Spectral Pattern Reveals Differences in Probiotics Growth When Used for Rapid Bacteria Selection.

    Directory of Open Access Journals (Sweden)

    Aleksandar Slavchev

    Full Text Available Development of efficient screening method coupled with cell functionality evaluation is highly needed in contemporary microbiology. The presented novel concept and fast non-destructive method brings in to play the water spectral pattern of the solution as a molecular fingerprint of the cell culture system. To elucidate the concept, NIR spectroscopy with Aquaphotomics were applied to monitor the growth of sixteen Lactobacillus bulgaricus one Lactobacillus pentosus and one Lactobacillus gasseri bacteria strains. Their growth rate, maximal optical density, low pH and bile tolerances were measured and further used as a reference data for analysis of the simultaneously acquired spectral data. The acquired spectral data in the region of 1100-1850nm was subjected to various multivariate data analyses - PCA, OPLS-DA, PLSR. The results showed high accuracy of bacteria strains classification according to their probiotic strength. Most informative spectral fingerprints covered the first overtone of water, emphasizing the relation of water molecular system to cell functionality.

  17. Rule-based land cover classification from very high-resolution satellite image with multiresolution segmentation

    Science.gov (United States)

    Haque, Md. Enamul; Al-Ramadan, Baqer; Johnson, Brian A.

    2016-07-01

    Multiresolution segmentation and rule-based classification techniques are used to classify objects from very high-resolution satellite images of urban areas. Custom rules are developed using different spectral, geometric, and textural features with five scale parameters, which exploit varying classification accuracy. Principal component analysis is used to select the most important features out of a total of 207 different features. In particular, seven different object types are considered for classification. The overall classification accuracy achieved for the rule-based method is 95.55% and 98.95% for seven and five classes, respectively. Other classifiers that are not using rules perform at 84.17% and 97.3% accuracy for seven and five classes, respectively. The results exploit coarse segmentation for higher scale parameter and fine segmentation for lower scale parameter. The major contribution of this research is the development of rule sets and the identification of major features for satellite image classification where the rule sets are transferable and the parameters are tunable for different types of imagery. Additionally, the individual objectwise classification and principal component analysis help to identify the required object from an arbitrary number of objects within images given ground truth data for the training.

  18. Classification of hydration status using electrocardiogram and machine learning

    Science.gov (United States)

    Kaveh, Anthony; Chung, Wayne

    2013-10-01

    The electrocardiogram (ECG) has been used extensively in clinical practice for decades to non-invasively characterize the health of heart tissue; however, these techniques are limited to time domain features. We propose a machine classification system using support vector machines (SVM) that uses temporal and spectral information to classify health state beyond cardiac arrhythmias. Our method uses single lead ECG to classify volume depletion (or dehydration) without the lengthy and costly blood analysis tests traditionally used for detecting dehydration status. Our method builds on established clinical ECG criteria for identifying electrolyte imbalances and lends to automated, computationally efficient implementation. The method was tested on the MIT-BIH PhysioNet database to validate this purely computational method for expedient disease-state classification. The results show high sensitivity, supporting use as a cost- and time-effective screening tool.

  19. Object Classification Based on Analysis of Spectral Characteristics of Seismic Signal Envelopes

    Science.gov (United States)

    Morozov, Yu. V.; Spektor, A. A.

    2017-11-01

    A method for classifying moving objects having a seismic effect on the ground surface is proposed which is based on statistical analysis of the envelopes of received signals. The values of the components of the amplitude spectrum of the envelopes obtained applying Hilbert and Fourier transforms are used as classification criteria. Examples illustrating the statistical properties of spectra and the operation of the seismic classifier are given for an ensemble of objects of four classes (person, group of people, large animal, vehicle). It is shown that the computational procedures for processing seismic signals are quite simple and can therefore be used in real-time systems with modest requirements for computational resources.

  20. DETERMINING SPECTRAL REFLECTANCE COEFFICIENTS FROM HYPERSPECTRAL IMAGES OBTAINED FROM LOW ALTITUDES

    Directory of Open Access Journals (Sweden)

    P. Walczykowski

    2016-06-01

    Full Text Available Remote Sensing plays very important role in many different study fields, like hydrology, crop management, environmental and ecosystem studies. For all mentioned areas of interest different remote sensing and image processing techniques, such as: image classification (object and pixel- based, object identification, change detection, etc. can be applied. Most of this techniques use spectral reflectance coefficients as the basis for the identification and distinction of different objects and materials, e.g. monitoring of vegetation stress, identification of water pollutants, yield identification, etc. Spectral characteristics are usually acquired using discrete methods such as spectrometric measurements in both laboratory and field conditions. Such measurements however can be very time consuming, which has led many international researchers to investigate the reliability and accuracy of using image-based methods. According to published and ongoing studies, in order to acquire these spectral characteristics from images, it is necessary to have hyperspectral data. The presented article describes a series of experiments conducted using the push-broom Headwall MicroHyperspec A-series VNIR. This hyperspectral scanner allows for registration of images with more than 300 spectral channels with a 1.9 nm spectral bandwidth in the 380- 1000 nm range. The aim of these experiments was to establish a methodology for acquiring spectral reflectance characteristics of different forms of land cover using such sensor. All research work was conducted in controlled conditions from low altitudes. Hyperspectral images obtained with this specific type of sensor requires a unique approach in terms of post-processing, especially radiometric correction. Large amounts of acquired imagery data allowed the authors to establish a new post- processing approach. The developed methodology allowed the authors to obtain spectral reflectance coefficients from a hyperspectral sensor

  1. Determining Spectral Reflectance Coefficients from Hyperspectral Images Obtained from Low Altitudes

    Science.gov (United States)

    Walczykowski, P.; Jenerowicz, A.; Orych, A.; Siok, K.

    2016-06-01

    Remote Sensing plays very important role in many different study fields, like hydrology, crop management, environmental and ecosystem studies. For all mentioned areas of interest different remote sensing and image processing techniques, such as: image classification (object and pixel- based), object identification, change detection, etc. can be applied. Most of this techniques use spectral reflectance coefficients as the basis for the identification and distinction of different objects and materials, e.g. monitoring of vegetation stress, identification of water pollutants, yield identification, etc. Spectral characteristics are usually acquired using discrete methods such as spectrometric measurements in both laboratory and field conditions. Such measurements however can be very time consuming, which has led many international researchers to investigate the reliability and accuracy of using image-based methods. According to published and ongoing studies, in order to acquire these spectral characteristics from images, it is necessary to have hyperspectral data. The presented article describes a series of experiments conducted using the push-broom Headwall MicroHyperspec A-series VNIR. This hyperspectral scanner allows for registration of images with more than 300 spectral channels with a 1.9 nm spectral bandwidth in the 380- 1000 nm range. The aim of these experiments was to establish a methodology for acquiring spectral reflectance characteristics of different forms of land cover using such sensor. All research work was conducted in controlled conditions from low altitudes. Hyperspectral images obtained with this specific type of sensor requires a unique approach in terms of post-processing, especially radiometric correction. Large amounts of acquired imagery data allowed the authors to establish a new post- processing approach. The developed methodology allowed the authors to obtain spectral reflectance coefficients from a hyperspectral sensor mounted on an

  2. Precision Stellar Characterization of FGKM Stars using an Empirical Spectral Library

    Science.gov (United States)

    Yee, Samuel W.; Petigura, Erik A.; von Braun, Kaspar

    2017-02-01

    Classification of stars, by comparing their optical spectra to a few dozen spectral standards, has been a workhorse of observational astronomy for more than a century. Here, we extend this technique by compiling a library of optical spectra of 404 touchstone stars observed with Keck/HIRES by the California Planet Search. The spectra have high resolution (R ≈ 60,000), high signal-to-noise ratio (S/N ≈ 150/pixel), and are registered onto a common wavelength scale. The library stars have properties derived from interferometry, asteroseismology, LTE spectral synthesis, and spectrophotometry. To address a lack of well-characterized late-K dwarfs in the literature, we measure stellar radii and temperatures for 23 nearby K dwarfs, using modeling of the spectral energy distribution and Gaia parallaxes. This library represents a uniform data set spanning the spectral types ˜M5-F1 (T eff ≈ 3000-7000 K, R ⋆ ≈ 0.1-16 R ⊙). We also present “Empirical SpecMatch” (SpecMatch-Emp), a tool for parameterizing unknown spectra by comparing them against our spectral library. For FGKM stars, SpecMatch-Emp achieves accuracies of 100 K in effective temperature (T eff), 15% in stellar radius (R ⋆), and 0.09 dex in metallicity ([Fe/H]). Because the code relies on empirical spectra it performs particularly well for stars ˜K4 and later, which are challenging to model with existing spectral synthesizers, reaching accuracies of 70 K in T eff, 10% in R ⋆, and 0.12 dex in [Fe/H]. We also validate the performance of SpecMatch-Emp, finding it to be robust at lower spectral resolution and S/N, enabling the characterization of faint late-type stars. Both the library and stellar characterization code are publicly available.

  3. Precision Stellar Characterization of FGKM Stars using an Empirical Spectral Library

    Energy Technology Data Exchange (ETDEWEB)

    Yee, Samuel W.; Petigura, Erik A. [California Institute of Technology (United States); Von Braun, Kaspar, E-mail: syee@caltech.edu [Lowell Observatory, 1400 W. Mars Hill Road, Flagstaff, AZ 86001 (United States)

    2017-02-10

    Classification of stars, by comparing their optical spectra to a few dozen spectral standards, has been a workhorse of observational astronomy for more than a century. Here, we extend this technique by compiling a library of optical spectra of 404 touchstone stars observed with Keck/HIRES by the California Planet Search. The spectra have high resolution ( R ≈ 60,000), high signal-to-noise ratio (S/N ≈ 150/pixel), and are registered onto a common wavelength scale. The library stars have properties derived from interferometry, asteroseismology, LTE spectral synthesis, and spectrophotometry. To address a lack of well-characterized late-K dwarfs in the literature, we measure stellar radii and temperatures for 23 nearby K dwarfs, using modeling of the spectral energy distribution and Gaia parallaxes. This library represents a uniform data set spanning the spectral types ∼M5–F1 ( T {sub eff} ≈ 3000–7000 K, R {sub ⋆} ≈ 0.1–16 R {sub ⊙}). We also present “Empirical SpecMatch” (SpecMatch-Emp), a tool for parameterizing unknown spectra by comparing them against our spectral library. For FGKM stars, SpecMatch-Emp achieves accuracies of 100 K in effective temperature ( T {sub eff}), 15% in stellar radius ( R {sub ⋆}), and 0.09 dex in metallicity ([Fe/H]). Because the code relies on empirical spectra it performs particularly well for stars ∼K4 and later, which are challenging to model with existing spectral synthesizers, reaching accuracies of 70 K in T {sub eff}, 10% in R {sub ⋆}, and 0.12 dex in [Fe/H]. We also validate the performance of SpecMatch-Emp, finding it to be robust at lower spectral resolution and S/N, enabling the characterization of faint late-type stars. Both the library and stellar characterization code are publicly available.

  4. Farmers Prone to Drought Risk: Why Some Farmers Undertake Farm-Level Risk-Reduction Measures While Others Not?

    Science.gov (United States)

    Gebrehiwot, Tagel; van der Veen, Anne

    2015-03-01

    This research investigates farmers' cognitive perceptions of risk and the behavioral intentions to undertake farm-level risk-reduction measures. It has been observed that people who are susceptible to natural hazards often fail to act, or do very little, to protect their assets or lives. To answer the question of why some people show adaptive behavior while others do not, a socio-psychological model of precautionary adaptation based on protection motivation theory and trans-theoretical stage model has been applied for the first time to areas of drought risk in the developing countries cultural context. The applicability of the integrated model is explored by means of a representative sample survey of smallholder farmers in northern Ethiopia. The result of the study showed that there is a statistically significant association between farmer's behavioral intention to undertake farm-level risk-reduction measures and the main important protection motivation model variables. High perceived vulnerability, severity of consequences, self-efficacy, and response efficacy lead to higher levels of behavioral intentions to undertake farm-level risk-reduction measures. For farmers in the action stage, self-efficacy and response efficacy were the main motivators of behavioral intention. For farmers in the contemplative stage, self-efficacy and cost appear to be the main motivators for them to act upon risk reduction, while perceived severity of consequences and cost of response actions were found to be important for farmers in the pre-contemplative stage.

  5. High Spatial Resolution Visual Band Imagery Outperforms Medium Resolution Spectral Imagery for Ecosystem Assessment in the Semi-Arid Brazilian Sertão

    Directory of Open Access Journals (Sweden)

    Ran Goldblatt

    2017-12-01

    Full Text Available Semi-arid ecosystems play a key role in global agricultural production, seasonal carbon cycle dynamics, and longer-run climate change. Because semi-arid landscapes are heterogeneous and often sparsely vegetated, repeated and large-scale ecosystem assessments of these regions have to date been impossible. Here, we assess the potential of high-spatial resolution visible band imagery for semi-arid ecosystem mapping. We use WorldView satellite imagery at 0.3–0.5 m resolution to develop a reference data set of nearly 10,000 labeled examples of three classes—trees, shrubs/grasses, and bare land—across 1000 km 2 of the semi-arid Sertão region of northeast Brazil. Using Google Earth Engine, we show that classification with low-spectral but high-spatial resolution input (WorldView outperforms classification with the full spectral information available from Landsat 30 m resolution imagery as input. Classification with high spatial resolution input improves detection of sparse vegetation and distinction between trees and seasonal shrubs and grasses, two features which are lost at coarser spatial (but higher spectral resolution input. Our total tree cover estimates for the study area disagree with recent estimates using other methods that may underestimate treecover because they confuse trees with seasonal vegetation (shrubs and grasses. This distinction is important for monitoring seasonal and long-run carbon cycle and ecosystem health. Our results suggest that newer remote sensing products that promise high frequency global coverage at high spatial but lower spectral resolution may offer new possibilities for direct monitoring of the world’s semi-arid ecosystems, and we provide methods that could be scaled to do so.

  6. EVALUATING THE POTENTIAL OF SATELLITE HYPERSPECTRAL RESURS-P DATA FOR FOREST SPECIES CLASSIFICATION

    Directory of Open Access Journals (Sweden)

    O. Brovkina

    2016-06-01

    Full Text Available Satellite-based hyperspectral sensors provide spectroscopic information in relatively narrow contiguous spectral bands over a large area which can be useful in forestry applications. This study evaluates the potential of satellite hyperspectral Resurs-P data for forest species mapping. Firstly, a comparative study between top of canopy reflectance obtained from the Resurs-P, from the airborne hyperspectral scanner CASI and from field measurement (FieldSpec ASD 4 on selected vegetation cover types is conducted. Secondly, Resurs-P data is tested in classification and verification of different forest species compartments. The results demonstrate that satellite hyperspectral Resurs-P sensor can produce useful informational and show good performance for forest species classification comparable both with forestry map and classification from airborne CASI data, but also indicate that developments in pre-processing steps are still required to improve the mapping level.

  7. Reasons why specialist doctors undertake rural outreach services: an Australian cross-sectional study.

    Science.gov (United States)

    O'Sullivan, Belinda G; McGrail, Matthew R; Stoelwinder, Johannes U

    2017-01-07

    The purpose of the study is to explore the reasons why specialist doctors travel to provide regular rural outreach services, and whether reasons relate to (1) salaried or private fee-for-service practice and (2) providing rural outreach services in more remote locations. A national cross-sectional study of specialist doctors from the Medicine in Australia: Balancing Employment and Life (MABEL) survey in 2014 was implemented. Specialists providing rural outreach services self-reported on a 5-point scale their level of agreement with five reasons for participating. Chi-squared analysis tested association between agreement and variables of interest. Of 567 specialists undertaking rural outreach services, reasons for participating include to grow the practice (54%), maintain a regional connection (26%), provide complex healthcare (18%), healthcare for disadvantaged people (12%) and support rural staff (6%). Salaried specialists more commonly participated to grow the practice compared with specialists in fee-for-service practice (68 vs 49%). This reason was also related to travelling further and providing outreach services in outer regional/remote locations. Private fee-for-service specialists more commonly undertook outreach services to provide complex healthcare (22 vs 14%). Specialist doctors undertake rural outreach services for a range of reasons, mainly to complement the growth and diversity of their main practice or maintain a regional connection. Structuring rural outreach around the specialist's main practice is likely to support participation and improve service distribution.

  8. The Costs and Benefits of Undertaking Adult Education Courses from the Perspective of the Individual

    Science.gov (United States)

    AONTAS The National Adult Learning Organisation, 2009

    2009-01-01

    The aim of this study is to examine the costs and benefits of undertaking adult education courses from the perspective of the individual, using three different case studies. This will give a snapshot of the benefits and the types of costs incurred by three adult learners. Three individuals were contacted by Aontas and were asked if they would be…

  9. Wavelet-based multicomponent denoising on GPU to improve the classification of hyperspectral images

    Science.gov (United States)

    Quesada-Barriuso, Pablo; Heras, Dora B.; Argüello, Francisco; Mouriño, J. C.

    2017-10-01

    Supervised classification allows handling a wide range of remote sensing hyperspectral applications. Enhancing the spatial organization of the pixels over the image has proven to be beneficial for the interpretation of the image content, thus increasing the classification accuracy. Denoising in the spatial domain of the image has been shown as a technique that enhances the structures in the image. This paper proposes a multi-component denoising approach in order to increase the classification accuracy when a classification method is applied. It is computed on multicore CPUs and NVIDIA GPUs. The method combines feature extraction based on a 1Ddiscrete wavelet transform (DWT) applied in the spectral dimension followed by an Extended Morphological Profile (EMP) and a classifier (SVM or ELM). The multi-component noise reduction is applied to the EMP just before the classification. The denoising recursively applies a separable 2D DWT after which the number of wavelet coefficients is reduced by using a threshold. Finally, inverse 2D-DWT filters are applied to reconstruct the noise free original component. The computational cost of the classifiers as well as the cost of the whole classification chain is high but it is reduced achieving real-time behavior for some applications through their computation on NVIDIA multi-GPU platforms.

  10. Feature extraction based on extended multi-attribute profiles and sparse autoencoder for remote sensing image classification

    Science.gov (United States)

    Teffahi, Hanane; Yao, Hongxun; Belabid, Nasreddine; Chaib, Souleyman

    2018-02-01

    The satellite images with very high spatial resolution have been recently widely used in image classification topic as it has become challenging task in remote sensing field. Due to a number of limitations such as the redundancy of features and the high dimensionality of the data, different classification methods have been proposed for remote sensing images classification particularly the methods using feature extraction techniques. This paper propose a simple efficient method exploiting the capability of extended multi-attribute profiles (EMAP) with sparse autoencoder (SAE) for remote sensing image classification. The proposed method is used to classify various remote sensing datasets including hyperspectral and multispectral images by extracting spatial and spectral features based on the combination of EMAP and SAE by linking them to kernel support vector machine (SVM) for classification. Experiments on new hyperspectral image "Huston data" and multispectral image "Washington DC data" shows that this new scheme can achieve better performance of feature learning than the primitive features, traditional classifiers and ordinary autoencoder and has huge potential to achieve higher accuracy for classification in short running time.

  11. Raman spectral properties of squamous cell carcinoma of oral tissues and cells

    Science.gov (United States)

    Su, L.; Sun, Y. F.; Chen, Y.; Chen, P.; Shen, A. G.; Wang, X. H.; Jia, J.; Zhao, Y. F.; Zhou, X. D.; Hu, J. M.

    2012-01-01

    Early diagnosis is the key of the improved survival rates of oral cancer. Raman spectroscopy is sensitive to the early changes of molecular composition and structure that occur in benign lesion during carcinogenesis. In this study, in situ Raman analysis provided distinct spectra that can be used to discriminate between normal and malignant tissues, as well as normal and cancer cells. The biochemical variations between different groups were analyzed by the characteristic bands by comparing the normalized mean spectra. Spectral profiles of normal, malignant conditions show pronounced differences between one another, and multiple Raman markers associated with DNA and protein vibrational modes have been identified that exhibit excellent discrimination power for cancer sample identification. Statistical analyses of the Raman data and classification using principal component analysis (PCA) are shown to be effective for the Raman spectral diagnosis of oral mucosal diseases. The results indicate that the biomolecular differences between normal and malignant conditions are more obviously at the cellular level. This technique could provide a research foundation for the Raman spectral diagnosis of oral mucosal diseases.

  12. A Phytase Enzyme-Based Biochemistry Practical Particularly Suited to Students Undertaking Courses in Biotechnology and Environmental Science

    Science.gov (United States)

    Boyce, Angela; Casey, Anne; Walsh, Gary

    2004-01-01

    Courses in introductory biochemistry invariably encompass basic principles of enzymology, with reinforcement of lecture-based material in appropriate laboratory practicals. Students undertaking practical classes are more enthusiastic, and generally display improved performance, when the specific experiments undertaken show direct relevance to…

  13. EU program fuel cells in 2012 - FCH JU Fuel Cell and Hydrogen Joint Undertaking; EU-program braensleceller 2012 - FCH JU Fuel Cell and Hydrogen Joint Undertaking

    Energy Technology Data Exchange (ETDEWEB)

    Ridell, Bengt

    2013-03-15

    An EU activity in fuel cell and hydrogen field are gathered since 2008 in a so called JU, Joint Undertaking, or as it is also referred to as JTI Joint Technology Initiative. The program will run 2008 - 2013 and covers in total 940 MEUR of which the EU Commission is funding 470 MEUR. The activities of the FCH JU are governed by a Governing Board which has 12 members, five from the Commission, one of the research group and 5 from the Industrial Group. The current agreement for the FCH JU / JTI is coming to an end, and the sixth and final call was released in January 2013 with the deadline of 22 May 2013. Funding from the Commission is made through the Seventh Framework Programme FP7, which ends in 2013. Next the Eighth Framework Programme called Horizon 2020 shall continue for the years 2014 - 2020. Five of the six calls are completed. From the four first calls there are 61 projects started which 6 have been completed. From the fifth announcement is further 27 projects selected for negotiation with the Commission and they will start soon. It is now working intensively to plan Horizon 2020. There are plans to continue the new FCH JU but nothing is decided either for this or for the budget for Horizon 2020. If the FCH Joint Undertaking shall continue in its present form as a Joint Undertaking it will require clear long-term commitments from the private sector and also from the Member States. Another issue is that the long-term research should also get space it has not been the case in the present FCH JU. There are several Swedish participants in the projects and in the working groups of the program. There are Swedish participants in 11 of the 68 projects launched so far. It is in the areas of Stationary systems, Transportation and Early Markets. Project manager for the project FCGEN is Volvo Technology AB. FCH JU has its own website, www.fch-ju.eu, which opened in 2010 when the organization of the program was taken over from the Commission to permanent organisation

  14. Classification of Astaxanthin Colouration of Salmonid Fish using Spectral Imaging and Tricolour Measurement

    DEFF Research Database (Denmark)

    Ljungqvist, Martin Georg; Dissing, Bjørn Skovlund; Nielsen, Michael Engelbrecht

    capturing, tricolour CIELAB measurement, and manual SalmoFan inspection. Furthermore it was tested whether the best predictions come from measurements of the steak or the fillet of the fish. Methods used for classication were linear discriminant analysis (LDA), quadratic discriminant analysis (QDA......The goal of this study was to investigate if it is possible to differentiate between rainbow trout (Oncorhynchus mykiss) having been fed with natural or synthetic astaxanthin. Three different techniques were used for visual inspection of the surface colour of the fish meat: multi-spectral image...

  15. STRUCTURE AND DYNAMICS OF BOREAL ECOSYSTEMS: ANOTHER APPROACH TO LANDSAT IMAGERY CLASSIFICATION

    Directory of Open Access Journals (Sweden)

    P. Litinsky

    2017-01-01

    Full Text Available An alternative approach to information extraction from Landsat TM/ETM+ imagery is proposed. It involves transformation the image space into visible 3D form and comparing location in this space the segments of the ecosystem types with expressed graphically typology of forest and mire cover (biogeocenotic scheme. The model is built in LC1-LC2-MSI axis (the two first principal components of the image matrix in logarithmic form and moisture stress index. Comparing to Tasseled Cap, this transformation is more suitable for study area (north taiga zone of Eastern Fennoscandia. The spectral segments of mature and old-growth forests line up from the ecological optimum (moraine hills along two main environmental gradients: i lack of water and nutrition (fluvioglacial sands bedrock and ii degree of paludication (lacustrine plains. Thus, the biogeocenotic complexes are identified. The succession trajectories of forest regeneration through spectral space are also associated with the type of Quaternary deposits. For mire ecosystems spectral classes accurately reflect the type of water and mineral nutrition (ombrotrophic or mesotrophic. Spectral space model created using measured by the scanner physical ecosystem characteristics can be the base for developing objective classification of boreal ecosystems, where one of the most significant clustering criterions is the position in the spectral space.

  16. Multispectral and Panchromatic used Enhancement Resolution and Study Effective Enhancement on Supervised and Unsupervised Classification Land – Cover

    Science.gov (United States)

    Salman, S. S.; Abbas, W. A.

    2018-05-01

    The goal of the study is to support analysis Enhancement of Resolution and study effect on classification methods on bands spectral information of specific and quantitative approaches. In this study introduce a method to enhancement resolution Landsat 8 of combining the bands spectral of 30 meters resolution with panchromatic band 8 of 15 meters resolution, because of importance multispectral imagery to extracting land - cover. Classification methods used in this study to classify several lands -covers recorded from OLI- 8 imagery. Two methods of Data mining can be classified as either supervised or unsupervised. In supervised methods, there is a particular predefined target, that means the algorithm learn which values of the target are associated with which values of the predictor sample. K-nearest neighbors and maximum likelihood algorithms examine in this work as supervised methods. In other hand, no sample identified as target in unsupervised methods, the algorithm of data extraction searches for structure and patterns between all the variables, represented by Fuzzy C-mean clustering method as one of the unsupervised methods, NDVI vegetation index used to compare the results of classification method, the percent of dense vegetation in maximum likelihood method give a best results.

  17. Classification of Herbaceous Vegetation Using Airborne Hyperspectral Imagery

    Directory of Open Access Journals (Sweden)

    Péter Burai

    2015-02-01

    Full Text Available Alkali landscapes hold an extremely fine-scale mosaic of several vegetation types, thus it seems challenging to separate these classes by remote sensing. Our aim was to test the applicability of different image classification methods of hyperspectral data in this complex situation. To reach the highest classification accuracy, we tested traditional image classifiers (maximum likelihood classifier—MLC, machine learning algorithms (support vector machine—SVM, random forest—RF and feature extraction (minimum noise fraction (MNF-transformation on training datasets of different sizes. Digital images were acquired from an AISA EAGLE II hyperspectral sensor of 128 contiguous bands (400–1000 nm, a spectral sampling of 5 nm bandwidth and a ground pixel size of 1 m. For the classification, we established twenty vegetation classes based on the dominant species, canopy height, and total vegetation cover. Image classification was applied to the original and MNF (minimum noise fraction transformed dataset with various training sample sizes between 10 and 30 pixels. In order to select the optimal number of the transformed features, we applied SVM, RF and MLC classification to 2–15 MNF transformed bands. In the case of the original bands, SVM and RF classifiers provided high accuracy irrespective of the number of the training pixels. We found that SVM and RF produced the best accuracy when using the first nine MNF transformed bands; involving further features did not increase classification accuracy. SVM and RF provided high accuracies with the transformed bands, especially in the case of the aggregated groups. Even MLC provided high accuracy with 30 training pixels (80.78%, but the use of a smaller training dataset (10 training pixels significantly reduced the accuracy of classification (52.56%. Our results suggest that in alkali landscapes, the application of SVM is a feasible solution, as it provided the highest accuracies compared to RF and MLC

  18. Remote Sensing Image Fusion at the Segment Level Using a Spatially-Weighted Approach: Applications for Land Cover Spectral Analysis and Mapping

    Directory of Open Access Journals (Sweden)

    Brian Johnson

    2015-01-01

    Full Text Available Segment-level image fusion involves segmenting a higher spatial resolution (HSR image to derive boundaries of land cover objects, and then extracting additional descriptors of image segments (polygons from a lower spatial resolution (LSR image. In past research, an unweighted segment-level fusion (USF approach, which extracts information from a resampled LSR image, resulted in more accurate land cover classification than the use of HSR imagery alone. However, simply fusing the LSR image with segment polygons may lead to significant errors due to the high level of noise in pixels along the segment boundaries (i.e., pixels containing multiple land cover types. To mitigate this, a spatially-weighted segment-level fusion (SWSF method was proposed for extracting descriptors (mean spectral values of segments from LSR images. SWSF reduces the weights of LSR pixels located on or near segment boundaries to reduce errors in the fusion process. Compared to the USF approach, SWSF extracted more accurate spectral properties of land cover objects when the ratio of the LSR image resolution to the HSR image resolution was greater than 2:1, and SWSF was also shown to increase classification accuracy. SWSF can be used to fuse any type of imagery at the segment level since it is insensitive to spectral differences between the LSR and HSR images (e.g., different spectral ranges of the images or different image acquisition dates.

  19. AUTOMATED UNSUPERVISED CLASSIFICATION OF THE SLOAN DIGITAL SKY SURVEY STELLAR SPECTRA USING k-MEANS CLUSTERING

    Energy Technology Data Exchange (ETDEWEB)

    Sanchez Almeida, J.; Allende Prieto, C., E-mail: jos@iac.es, E-mail: callende@iac.es [Instituto de Astrofisica de Canarias, E-38205 La Laguna, Tenerife (Spain)

    2013-01-20

    Large spectroscopic surveys require automated methods of analysis. This paper explores the use of k-means clustering as a tool for automated unsupervised classification of massive stellar spectral catalogs. The classification criteria are defined by the data and the algorithm, with no prior physical framework. We work with a representative set of stellar spectra associated with the Sloan Digital Sky Survey (SDSS) SEGUE and SEGUE-2 programs, which consists of 173,390 spectra from 3800 to 9200 A sampled on 3849 wavelengths. We classify the original spectra as well as the spectra with the continuum removed. The second set only contains spectral lines, and it is less dependent on uncertainties of the flux calibration. The classification of the spectra with continuum renders 16 major classes. Roughly speaking, stars are split according to their colors, with enough finesse to distinguish dwarfs from giants of the same effective temperature, but with difficulties to separate stars with different metallicities. There are classes corresponding to particular MK types, intrinsically blue stars, dust-reddened, stellar systems, and also classes collecting faulty spectra. Overall, there is no one-to-one correspondence between the classes we derive and the MK types. The classification of spectra without continuum renders 13 classes, the color separation is not so sharp, but it distinguishes stars of the same effective temperature and different metallicities. Some classes thus obtained present a fairly small range of physical parameters (200 K in effective temperature, 0.25 dex in surface gravity, and 0.35 dex in metallicity), so that the classification can be used to estimate the main physical parameters of some stars at a minimum computational cost. We also analyze the outliers of the classification. Most of them turn out to be failures of the reduction pipeline, but there are also high redshift QSOs, multiple stellar systems, dust-reddened stars, galaxies, and, finally, odd

  20. Using spectral imaging for the analysis of abnormalities for colorectal cancer: When is it helpful?

    Science.gov (United States)

    Awan, Ruqayya; Al-Maadeed, Somaya; Al-Saady, Rafif

    2018-01-01

    The spectral imaging technique has been shown to provide more discriminative information than the RGB images and has been proposed for a range of problems. There are many studies demonstrating its potential for the analysis of histopathology images for abnormality detection but there have been discrepancies among previous studies as well. Many multispectral based methods have been proposed for histopathology images but the significance of the use of whole multispectral cube versus a subset of bands or a single band is still arguable. We performed comprehensive analysis using individual bands and different subsets of bands to determine the effectiveness of spectral information for determining the anomaly in colorectal images. Our multispectral colorectal dataset consists of four classes, each represented by infra-red spectrum bands in addition to the visual spectrum bands. We performed our analysis of spectral imaging by stratifying the abnormalities using both spatial and spectral information. For our experiments, we used a combination of texture descriptors with an ensemble classification approach that performed best on our dataset. We applied our method to another dataset and got comparable results with those obtained using the state-of-the-art method and convolutional neural network based method. Moreover, we explored the relationship of the number of bands with the problem complexity and found that higher number of bands is required for a complex task to achieve improved performance. Our results demonstrate a synergy between infra-red and visual spectrum by improving the classification accuracy (by 6%) on incorporating the infra-red representation. We also highlight the importance of how the dataset should be divided into training and testing set for evaluating the histopathology image-based approaches, which has not been considered in previous studies on multispectral histopathology images.

  1. HYPERSPECTRAL HYPERION IMAGERY ANALYSIS AND ITS APPLICATION USING SPECTRAL ANALYSIS

    Directory of Open Access Journals (Sweden)

    W. Pervez

    2015-03-01

    Full Text Available Rapid advancement in remote sensing open new avenues to explore the hyperspectral Hyperion imagery pre-processing techniques, analysis and application for land use mapping. The hyperspectral data consists of 242 bands out of which 196 calibrated/useful bands are available for hyperspectral applications. Atmospheric correction applied to the hyperspectral calibrated bands make the data more useful for its further processing/ application. Principal component (PC analysis applied to the hyperspectral calibrated bands reduced the dimensionality of the data and it is found that 99% of the data is held in first 10 PCs. Feature extraction is one of the important application by using vegetation delineation and normalized difference vegetation index. The machine learning classifiers uses the technique to identify the pixels having significant difference in the spectral signature which is very useful for classification of an image. Supervised machine learning classifier technique has been used for classification of hyperspectral image which resulted in overall efficiency of 86.6703 and Kappa co-efficient of 0.7998.

  2. Deep learning decision fusion for the classification of urban remote sensing data

    Science.gov (United States)

    Abdi, Ghasem; Samadzadegan, Farhad; Reinartz, Peter

    2018-01-01

    Multisensor data fusion is one of the most common and popular remote sensing data classification topics by considering a robust and complete description about the objects of interest. Furthermore, deep feature extraction has recently attracted significant interest and has become a hot research topic in the geoscience and remote sensing research community. A deep learning decision fusion approach is presented to perform multisensor urban remote sensing data classification. After deep features are extracted by utilizing joint spectral-spatial information, a soft-decision made classifier is applied to train high-level feature representations and to fine-tune the deep learning framework. Next, a decision-level fusion classifies objects of interest by the joint use of sensors. Finally, a context-aware object-based postprocessing is used to enhance the classification results. A series of comparative experiments are conducted on the widely used dataset of 2014 IEEE GRSS data fusion contest. The obtained results illustrate the considerable advantages of the proposed deep learning decision fusion over the traditional classifiers.

  3. Improving the Classification Accuracy for Near-Infrared Spectroscopy of Chinese Salvia miltiorrhiza Using Local Variable Selection

    Directory of Open Access Journals (Sweden)

    Lianqing Zhu

    2018-01-01

    Full Text Available In order to improve the classification accuracy of Chinese Salvia miltiorrhiza using near-infrared spectroscopy, a novel local variable selection strategy is thus proposed. Combining the strengths of the local algorithm and interval partial least squares, the spectra data have firstly been divided into several pairs of classes in sample direction and equidistant subintervals in variable direction. Then, a local classification model has been built, and the most proper spectral region has been selected based on the new evaluation criterion considering both classification error rate and best predictive ability under the leave-one-out cross validation scheme for each pair of classes. Finally, each observation can be assigned to belong to the class according to the statistical analysis of classification results of the local classification model built on selected variables. The performance of the proposed method was demonstrated through near-infrared spectra of cultivated or wild Salvia miltiorrhiza, which are collected from 8 geographical origins in 5 provinces of China. For comparison, soft independent modelling of class analogy and partial least squares discriminant analysis methods are, respectively, employed as the classification model. Experimental results showed that classification performance of the classification model with local variable selection was obvious better than that without variable selection.

  4. Physical and Hydrological Meaning of the Spectral Information from Hydrodynamic Signals at Karst Springs

    Science.gov (United States)

    Dufoyer, A.; Lecoq, N.; Massei, N.; Marechal, J. C.

    2017-12-01

    Physics-based modeling of karst systems remains almost impossible without enough accurate information about the inner physical characteristics. Usually, the only available hydrodynamic information is the flow rate at the karst outlet. Numerous works in the past decades have used and proven the usefulness of time-series analysis and spectral techniques applied to spring flow, precipitations or even physico-chemical parameters, for interpreting karst hydrological functioning. However, identifying or interpreting the karst systems physical features that control statistical or spectral characteristics of spring flow variations is still challenging, not to say sometimes controversial. The main objective of this work is to determine how the statistical and spectral characteristics of the hydrodynamic signal at karst springs can be related to inner physical and hydraulic properties. In order to address this issue, we undertake an empirical approach based on the use of both distributed and physics-based models, and on synthetic systems responses. The first step of the research is to conduct a sensitivity analysis of time-series/spectral methods to karst hydraulic and physical properties. For this purpose, forward modeling of flow through several simple, constrained and synthetic cases in response to precipitations is undertaken. It allows us to quantify how the statistical and spectral characteristics of flow at the outlet are sensitive to changes (i) in conduit geometries, and (ii) in hydraulic parameters of the system (matrix/conduit exchange rate, matrix hydraulic conductivity and storativity). The flow differential equations resolved by MARTHE, a computer code developed by the BRGM, allows karst conduits modeling. From signal processing on simulated spring responses, we hope to determine if specific frequencies are always modified, thanks to Fourier series and multi-resolution analysis. We also hope to quantify which parameters are the most variable with auto

  5. Understanding Soliton Spectral Tunneling as a Spectral Coupling Effect

    DEFF Research Database (Denmark)

    Guo, Hairun; Wang, Shaofei; Zeng, Xianglong

    2013-01-01

    Soliton eigenstate is found corresponding to a dispersive phase profile under which the soliton phase changes induced by the dispersion and nonlinearity are instantaneously counterbalanced. Much like a waveguide coupler relying on a spatial refractive index profile that supports mode coupling...... between channels, here we suggest that the soliton spectral tunneling effect can be understood supported by a spectral phase coupler. The dispersive wave number in the spectral domain must have a coupler-like symmetric profile for soliton spectral tunneling to occur. We show that such a spectral coupler...

  6. Sound Classification in Hearing Aids Inspired by Auditory Scene Analysis

    Science.gov (United States)

    Büchler, Michael; Allegro, Silvia; Launer, Stefan; Dillier, Norbert

    2005-12-01

    A sound classification system for the automatic recognition of the acoustic environment in a hearing aid is discussed. The system distinguishes the four sound classes "clean speech," "speech in noise," "noise," and "music." A number of features that are inspired by auditory scene analysis are extracted from the sound signal. These features describe amplitude modulations, spectral profile, harmonicity, amplitude onsets, and rhythm. They are evaluated together with different pattern classifiers. Simple classifiers, such as rule-based and minimum-distance classifiers, are compared with more complex approaches, such as Bayes classifier, neural network, and hidden Markov model. Sounds from a large database are employed for both training and testing of the system. The achieved recognition rates are very high except for the class "speech in noise." Problems arise in the classification of compressed pop music, strongly reverberated speech, and tonal or fluctuating noises.

  7. Improved classification of small-scale urban watersheds using thematic mapper simulator data

    Science.gov (United States)

    Owe, M.; Ormsby, J. P.

    1984-01-01

    The utility of Landsat MSS classification methods in the case of small, highly urbanized hydrological basins containing complex land-use patterns is limited, and is plagued by misclassifications due to the spectral response similarity of many dissimilar surfaces. Landsat MSS data for the Conley Creek basin near Atlanta, Georgia, have been compared to thematic mapper simulator (TMS) data obtained on the same day by aircraft. The TMS data were able to alleviate many of the recurring patterns associated with MSS data, through bandwidth optimization, an increase of the number of spectral bands to seven, and an improvement of ground resolution to 30 m. The TMS is thereby able to detect small water bodies, powerline rights-of-way, and even individual buildings.

  8. Improved documentation of spectral lines for inductively coupled plasma emission spectrometry

    Science.gov (United States)

    Doidge, Peter S.

    2018-05-01

    An approach to improving the documentation of weak spectral lines falling near the prominent analytical lines used in inductively coupled plasma optical emission spectrometry (ICP-OES) is described. Measurements of ICP emission spectra in the regions around several hundred prominent lines, using concentrated solutions (up to 1% w/v) of some 70 elements, and comparison of the observed spectra with both recent published work and with the output of a computer program that allows calculation of transitions between the known energy levels, show that major improvements can be made in the coverage of spectral atlases for ICP-OES, with respect to "classical" line tables. It is argued that the atomic spectral data (wavelengths, energy levels) required for the reliable identification and documentation of a large majority of the weak interfering lines of the elements detectable by ICP-OES now exist, except for most of the observed lines of the lanthanide elements. In support of this argument, examples are provided from a detailed analysis of a spectral window centered on the prominent Pb II 220.353 nm line, and from a selected line-rich spectrum (W). Shortcomings in existing analyses are illustrated with reference to selected spectral interferences due to Zr. This approach has been used to expand the spectral-line library used in commercial ICP-ES instruments (Agilent 700-ES/5100-ES). The precision of wavelength measurements is evaluated in terms of the shot-noise limit, while the absolute accuracy of wavelength measurement is characterised through comparison with a small set of precise Ritz wavelengths for Sb I, and illustrated through the identification of Zr III lines; it is further shown that fractional-pixel absolute wavelength accuracies can be achieved. Finally, problems with the wavelengths and classifications of certain Au I lines are discussed.

  9. Farmers prone to drought risk : why some farmers undertake farm-level risk-reduction measures while others not?

    NARCIS (Netherlands)

    Gidey, T.G.; van der Veen, A.

    2015-01-01

    This research investigates farmers’ cognitive perceptions of risk and the behavioral intentions to undertake farm-level risk-reduction measures. It has been observed that people who are susceptible to natural hazards often fail to act, or do very little, to protect their assets or lives. To answer

  10. Piecewise spectrally band-pass for compressive coded aperture spectral imaging

    International Nuclear Information System (INIS)

    Qian Lu-Lu; Lü Qun-Bo; Huang Min; Xiang Li-Bin

    2015-01-01

    Coded aperture snapshot spectral imaging (CASSI) has been discussed in recent years. It has the remarkable advantages of high optical throughput, snapshot imaging, etc. The entire spatial-spectral data-cube can be reconstructed with just a single two-dimensional (2D) compressive sensing measurement. On the other hand, for less spectrally sparse scenes, the insufficiency of sparse sampling and aliasing in spatial-spectral images reduce the accuracy of reconstructed three-dimensional (3D) spectral cube. To solve this problem, this paper extends the improved CASSI. A band-pass filter array is mounted on the coded mask, and then the first image plane is divided into some continuous spectral sub-band areas. The entire 3D spectral cube could be captured by the relative movement between the object and the instrument. The principle analysis and imaging simulation are presented. Compared with peak signal-to-noise ratio (PSNR) and the information entropy of the reconstructed images at different numbers of spectral sub-band areas, the reconstructed 3D spectral cube reveals an observable improvement in the reconstruction fidelity, with an increase in the number of the sub-bands and a simultaneous decrease in the number of spectral channels of each sub-band. (paper)

  11. Classifying Classifications

    DEFF Research Database (Denmark)

    Debus, Michael S.

    2017-01-01

    This paper critically analyzes seventeen game classifications. The classifications were chosen on the basis of diversity, ranging from pre-digital classification (e.g. Murray 1952), over game studies classifications (e.g. Elverdam & Aarseth 2007) to classifications of drinking games (e.g. LaBrie et...... al. 2013). The analysis aims at three goals: The classifications’ internal consistency, the abstraction of classification criteria and the identification of differences in classification across fields and/or time. Especially the abstraction of classification criteria can be used in future endeavors...... into the topic of game classifications....

  12. Cloud-based processing of multi-spectral imaging data

    Science.gov (United States)

    Bernat, Amir S.; Bolton, Frank J.; Weiser, Reuven; Levitz, David

    2017-03-01

    Multispectral imaging holds great promise as a non-contact tool for the assessment of tissue composition. Performing multi - spectral imaging on a hand held mobile device would allow to bring this technology and with it knowledge to low resource settings to provide a state of the art classification of tissue health. This modality however produces considerably larger data sets than white light imaging and requires preliminary image analysis for it to be used. The data then needs to be analyzed and logged, while not requiring too much of the system resource or a long computation time and battery use by the end point device. Cloud environments were designed to allow offloading of those problems by allowing end point devices (smartphones) to offload computationally hard tasks. For this end we present a method where the a hand held device based around a smartphone captures a multi - spectral dataset in a movie file format (mp4) and compare it to other image format in size, noise and correctness. We present the cloud configuration used for segmenting images to frames where they can later be used for further analysis.

  13. Multi-stage classification method oriented to aerial image based on low-rank recovery and multi-feature fusion sparse representation.

    Science.gov (United States)

    Ma, Xu; Cheng, Yongmei; Hao, Shuai

    2016-12-10

    Automatic classification of terrain surfaces from an aerial image is essential for an autonomous unmanned aerial vehicle (UAV) landing at an unprepared site by using vision. Diverse terrain surfaces may show similar spectral properties due to the illumination and noise that easily cause poor classification performance. To address this issue, a multi-stage classification algorithm based on low-rank recovery and multi-feature fusion sparse representation is proposed. First, color moments and Gabor texture feature are extracted from training data and stacked as column vectors of a dictionary. Then we perform low-rank matrix recovery for the dictionary by using augmented Lagrange multipliers and construct a multi-stage terrain classifier. Experimental results on an aerial map database that we prepared verify the classification accuracy and robustness of the proposed method.

  14. ULTRAVIOLET RAMAN SPECTRAL SIGNATURE ACQUISITION: UV RAMAN SPECTRAL FINGERPRINTS.

    Energy Technology Data Exchange (ETDEWEB)

    SEDLACEK,III, A.J.FINFROCK,C.

    2002-09-01

    As a member of the science-support part of the ITT-lead LISA development program, BNL is tasked with the acquisition of UV Raman spectral fingerprints and associated scattering cross-sections for those chemicals-of-interest to the program's sponsor. In support of this role, the present report contains the first installment of UV Raman spectral fingerprint data on the initial subset of chemicals. Because of the unique nature associated with the acquisition of spectral fingerprints for use in spectral pattern matching algorithms (i.e., CLS, PLS, ANN) great care has been undertaken to maximize the signal-to-noise and to minimize unnecessary spectral subtractions, in an effort to provide the highest quality spectral fingerprints. This report is divided into 4 sections. The first is an Experimental section that outlines how the Raman spectra are performed. This is then followed by a section on Sample Handling. Following this, the spectral fingerprints are presented in the Results section where the data reduction process is outlined. Finally, a Photographs section is included.

  15. The SAGE-Spec Spitzer Legacy program: the life-cycle of dust and gas in the Large Magellanic Cloud. Point source classification - III

    Science.gov (United States)

    Jones, O. C.; Woods, P. M.; Kemper, F.; Kraemer, K. E.; Sloan, G. C.; Srinivasan, S.; Oliveira, J. M.; van Loon, J. Th.; Boyer, M. L.; Sargent, B. A.; McDonald, I.; Meixner, M.; Zijlstra, A. A.; Ruffle, P. M. E.; Lagadec, E.; Pauly, T.; Sewiło, M.; Clayton, G. C.; Volk, K.

    2017-09-01

    The Infrared Spectrograph (IRS) on the Spitzer Space Telescope observed nearly 800 point sources in the Large Magellanic Cloud (LMC), taking over 1000 spectra. 197 of these targets were observed as part of the SAGE-Spec Spitzer Legacy program; the remainder are from a variety of different calibration, guaranteed time and open time projects. We classify these point sources into types according to their infrared spectral features, continuum and spectral energy distribution shape, bolometric luminosity, cluster membership and variability information, using a decision-tree classification method. We then refine the classification using supplementary information from the astrophysical literature. We find that our IRS sample is comprised substantially of YSO and H II regions, post-main-sequence low-mass stars: (post-)asymptotic giant branch stars and planetary nebulae and massive stars including several rare evolutionary types. Two supernova remnants, a nova and several background galaxies were also observed. We use these classifications to improve our understanding of the stellar populations in the LMC, study the composition and characteristics of dust species in a variety of LMC objects, and to verify the photometric classification methods used by mid-IR surveys. We discover that some widely used catalogues of objects contain considerable contamination and others are missing sources in our sample.

  16. Supporting students undertaking the Specialist Practitioner Qualification in District Nursing.

    Science.gov (United States)

    Ginger, Tracey; Ritchie, Georgina

    2017-11-02

    The ever-evolving role of the Specialist Practitioner Qualified District Nurse (SPQDN) presents an increasing number of challenges for Practice Teachers and mentors in preparing SPQDN students for the elevated level clinical and transformational leadership necessary to ensure high-quality patient care. The daily challenges of clinical practice within the community nursing setting in addition to undertaking educational interventions in the clinical arena demand that a structured approach to supervision and mentorship is crucial. Employing learning plans to assess individual students learning needs, prepare plans for educational developments and interventions and evaluate a student's progress can be a helpful tool in aiding the learning journey for both the SPQDN student and Practice Teacher or mentor. This article examines how and why a structured learning plan may be used in supporting learning and competency in achieving the necessary level of practice to meet the requirements of the SPQDN.

  17. An efficient swarm intelligence approach to feature selection based on invasive weed optimization: Application to multivariate calibration and classification using spectroscopic data

    Science.gov (United States)

    Sheykhizadeh, Saheleh; Naseri, Abdolhossein

    2018-04-01

    Variable selection plays a key role in classification and multivariate calibration. Variable selection methods are aimed at choosing a set of variables, from a large pool of available predictors, relevant to the analyte concentrations estimation, or to achieve better classification results. Many variable selection techniques have now been introduced among which, those which are based on the methodologies of swarm intelligence optimization have been more respected during a few last decades since they are mainly inspired by nature. In this work, a simple and new variable selection algorithm is proposed according to the invasive weed optimization (IWO) concept. IWO is considered a bio-inspired metaheuristic mimicking the weeds ecological behavior in colonizing as well as finding an appropriate place for growth and reproduction; it has been shown to be very adaptive and powerful to environmental changes. In this paper, the first application of IWO, as a very simple and powerful method, to variable selection is reported using different experimental datasets including FTIR and NIR data, so as to undertake classification and multivariate calibration tasks. Accordingly, invasive weed optimization - linear discrimination analysis (IWO-LDA) and invasive weed optimization- partial least squares (IWO-PLS) are introduced for multivariate classification and calibration, respectively.

  18. On a classification of irreducible almost-commutative geometries IV

    International Nuclear Information System (INIS)

    Jureit, Jan-Hendrik; Stephan, Christoph A.

    2008-01-01

    In this paper, we will classify the finite spectral triples with KO-dimension 6, following the classification found in Iochum, B., Schuecker, T., and Stephan, C. A., J. Math. Phys. 45, 5003 (2004); Jureit, J.-H. and Stephan, C. A., J. Math. Phys. 46, 043512 (2005); Schuecker, T. (unpublished); Jureit, J.-H., Schuecker, T., and Stephan, C. A., J. Math. Phys. 46, 072302 (2005). with up to four summands in the matrix algebra. Again, heavy use is made of Krajewski diagrams [Krajewski, T., J. Geom. Phys. 28, 1 (1998).] This work has been inspired by the recent paper by Connes (unpublished) and Barrett (unpublished). In the classification, we find that the standard model of particle physics in its minimal version fits the axioms of noncommutative geometry in the case of KO-dimension 6. By minimal version, it is meant that at least one neutrino has to be massless and mass-terms mixing particles and antiparticles are prohibited

  19. Feature ranking and rank aggregation for automatic sleep stage classification: a comparative study.

    Science.gov (United States)

    Najdi, Shirin; Gharbali, Ali Abdollahi; Fonseca, José Manuel

    2017-08-18

    Nowadays, sleep quality is one of the most important measures of healthy life, especially considering the huge number of sleep-related disorders. Identifying sleep stages using polysomnographic (PSG) signals is the traditional way of assessing sleep quality. However, the manual process of sleep stage classification is time-consuming, subjective and costly. Therefore, in order to improve the accuracy and efficiency of the sleep stage classification, researchers have been trying to develop automatic classification algorithms. Automatic sleep stage classification mainly consists of three steps: pre-processing, feature extraction and classification. Since classification accuracy is deeply affected by the extracted features, a poor feature vector will adversely affect the classifier and eventually lead to low classification accuracy. Therefore, special attention should be given to the feature extraction and selection process. In this paper the performance of seven feature selection methods, as well as two feature rank aggregation methods, were compared. Pz-Oz EEG, horizontal EOG and submental chin EMG recordings of 22 healthy males and females were used. A comprehensive feature set including 49 features was extracted from these recordings. The extracted features are among the most common and effective features used in sleep stage classification from temporal, spectral, entropy-based and nonlinear categories. The feature selection methods were evaluated and compared using three criteria: classification accuracy, stability, and similarity. Simulation results show that MRMR-MID achieves the highest classification performance while Fisher method provides the most stable ranking. In our simulations, the performance of the aggregation methods was in the average level, although they are known to generate more stable results and better accuracy. The Borda and RRA rank aggregation methods could not outperform significantly the conventional feature ranking methods. Among

  20. Spectral Classification of Galaxies at 0.5 <= z <= 1 in the CDFS: The Artificial Neural Network Approach

    Science.gov (United States)

    Teimoorinia, H.

    2012-12-01

    The aim of this work is to combine spectral energy distribution (SED) fitting with artificial neural network techniques to assign spectral characteristics to a sample of galaxies at 0.5 MUSIC catalog covering bands between ~0.4 and 24 μm in 10-13 filters. We use the CIGALE code to fit photometric data to Maraston's synthesis spectra to derive mass, specific star formation rate, and age, as well as the best SED of the galaxies. We use the spectral models presented by Kinney et al. as targets in the wavelength interval ~1200-7500 Å. Then a series of neural networks are trained, with average performance ~90%, to classify the best SED in a supervised manner. We consider the effects of the prominent features of the best SED on the performance of the trained networks and also test networks on the galaxy spectra of Coleman et al., which have a lower resolution than the target models. In this way, we conclude that the trained networks take into account all the features of the spectra simultaneously. Using the method, 105 out of 142 galaxies of the sample are classified with high significance. The locus of the classified galaxies in the three graphs of the physical parameters of mass, age, and specific star formation rate appears consistent with the morphological characteristics of the galaxies.

  1. NSCC-A NEW SCHEME OF CLASSIFICATION OF C-RICH STARS DEVISED FROM OPTICAL AND INFRARED OBSERVATIONS

    International Nuclear Information System (INIS)

    De Mello, A. B.; De Araujo, F. X.; Pereira, C. Bastos; Landaberry, S. J. Codina; Lorenz-Martins, S.

    2009-01-01

    A new classification system for carbon-rich stars is presented based on an analysis of 51 asymptotic giant branch carbon stars through the most relevant classifying indices available. The extension incorporated, which also represents the major advantage of this new system, is the combination of the usual optical indices that describe the photospheres of the objects, with new infrared ones, which allow an interpretation of the circumstellar environment of the carbon-rich stars. This new system is presented with the usual spectral subclasses and C 2 -, j-, MS-, and temperature indices, and also with the new SiC- (SiC/C.A. abundance estimation) and τ- (opacity) indices. The values for the infrared indices were carried out through a Monte Carlo simulation of the radiative transfer in the circumstellar envelopes of the stars. The full set of indices, when applied to our sample, resulted in a more efficient system of classification, since an examination in a wide spectral range allows us to obtain a complete scenario for carbon stars.

  2. Classification of permafrost active layer depth from remotely sensed and topographic evidence

    International Nuclear Information System (INIS)

    Peddle, D.R.; Franklin, S.E.

    1993-01-01

    The remote detection of permafrost (perennially frozen ground) has important implications to environmental resource development, engineering studies, natural hazard prediction, and climate change research. In this study, the authors present results from two experiments into the classification of permafrost active layer depth within the zone of discontinuous permafrost in northern Canada. A new software system based on evidential reasoning was implemented to permit the integrated classification of multisource data consisting of landcover, terrain aspect, and equivalent latitude, each of which possessed different formats, data types, or statistical properties that could not be handled by conventional classification algorithms available to this study. In the first experiment, four active layer depth classes were classified using ground based measurements of the three variables with an accuracy of 83% compared to in situ soil probe determination of permafrost active layer depth at over 500 field sites. This confirmed the environmental significance of the variables selected, and provided a baseline result to which a remote sensing classification could be compared. In the second experiment, evidence for each input variable was obtained from image processing of digital SPOT imagery and a photogrammetric digital elevation model, and used to classify active layer depth with an accuracy of 79%. These results suggest the classification of evidence from remotely sensed measures of spectral response and topography may provide suitable indicators of permafrost active layer depth

  3. Dichotomous classification of black-colored metal using spectral analysis

    Directory of Open Access Journals (Sweden)

    Abramovich A.O.

    2017-05-01

    Full Text Available The task of detecting metal objects in different environments has always been important. To solve it metal detectors are used. They are designed to detect and identify objects that in their electric or magnetic properties different from the environment in which they are located. The most common among them are the metal detectors of the «detection of very low frequency» type (Very Low Frequency (VLF detectors. They use eddy current testing for detecting metal targets, which solves the problem of dichotomous distinction, that is a problem of splitting (or set into two parts (subsets: black or colored target. The target distinction is performed by a threshold level of the received signal. However, this approach does not allow to identify the type of target, if two samples of different metals are nearby. To overcome the above described limitations we propose another way of distinction based on the use of spectral analysis, which occurs in the metal detector antenna by Foucault current. We show that the problem of dichotomous distinction can be solved in just a measurement of width and area by the envelope of amplitude spectrum (hereinafter spectrum of the received signal. In this regard the laboratory model using eddy current metal detector will combat withdrawal from two samples – steel and copper, located along and calculate its range. The task of distinguishing between metal targets reduced to determining the hit spectra of reference samples obtained spectrum. The ratio between the areas is measured and reference spectra indicates the percentage of specific metals (e.g. two identical samples of different metals lying side by side. Signal processing is performed by specially designed program that compares two spectra along posted samples of black and colored metals with base.

  4. Ten-Year Landsat Classification of Deforestation and Forest Degradation in the Brazilian Amazon

    OpenAIRE

    Jr, Carlos Souza,; Siqueira, João; Sales, Marcio; Fonseca, Antônio; Ribeiro, Júlia; Numata, Izaya; Cochrane, Mark; Barber, Christopher; Roberts, Dar; Barlow, Jos

    2013-01-01

    Forest degradation in the Brazilian Amazon due to selective logging and forest fires may greatly increase the human footprint beyond outright deforestation. We demonstrate a method to quantify annual deforestation and degradation simultaneously across the entire region for the years 2000–2010 using high-resolution Landsat satellite imagery. Combining spectral mixture analysis, normalized difference fraction index, and knowledge-based decision tree classification, we mapped and assessed the ac...

  5. Spectral properties of eight near-Earth asteroids

    Science.gov (United States)

    Popescu, M.; Birlan, M.; Binzel, R.; Vernazza, P.; Barucci, A.; Nedelcu, D. A.; DeMeo, F.; Fulchignoni, M.

    2011-11-01

    Context. Near-Earth objects are among the most accessible bodies in the solar system in terms of the spacecraft propulsion requirements to reach them. The choice of targets and the planning of space missions are based on high quality ground-based science. Aims: The knowledge of the ensemble of physical parameters for these objects, including their composition, is a critical point in defining any mission scientific objectives. Determining the physical properties of near-Earth asteroids (NEAs) is also possible from the ground by analyzing spectroscopy at both visible and infrared wavelengths. Methods: We present spectra of eight NEAs (1917, 8567, 16960, 164400, 188452, 2001 SG286, and 2010 TD54) obtained using the NASA telescope IRTF equipped with the spectro-imager SpeX. The observations were performed in the 0.8-2.5 μm spectral region using the low resolution mode of the spectrograph. We completed the taxonomic classification using the Bus-DeMeo taxonomy. We analyzed the spectra by comparing them to meteorite spectra from the Relab database using a χ2 approach. For the S-type asteroids of our sample, the band centers and BAR were calculated. We also attempted to interpret our data using a space-weathering model. Results: The taxonomic classification of five objects was reviewed and we assigned a corresponding type to the other three asteroids that were not classified before. We found that (1917) Cuyo, (8567) 1996 HW1, (16960) 1998 QS52, (188452) 2004 HE62, and 2010 TD54 are in the S-complex. We achieved a good matching of our S-type asteroids with the spectra of ordinary chondrites meteorites. The asteroid (5620) Jasonwheeler was found to have a NIR spectrum similar to carbonaceous chondrite meteorites. Thus, our results confirm its primitive properties obtained in several other spectral intervals. Appendices A and B are available in electronic form at http://www.aanda.org

  6. A Color-Texture-Structure Descriptor for High-Resolution Satellite Image Classification

    Directory of Open Access Journals (Sweden)

    Huai Yu

    2016-03-01

    Full Text Available Scene classification plays an important role in understanding high-resolution satellite (HRS remotely sensed imagery. For remotely sensed scenes, both color information and texture information provide the discriminative ability in classification tasks. In recent years, substantial performance gains in HRS image classification have been reported in the literature. One branch of research combines multiple complementary features based on various aspects such as texture, color and structure. Two methods are commonly used to combine these features: early fusion and late fusion. In this paper, we propose combining the two methods under a tree of regions and present a new descriptor to encode color, texture and structure features using a hierarchical structure-Color Binary Partition Tree (CBPT, which we call the CTS descriptor. Specifically, we first build the hierarchical representation of HRS imagery using the CBPT. Then we quantize the texture and color features of dense regions. Next, we analyze and extract the co-occurrence patterns of regions based on the hierarchical structure. Finally, we encode local descriptors to obtain the final CTS descriptor and test its discriminative capability using object categorization and scene classification with HRS images. The proposed descriptor contains the spectral, textural and structural information of the HRS imagery and is also robust to changes in illuminant color, scale, orientation and contrast. The experimental results demonstrate that the proposed CTS descriptor achieves competitive classification results compared with state-of-the-art algorithms.

  7. VISIBE AND INFRARED SPECTRAL CHARACTERISATION OF CHINESE CABBAGE (BRASSICA RAPA L. SUBSPECIES CHINENSIS, GROWN UNDER DIFFERENT NITROGEN, POTASSIUM AND PHOSPHORUS CONCENTRATIONS

    Directory of Open Access Journals (Sweden)

    B. B. Mokoatsi

    2017-11-01

    Full Text Available There is a need to intensify research efforts on improving productivity of indigenous vegetables in South Africa. One research avenue is operationalizing remote sensing techniques to monitor crop health status. This study aimed at characterising the spectral properties of Chinese cabbage (Brassica Rapa L. subspecies Chinensis grown under varying fertilizer treatments: nitrogen (0 kg/ha, 75 kg/ha, 125 kg/ha, 175 kg/ha and 225 kg/ha, phosphorus (0 kg/ha, 9.4 kg/ha, 15.6, 21.9 kg/ha and 28.1 kg/ha and potassium (0 kg/ha, 9.4  kg/ha, 15.6 kg/ha, 21.9 kg/ha and 28.1 kg/ha. Visible and infrared spectral measurements were taken from a total of 60 samples inside the laboratory. Contiguous spectral regions were plotted to show spectral profiles of the different fertilizer treatments and then classified using gradient boosting and random forest classifiers. ANOVA revealed the potential of spectral reflectance data in discriminating different fertiliser treatments from crops. There was also a significant difference between the capabilities of the two classifiers. Gradient boost model (GBM yielded higher classification accuracies than random forest (RF. The important variables identified by each model improved the classification accuracy. Overall, the results indicate a potential for the use of spectroscopy in monitoring food quality parameters, thereby reducing the cost of traditional methods. Further research into advanced statistical analysis techniques is needed to improve the accuracy with which fertiliser concentrations in crops could be quantified. The random forest model particularly requires improvements.

  8. Object-Based Crop Species Classification Based on the Combination of Airborne Hyperspectral Images and LiDAR Data

    Directory of Open Access Journals (Sweden)

    Xiaolong Liu

    2015-01-01

    Full Text Available Identification of crop species is an important issue in agricultural management. In recent years, many studies have explored this topic using multi-spectral and hyperspectral remote sensing data. In this study, we perform dedicated research to propose a framework for mapping crop species by combining hyperspectral and Light Detection and Ranging (LiDAR data in an object-based image analysis (OBIA paradigm. The aims of this work were the following: (i to understand the performances of different spectral dimension-reduced features from hyperspectral data and their combination with LiDAR derived height information in image segmentation; (ii to understand what classification accuracies of crop species can be achieved by combining hyperspectral and LiDAR data in an OBIA paradigm, especially in regions that have fragmented agricultural landscape and complicated crop planting structure; and (iii to understand the contributions of the crop height that is derived from LiDAR data, as well as the geometric and textural features of image objects, to the crop species’ separabilities. The study region was an irrigated agricultural area in the central Heihe river basin, which is characterized by many crop species, complicated crop planting structures, and fragmented landscape. The airborne hyperspectral data acquired by the Compact Airborne Spectrographic Imager (CASI with a 1 m spatial resolution and the Canopy Height Model (CHM data derived from the LiDAR data acquired by the airborne Leica ALS70 LiDAR system were used for this study. The image segmentation accuracies of different feature combination schemes (very high-resolution imagery (VHR, VHR/CHM, and minimum noise fractional transformed data (MNF/CHM were evaluated and analyzed. The results showed that VHR/CHM outperformed the other two combination schemes with a segmentation accuracy of 84.8%. The object-based crop species classification results of different feature integrations indicated that

  9. Classification of rabbit meat obtained with industrial and organic breeding by means of spectrocolorimetric technique

    Science.gov (United States)

    Menesatti, P.; D'Andrea, S.; Negretti, P.

    2007-09-01

    Rabbit meat is for its nutritional characteristics a food corresponding to new models of consumption. Quality improvement is possible integrating an extensive organic breeding with suitable rabbit genetic typologies. Aim of this work (financed by a Project of the Lazio Region, Italy) was the characterization of rabbit meat by a statistic model, able to distinguish rabbit meat obtained by organic breeding from that achieved industrially. This was pursued through the analysis of spectral data and colorimetric values. Two genetic typologies of rabbit, Leprino Viterbese and a commercial hybrid, were studied. The Leprino Viterbese has been breeded with two different systems, organic and industrial. The commercial hybrid has been bred only industrially because of its characteristics of high sensibility to diseases. The device used for opto-electronic analysis is a VIS-NIR image spectrometer (range: 400-970 nm). The instrument has a stabilized light, it works in accordance to standard CIE L*a*b* technique and it measures the spectral reflectance and the colorimetric coordinates values. The statistic data analysis has been performed by Partial Least Square technique (PLS). A part of measured data was used to create the statistic model and the remaining data were utilized in phase of test to verify the correct model classification. The results put in evidence a high percentage of correct classification (90%) of the model for the two rabbit meat classes, deriving from organic and industrial breeding. Moreover, concerning the different genetic typologies, the percentage of correct classification was 90%.

  10. Classification of high-resolution remote sensing images based on multi-scale superposition

    Science.gov (United States)

    Wang, Jinliang; Gao, Wenjie; Liu, Guangjie

    2017-07-01

    Landscape structures and process on different scale show different characteristics. In the study of specific target landmarks, the most appropriate scale for images can be attained by scale conversion, which improves the accuracy and efficiency of feature identification and classification. In this paper, the authors carried out experiments on multi-scale classification by taking the Shangri-la area in the north-western Yunnan province as the research area and the images from SPOT5 HRG and GF-1 Satellite as date sources. Firstly, the authors upscaled the two images by cubic convolution, and calculated the optimal scale for different objects on the earth shown in images by variation functions. Then the authors conducted multi-scale superposition classification on it by Maximum Likelyhood, and evaluated the classification accuracy. The results indicates that: (1) for most of the object on the earth, the optimal scale appears in the bigger scale instead of the original one. To be specific, water has the biggest optimal scale, i.e. around 25-30m; farmland, grassland, brushwood, roads, settlement places and woodland follows with 20-24m. The optimal scale for shades and flood land is basically as the same as the original one, i.e. 8m and 10m respectively. (2) Regarding the classification of the multi-scale superposed images, the overall accuracy of the ones from SPOT5 HRG and GF-1 Satellite is 12.84% and 14.76% higher than that of the original multi-spectral images, respectively, and Kappa coefficient is 0.1306 and 0.1419 higher, respectively. Hence, the multi-scale superposition classification which was applied in the research area can enhance the classification accuracy of remote sensing images .

  11. Spectral classification of medium-scale high-latitude F region plasma density irregularities

    International Nuclear Information System (INIS)

    Singh, M.; Rodriguez, P.; Szuszczewicz, E.P.; Sachs Freeman Associates, Bowie, MD)

    1985-01-01

    The high-latitude ionosphere represents a highly structured plasma. Rodriguez and Szuszczewicz (1984) reported a wide range of plasma density irregularities (150 km to 75 m) at high latitudes near 200 km. They have shown that the small-scale irregularities (7.5 km to 75 m) populated the dayside oval more often than the other phenomenological regions. It was suggested that in the lower F region the chemical recombination is fast enough to remove small-scale irregularities before convection can transport them large distances, leaving structured particle precipitation as the dominant source term for irregularities. The present paper provides the results of spectral analyses of pulsed plasma probe data collected in situ aboard the STP/S3-4 satellite during the period March-September 1978. A quantitative description of irregularity spectra in the high-latitude lower F region plasma density is given. 22 references

  12. Detection and classification of salmonella serotypes using spectral signatures collected by fourier transform infrared (FT-IR) spectroscopy

    Science.gov (United States)

    Spectral signatures of Salmonella serotypes namely Salmonella Typhimurium, Salmonella Enteritidis, Salmonella Infantis, Salmonella Heidelberg and Salmonella Kentucky were collected using Fourier transform infrared spectroscopy (FT-IR). About 5-10 µL of Salmonella suspensions with concentrations of 1...

  13. A HIGH-RESOLUTION, MULTI-EPOCH SPECTRAL ATLAS OF PECULIAR STARS INCLUDING RAVE, GAIA , AND HERMES WAVELENGTH RANGES

    International Nuclear Information System (INIS)

    Tomasella, Lina; Munari, Ulisse; Zwitter, Tomaz

    2010-01-01

    We present an Echelle+CCD, high signal-to-noise ratio, high-resolution (R = 20,000) spectroscopic atlas of 108 well-known objects representative of the most common types of peculiar and variable stars. The wavelength interval extends from 4600 to 9400 A and includes the RAVE, Gaia, and HERMES wavelength ranges. Multi-epoch spectra are provided for the majority of the observed stars. A total of 425 spectra of peculiar stars, which were collected during 56 observing nights between 1998 November and 2002 August, are presented. The spectra are given in FITS format and heliocentric wavelengths, with accurate subtraction of both the sky background and the scattered light. Auxiliary material useful for custom applications (telluric dividers, spectrophotometric stars, flat-field tracings) is also provided. The atlas aims to provide a homogeneous database of the spectral appearance of stellar peculiarities, a tool useful both for classification purposes and inter-comparison studies. It could also serve in the planning and development of automated classification algorithms designed for RAVE, Gaia, HERMES, and other large-scale spectral surveys. The spectrum of XX Oph is discussed in some detail as an example of the content of the present atlas.

  14. CLASSIFICATION AND QUANTITATIVE ANALYSIS OF GEOGRAPHIC ATROPHY JUNCTIONAL ZONE USING SPECTRAL DOMAIN OPTICAL COHERENCE TOMOGRAPHY.

    Science.gov (United States)

    Qu, Jinfeng; Velaga, Swetha Bindu; Hariri, Amir H; Nittala, Muneeswar Gupta; Sadda, Srinivas

    2017-08-22

    The junctional zone at the border of areas of geographic atrophy (GA) in eyes with nonneovascular age-related macular degeneration is an important target region for future therapeutic strategies. The goal of this study was to perform a detailed classification and quantitative characterization of the junctional zone using spectral domain optical coherence tomography. Spectral domain optical coherence tomography volume cube scans (Spectralis OCT, 1024 × 37, Automatic Real Time > 9) were obtained from 15 eyes of 11 patients with GA because of nonneovascular age-related macular degeneration. Volume optical coherence tomography data were imported into previously described validated grading software (3D-OCTOR), and manual segmentation of the retinal pigment epithelium (RPE) and photoreceptor layers was performed on all B-scans (total of 555). Retinal pigment epithelium and photoreceptor defect maps were produced for each case. The borders of the photoreceptor defect area and RPE defect area were delineated individually on separate annotation layers. The two outlines were then superimposed to compare the areas of overlap and nonoverlap. The perimeter of the RPE defect area was calculated by the software in pixels. The superimposed outline of the photoreceptor defect area and the RPE defect area was scrutinized to classify the overlap configuration of the junctional zone into one of three categories: Type 0, exact correspondence between the edge of the RPE defect and photoreceptor defect; Type 1, loss of photoreceptors outside and beyond the edge of the RPE defect; Type 2, preservation of photoreceptors beyond the edge of the RPE defect. The relative proportion of the various border configurations was expressed as a percentage of the perimeter of the RPE defect. Each configuration was then classified into four subgroups according to irregularity of the RPE band and the presence of debris. Fifteen eyes of 11 patients (mean age: 79.3 ± 4.3 years; range: 79-94 years) were

  15. Gasoline classification using near infrared (NIR) spectroscopy data: Comparison of multivariate techniques

    Energy Technology Data Exchange (ETDEWEB)

    Balabin, Roman M., E-mail: balabin@org.chem.ethz.ch [Department of Chemistry and Applied Biosciences, ETH Zurich, 8093 Zurich (Switzerland); Safieva, Ravilya Z. [Gubkin Russian State University of Oil and Gas, 119991 Moscow (Russian Federation); Lomakina, Ekaterina I. [Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, 119992 Moscow (Russian Federation)

    2010-06-25

    Near infrared (NIR) spectroscopy is a non-destructive (vibrational spectroscopy based) measurement technique for many multicomponent chemical systems, including products of petroleum (crude oil) refining and petrochemicals, food products (tea, fruits, e.g., apples, milk, wine, spirits, meat, bread, cheese, etc.), pharmaceuticals (drugs, tablets, bioreactor monitoring, etc.), and combustion products. In this paper we have compared the abilities of nine different multivariate classification methods: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), regularized discriminant analysis (RDA), soft independent modeling of class analogy (SIMCA), partial least squares (PLS) classification, K-nearest neighbor (KNN), support vector machines (SVM), probabilistic neural network (PNN), and multilayer perceptron (ANN-MLP) - for gasoline classification. Three sets of near infrared (NIR) spectra (450, 415, and 345 spectra) were used for classification of gasolines into 3, 6, and 3 classes, respectively, according to their source (refinery or process) and type. The 14,000-8000 cm{sup -1} NIR spectral region was chosen. In all cases NIR spectroscopy was found to be effective for gasoline classification purposes, when compared with nuclear magnetic resonance (NMR) spectroscopy or gas chromatography (GC). KNN, SVM, and PNN techniques for classification were found to be among the most effective ones. Artificial neural network (ANN-MLP) approach based on principal component analysis (PCA), which was believed to be efficient, has shown much worse results. We hope that the results obtained in this study will help both further chemometric (multivariate data analysis) investigations and investigations in the sphere of applied vibrational (infrared/IR, near-IR, and Raman) spectroscopy of sophisticated multicomponent systems.

  16. Gasoline classification using near infrared (NIR) spectroscopy data: Comparison of multivariate techniques

    International Nuclear Information System (INIS)

    Balabin, Roman M.; Safieva, Ravilya Z.; Lomakina, Ekaterina I.

    2010-01-01

    Near infrared (NIR) spectroscopy is a non-destructive (vibrational spectroscopy based) measurement technique for many multicomponent chemical systems, including products of petroleum (crude oil) refining and petrochemicals, food products (tea, fruits, e.g., apples, milk, wine, spirits, meat, bread, cheese, etc.), pharmaceuticals (drugs, tablets, bioreactor monitoring, etc.), and combustion products. In this paper we have compared the abilities of nine different multivariate classification methods: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), regularized discriminant analysis (RDA), soft independent modeling of class analogy (SIMCA), partial least squares (PLS) classification, K-nearest neighbor (KNN), support vector machines (SVM), probabilistic neural network (PNN), and multilayer perceptron (ANN-MLP) - for gasoline classification. Three sets of near infrared (NIR) spectra (450, 415, and 345 spectra) were used for classification of gasolines into 3, 6, and 3 classes, respectively, according to their source (refinery or process) and type. The 14,000-8000 cm -1 NIR spectral region was chosen. In all cases NIR spectroscopy was found to be effective for gasoline classification purposes, when compared with nuclear magnetic resonance (NMR) spectroscopy or gas chromatography (GC). KNN, SVM, and PNN techniques for classification were found to be among the most effective ones. Artificial neural network (ANN-MLP) approach based on principal component analysis (PCA), which was believed to be efficient, has shown much worse results. We hope that the results obtained in this study will help both further chemometric (multivariate data analysis) investigations and investigations in the sphere of applied vibrational (infrared/IR, near-IR, and Raman) spectroscopy of sophisticated multicomponent systems.

  17. Spectral evolution of GRBs with negative spectral lag using Fermi GBM observations

    Science.gov (United States)

    Chakrabarti, Arundhati; Chaudhury, Kishor; Sarkar, Samir K.; Bhadra, Arunava

    2018-06-01

    The positive spectral lag of Gamma Ray Bursts (GRBs) is often explained in terms of hard-to-soft spectral evolution of GRB pulses. While positive lags of GRBs is very common, there are few GRB pulses that exhibits negative spectral lags. In the present work we examine whether negative lags of GRBs also can be interpreted in terms of spectral evolution of GRB pulses or not. Using Fermi-GBM data, we identify two GRBs, GRB 090426C and GRB 150213A, with clean pulses that exhibit negative spectral lag. An indication of soft to hard transition has been noticed for the negative spectral lag events from the spectral evolution study. The implication of the present findings on the models of GRB spectral lags are discussed.

  18. Fusion of shallow and deep features for classification of high-resolution remote sensing images

    Science.gov (United States)

    Gao, Lang; Tian, Tian; Sun, Xiao; Li, Hang

    2018-02-01

    Effective spectral and spatial pixel description plays a significant role for the classification of high resolution remote sensing images. Current approaches of pixel-based feature extraction are of two main kinds: one includes the widelyused principal component analysis (PCA) and gray level co-occurrence matrix (GLCM) as the representative of the shallow spectral and shape features, and the other refers to the deep learning-based methods which employ deep neural networks and have made great promotion on classification accuracy. However, the former traditional features are insufficient to depict complex distribution of high resolution images, while the deep features demand plenty of samples to train the network otherwise over fitting easily occurs if only limited samples are involved in the training. In view of the above, we propose a GLCM-based convolution neural network (CNN) approach to extract features and implement classification for high resolution remote sensing images. The employment of GLCM is able to represent the original images and eliminate redundant information and undesired noises. Meanwhile, taking shallow features as the input of deep network will contribute to a better guidance and interpretability. In consideration of the amount of samples, some strategies such as L2 regularization and dropout methods are used to prevent over-fitting. The fine-tuning strategy is also used in our study to reduce training time and further enhance the generalization performance of the network. Experiments with popular data sets such as PaviaU data validate that our proposed method leads to a performance improvement compared to individual involved approaches.

  19. Training staff to empower people with long-term conditions to undertake self care activities.

    Science.gov (United States)

    Bowler, Mandy

    Self care can help people with long-term conditions take control of their lives. However, their interest and ability to engage with it may fluctuate over the course of an illness and many need support to undertake self care activities. A team of community matrons in NHS South of Tyne and Wear helped to develop and pilot an e-learning tool for staff, to remind them of the importance of self care and give advice on ways to support patients. The tool has since been rolled out to all staff groups.

  20. Vehicle Classification Using the Discrete Fourier Transform with Traffic Inductive Sensors.

    Science.gov (United States)

    Lamas-Seco, José J; Castro, Paula M; Dapena, Adriana; Vazquez-Araujo, Francisco J

    2015-10-26

    Inductive Loop Detectors (ILDs) are the most commonly used sensors in traffic management systems. This paper shows that some spectral features extracted from the Fourier Transform (FT) of inductive signatures do not depend on the vehicle speed. Such a property is used to propose a novel method for vehicle classification based on only one signature acquired from a sensor single-loop, in contrast to standard methods using two sensor loops. Our proposal will be evaluated by means of real inductive signatures captured with our hardware prototype.

  1. Multi-agent Negotiation Mechanisms for Statistical Target Classification in Wireless Multimedia Sensor Networks

    Science.gov (United States)

    Wang, Xue; Bi, Dao-wei; Ding, Liang; Wang, Sheng

    2007-01-01

    The recent availability of low cost and miniaturized hardware has allowed wireless sensor networks (WSNs) to retrieve audio and video data in real world applications, which has fostered the development of wireless multimedia sensor networks (WMSNs). Resource constraints and challenging multimedia data volume make development of efficient algorithms to perform in-network processing of multimedia contents imperative. This paper proposes solving problems in the domain of WMSNs from the perspective of multi-agent systems. The multi-agent framework enables flexible network configuration and efficient collaborative in-network processing. The focus is placed on target classification in WMSNs where audio information is retrieved by microphones. To deal with the uncertainties related to audio information retrieval, the statistical approaches of power spectral density estimates, principal component analysis and Gaussian process classification are employed. A multi-agent negotiation mechanism is specially developed to efficiently utilize limited resources and simultaneously enhance classification accuracy and reliability. The negotiation is composed of two phases, where an auction based approach is first exploited to allocate the classification task among the agents and then individual agent decisions are combined by the committee decision mechanism. Simulation experiments with real world data are conducted and the results show that the proposed statistical approaches and negotiation mechanism not only reduce memory and computation requirements in WMSNs but also significantly enhance classification accuracy and reliability. PMID:28903223

  2. A NEW CLASSIFICATION METHOD FOR GAMMA-RAY BURSTS

    International Nuclear Information System (INIS)

    Lue Houjun; Liang Enwei; Zhang Binbin; Zhang Bing

    2010-01-01

    Recent Swift observations suggest that the traditional long versus short gamma-ray burst (GRB) classification scheme does not always associate GRBs to the two physically motivated model types, i.e., Type II (massive star origin) versus Type I (compact star origin). We propose a new phenomenological classification method of GRBs by introducing a new parameter ε = E γ,iso,52 /E 5/3 p,z,2 , where E γ,iso is the isotropic gamma-ray energy (in units of 10 52 erg) and E p,z is the cosmic rest-frame spectral peak energy (in units of 100 keV). For those short GRBs with 'extended emission', both quantities are defined for the short/hard spike only. With the current complete sample of GRBs with redshift and E p measurements, the ε parameter shows a clear bimodal distribution with a separation at ε ∼ 0.03. The high-ε region encloses the typical long GRBs with high luminosity, some high-z 'rest-frame-short' GRBs (such as GRB 090423 and GRB 080913), as well as some high-z short GRBs (such as GRB 090426). All these GRBs have been claimed to be of Type II origin based on other observational properties in the literature. All the GRBs that are argued to be of Type I origin are found to be clustered in the low-ε region. They can be separated from some nearby low-luminosity long GRBs (in 3σ) by an additional T 90 criterion, i.e., T 90,z ∼< 5 s in the Swift/BAT band. We suggest that this new classification scheme can better match the physically motivated Type II/I classification scheme.

  3. DECISION LEVEL FUSION OF ORTHOPHOTO AND LIDAR DATA USING CONFUSION MATRIX INFORMATION FOR LNAD COVER CLASSIFICATION

    Directory of Open Access Journals (Sweden)

    S. Daneshtalab

    2017-09-01

    Full Text Available Automatic urban objects extraction from airborne remote sensing data is essential to process and efficiently interpret the vast amount of airborne imagery and Lidar data available today. The aim of this study is to propose a new approach for the integration of high-resolution aerial imagery and Lidar data to improve the accuracy of classification in the city complications. In the proposed method, first, the classification of each data is separately performed using Support Vector Machine algorithm. In this case, extracted Normalized Digital Surface Model (nDSM and pulse intensity are used in classification of LiDAR data, and three spectral visible bands (Red, Green, Blue are considered as feature vector for the orthoimage classification. Moreover, combining the extracted features of the image and Lidar data another classification is also performed using all the features. The outputs of these classifications are integrated in a decision level fusion system according to the their confusion matrices to find the final classification result. The proposed method was evaluated using an urban area of Zeebruges, Belgium. The obtained results represented several advantages of image fusion with respect to a single shot dataset. With the capabilities of the proposed decision level fusion method, most of the object extraction difficulties and uncertainty were decreased and, the overall accuracy and the kappa values were improved 7% and 10%, respectively.

  4. Improving classification accuracy of spectrally similar tree species: a complex case study in the Kruger National Park

    CSIR Research Space (South Africa)

    Debba, Pravesh

    2009-07-01

    Full Text Available , sugar beet, sunflower, alfalfa. Digital Imaging Spectrometer – DAIS-7915 – 79 channel hyperspectral image. Spectral range from visible (0.4 µm) to thermal infrared (12.3 µm). Spatial resolution 3–20 m depending on the carrier aircraft altitude...

  5. From spectral holeburning memory to spatial-spectral microwave signal processing

    International Nuclear Information System (INIS)

    Babbitt, Wm Randall; Barber, Zeb W; Harrington, Calvin; Mohan, R Krishna; Sharpe, Tia; Bekker, Scott H; Chase, Michael D; Merkel, Kristian D; Stiffler, Colton R; Traxinger, Aaron S; Woidtke, Alex J

    2014-01-01

    Many storage and processing systems based on spectral holeburning have been proposed that access the broad bandwidth and high dynamic range of spatial-spectral materials, but only recently have practical systems been developed that exceed the performance and functional capabilities of electronic devices. This paper reviews the history of the proposed applications of spectral holeburning and spatial-spectral materials, from frequency domain optical memory to microwave photonic signal processing systems. The recent results of a 20 GHz bandwidth high performance spectrum monitoring system with the additional capability of broadband direction finding demonstrates the potential for spatial-spectral systems to be the practical choice for solving demanding signal processing problems in the near future. (paper)

  6. SPECTRAL CLASSIFICATION OF GALAXIES AT 0.5 {<=} z {<=} 1 IN THE CDFS: THE ARTIFICIAL NEURAL NETWORK APPROACH

    Energy Technology Data Exchange (ETDEWEB)

    Teimoorinia, H., E-mail: hteimoo@uvic.ca [Department of Physics and Astronomy, University of Victoria, Victoria, British Columbia, V8P 1A1 (Canada)

    2012-12-01

    The aim of this work is to combine spectral energy distribution (SED) fitting with artificial neural network techniques to assign spectral characteristics to a sample of galaxies at 0.5 < z < 1. The sample is selected from the spectroscopic campaign of the ESO/GOODS-South field, with 142 sources having photometric data from the GOODS-MUSIC catalog covering bands between {approx}0.4 and 24 {mu}m in 10-13 filters. We use the CIGALE code to fit photometric data to Maraston's synthesis spectra to derive mass, specific star formation rate, and age, as well as the best SED of the galaxies. We use the spectral models presented by Kinney et al. as targets in the wavelength interval {approx}1200-7500 A. Then a series of neural networks are trained, with average performance {approx}90%, to classify the best SED in a supervised manner. We consider the effects of the prominent features of the best SED on the performance of the trained networks and also test networks on the galaxy spectra of Coleman et al., which have a lower resolution than the target models. In this way, we conclude that the trained networks take into account all the features of the spectra simultaneously. Using the method, 105 out of 142 galaxies of the sample are classified with high significance. The locus of the classified galaxies in the three graphs of the physical parameters of mass, age, and specific star formation rate appears consistent with the morphological characteristics of the galaxies.

  7. Analysis of steranes and triterpanes in geolipid extracts by automatic classification of mass spectra

    Science.gov (United States)

    Wardroper, A. M. K.; Brooks, P. W.; Humberston, M. J.; Maxwell, J. R.

    1977-01-01

    A computer method is described for the automatic classification of triterpanes and steranes into gross structural type from their mass spectral characteristics. The method has been applied to the spectra obtained by gas-chromatographic/mass-spectroscopic analysis of two mixtures of standards and of hydrocarbon fractions isolated from Green River and Messel oil shales. Almost all of the steranes and triterpanes identified previously in both shales were classified, in addition to a number of new components. The results indicate that classification of such alkanes is possible with a laboratory computer system. The method has application to diagenesis and maturation studies as well as to oil/oil and oil/source rock correlations in which rapid screening of large numbers of samples is required.

  8. PCDDB: the Protein Circular Dichroism Data Bank, a repository for circular dichroism spectral and metadata.

    Science.gov (United States)

    Whitmore, Lee; Woollett, Benjamin; Miles, Andrew John; Klose, D P; Janes, Robert W; Wallace, B A

    2011-01-01

    The Protein Circular Dichroism Data Bank (PCDDB) is a public repository that archives and freely distributes circular dichroism (CD) and synchrotron radiation CD (SRCD) spectral data and their associated experimental metadata. All entries undergo validation and curation procedures to ensure completeness, consistency and quality of the data included. A web-based interface enables users to browse and query sample types, sample conditions, experimental parameters and provides spectra in both graphical display format and as downloadable text files. The entries are linked, when appropriate, to primary sequence (UniProt) and structural (PDB) databases, as well as to secondary databases such as the Enzyme Commission functional classification database and the CATH fold classification database, as well as to literature citations. The PCDDB is available at: http://pcddb.cryst.bbk.ac.uk.

  9. BOREAS TE-18 Landsat TM Physical Classification Image of the NSA

    Science.gov (United States)

    Hall, Forrest G. (Editor); Knapp, David

    2000-01-01

    The BOREAS TE-18 team focused its efforts on using remotely sensed data to characterize the successional and disturbance dynamics of the boreal forest for use in carbon modeling. The objective of this classification is to provide the BOREAS investigators with a data product that characterizes the land cover of the NSA. A Landsat-5 TM image from 21-Jun-1995 was used to derive the classification. A technique was implemented that uses reflectances of various land cover types along with a geometric optical canopy model to produce spectral trajectories. These trajectories are used in a way that is similar to training data to classify the image into the different land cover classes. The data are provided in a binary, image file format. The data files are available on a CD-ROM (see document number 20010000884), or from the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).

  10. Site classification of Indian strong motion network using response spectra ratios

    Science.gov (United States)

    Chopra, Sumer; Kumar, Vikas; Choudhury, Pallabee; Yadav, R. B. S.

    2018-03-01

    In the present study, we tried to classify the Indian strong motion sites spread all over Himalaya and adjoining region, located on varied geological formations, based on response spectral ratio. A total of 90 sites were classified based on 395 strong motion records from 94 earthquakes recorded at these sites. The magnitude of these earthquakes are between 2.3 and 7.7 and the hypocentral distance for most of the cases is less than 50 km. The predominant period obtained from response spectral ratios is used to classify these sites. It was found that the shape and predominant peaks of the spectra at these sites match with those in Japan, Italy, Iran, and at some of the sites in Europe and the same classification scheme can be applied to Indian strong motion network. We found that the earlier schemes based on description of near-surface geology, geomorphology, and topography were not able to capture the effect of sediment thickness. The sites are classified into seven classes (CL-I to CL-VII) with varying predominant periods and ranges as proposed by Alessandro et al. (Bull Seismol Soc Am 102:680-695 2012). The effect of magnitudes and hypocentral distances on the shape and predominant peaks were also studied and found to be very small. The classification scheme is robust and cost-effective and can be used in region-specific attenuation relationships for accounting local site effect.

  11. Combined principal component preprocessing and n-tuple neural networks for improved classification

    DEFF Research Database (Denmark)

    Høskuldsson, Agnar; Linneberg, Christian

    2000-01-01

    We present a combined principal component analysis/neural network scheme for classification. The data used to illustrate the method consist of spectral fluorescence recordings from seven different production facilities, and the task is to relate an unknown sample to one of these seven factories....... The data are first preprocessed by performing an individual principal component analysis on each of the seven groups of data. The components found are then used for classifying the data, but instead of making a single multiclass classifier, we follow the ideas of turning a multiclass problem into a number...... of two-class problems. For each possible pair of classes we further apply a transformation to the calculated principal components in order to increase the separation between the classes. Finally we apply the so-called n-tuple neural network to the transformed data in order to give the classification...

  12. Analysis of optical proprieties of the water reservoir Rodolfo Costa e Silva – Itaara, RS, Brazil, with field spectral data and orbital multispectral images

    Directory of Open Access Journals (Sweden)

    Waterloo Pereira Filho

    2007-08-01

    Full Text Available An evaluation of the discrimination of water classes using continuum removal technique applied over spectral data obtained in field and multispectral images classification is presented. The study area was the Rodolfo Costa e Silva water reservoir, located in central region of Rio Grande do Sul (RS State, in Southern region of Brazil. The methodology was based on in situ data collection of: total suspended solids, chlorophyll (a, b and c, water transparency, and bidirectional spectral reflectance. These data were collected in 21 point (samples in May 16, 2006. The continuum removal technique was applied on the spectral data over 4 absorption bands: 400-550nm, 610-640nm, 650-680nm e 580-700nm. The continuum removal parameters analyzed for each absorption band were: depth, area and width. The multispectral images used were CBERS-2/CCD and Landsat 5/TM. The images were acquired in a date nearest to field work and with appropriate weather conditions. These images were corrected by removing atmospheric effects and then classified. According to the results obtained from the continuum removal technique, it was verified that band depth, area and width did not present a good potential to separate different water classes. Digital classification results did not show significant correlations with the limnological parameters collected in field and, therefore, could not be used to characterize spectrally different water classes or compartments. The main problem of establishing relationships between spectral reflectance and water quality parameters was due to the low variability of optical components in the water of Rodolfo Costa e Silva Reservoir. In this case the spectral analyses (considering both techniques were not sensitive to the relative small variations observed in field data.

  13. Land use and land cover classification for rural residential areas in China using soft-probability cascading of multifeatures

    Science.gov (United States)

    Zhang, Bin; Liu, Yueyan; Zhang, Zuyu; Shen, Yonglin

    2017-10-01

    A multifeature soft-probability cascading scheme to solve the problem of land use and land cover (LULC) classification using high-spatial-resolution images to map rural residential areas in China is proposed. The proposed method is used to build midlevel LULC features. Local features are frequently considered as low-level feature descriptors in a midlevel feature learning method. However, spectral and textural features, which are very effective low-level features, are neglected. The acquisition of the dictionary of sparse coding is unsupervised, and this phenomenon reduces the discriminative power of the midlevel feature. Thus, we propose to learn supervised features based on sparse coding, a support vector machine (SVM) classifier, and a conditional random field (CRF) model to utilize the different effective low-level features and improve the discriminability of midlevel feature descriptors. First, three kinds of typical low-level features, namely, dense scale-invariant feature transform, gray-level co-occurrence matrix, and spectral features, are extracted separately. Second, combined with sparse coding and the SVM classifier, the probabilities of the different LULC classes are inferred to build supervised feature descriptors. Finally, the CRF model, which consists of two parts: unary potential and pairwise potential, is employed to construct an LULC classification map. Experimental results show that the proposed classification scheme can achieve impressive performance when the total accuracy reached about 87%.

  14. Performance Evaluation of Machine Learning Algorithms for Urban Pattern Recognition from Multi-spectral Satellite Images

    Directory of Open Access Journals (Sweden)

    Marc Wieland

    2014-03-01

    Full Text Available In this study, a classification and performance evaluation framework for the recognition of urban patterns in medium (Landsat ETM, TM and MSS and very high resolution (WorldView-2, Quickbird, Ikonos multi-spectral satellite images is presented. The study aims at exploring the potential of machine learning algorithms in the context of an object-based image analysis and to thoroughly test the algorithm’s performance under varying conditions to optimize their usage for urban pattern recognition tasks. Four classification algorithms, Normal Bayes, K Nearest Neighbors, Random Trees and Support Vector Machines, which represent different concepts in machine learning (probabilistic, nearest neighbor, tree-based, function-based, have been selected and implemented on a free and open-source basis. Particular focus is given to assess the generalization ability of machine learning algorithms and the transferability of trained learning machines between different image types and image scenes. Moreover, the influence of the number and choice of training data, the influence of the size and composition of the feature vector and the effect of image segmentation on the classification accuracy is evaluated.

  15. Spectral Imaging by Upconversion

    DEFF Research Database (Denmark)

    Dam, Jeppe Seidelin; Pedersen, Christian; Tidemand-Lichtenberg, Peter

    2011-01-01

    We present a method to obtain spectrally resolved images using upconversion. By this method an image is spectrally shifted from one spectral region to another wavelength. Since the process is spectrally sensitive it allows for a tailored spectral response. We believe this will allow standard...... silicon based cameras designed for visible/near infrared radiation to be used for spectral images in the mid infrared. This can lead to much lower costs for such imaging devices, and a better performance....

  16. Automics: an integrated platform for NMR-based metabonomics spectral processing and data analysis

    Directory of Open Access Journals (Sweden)

    Qu Lijia

    2009-03-01

    Full Text Available Abstract Background Spectral processing and post-experimental data analysis are the major tasks in NMR-based metabonomics studies. While there are commercial and free licensed software tools available to assist these tasks, researchers usually have to use multiple software packages for their studies because software packages generally focus on specific tasks. It would be beneficial to have a highly integrated platform, in which these tasks can be completed within one package. Moreover, with open source architecture, newly proposed algorithms or methods for spectral processing and data analysis can be implemented much more easily and accessed freely by the public. Results In this paper, we report an open source software tool, Automics, which is specifically designed for NMR-based metabonomics studies. Automics is a highly integrated platform that provides functions covering almost all the stages of NMR-based metabonomics studies. Automics provides high throughput automatic modules with most recently proposed algorithms and powerful manual modules for 1D NMR spectral processing. In addition to spectral processing functions, powerful features for data organization, data pre-processing, and data analysis have been implemented. Nine statistical methods can be applied to analyses including: feature selection (Fisher's criterion, data reduction (PCA, LDA, ULDA, unsupervised clustering (K-Mean and supervised regression and classification (PLS/PLS-DA, KNN, SIMCA, SVM. Moreover, Automics has a user-friendly graphical interface for visualizing NMR spectra and data analysis results. The functional ability of Automics is demonstrated with an analysis of a type 2 diabetes metabolic profile. Conclusion Automics facilitates high throughput 1D NMR spectral processing and high dimensional data analysis for NMR-based metabonomics applications. Using Automics, users can complete spectral processing and data analysis within one software package in most cases

  17. Automics: an integrated platform for NMR-based metabonomics spectral processing and data analysis.

    Science.gov (United States)

    Wang, Tao; Shao, Kang; Chu, Qinying; Ren, Yanfei; Mu, Yiming; Qu, Lijia; He, Jie; Jin, Changwen; Xia, Bin

    2009-03-16

    Spectral processing and post-experimental data analysis are the major tasks in NMR-based metabonomics studies. While there are commercial and free licensed software tools available to assist these tasks, researchers usually have to use multiple software packages for their studies because software packages generally focus on specific tasks. It would be beneficial to have a highly integrated platform, in which these tasks can be completed within one package. Moreover, with open source architecture, newly proposed algorithms or methods for spectral processing and data analysis can be implemented much more easily and accessed freely by the public. In this paper, we report an open source software tool, Automics, which is specifically designed for NMR-based metabonomics studies. Automics is a highly integrated platform that provides functions covering almost all the stages of NMR-based metabonomics studies. Automics provides high throughput automatic modules with most recently proposed algorithms and powerful manual modules for 1D NMR spectral processing. In addition to spectral processing functions, powerful features for data organization, data pre-processing, and data analysis have been implemented. Nine statistical methods can be applied to analyses including: feature selection (Fisher's criterion), data reduction (PCA, LDA, ULDA), unsupervised clustering (K-Mean) and supervised regression and classification (PLS/PLS-DA, KNN, SIMCA, SVM). Moreover, Automics has a user-friendly graphical interface for visualizing NMR spectra and data analysis results. The functional ability of Automics is demonstrated with an analysis of a type 2 diabetes metabolic profile. Automics facilitates high throughput 1D NMR spectral processing and high dimensional data analysis for NMR-based metabonomics applications. Using Automics, users can complete spectral processing and data analysis within one software package in most cases. Moreover, with its open source architecture, interested

  18. Habitat Mapping and Classification of the Grand Bay National Estuarine Research Reserve using AISA Hyperspectral Imagery

    Science.gov (United States)

    Rose, K.

    2012-12-01

    Habitat mapping and classification provides essential information for land use planning and ecosystem research, monitoring and management. At the Grand Bay National Estuarine Research Reserve (GRDNERR), Mississippi, habitat characterization of the Grand Bay watershed will also be used to develop a decision-support tool for the NERR's managers and state and local partners. Grand Bay NERR habitat units were identified using a combination of remotely sensed imagery, aerial photography and elevation data. Airborne Imaging Spectrometer for Applications (AISA) hyperspectral data, acquired 5 and 6 May 2010, was analyzed and classified using ENVI v4.8 and v5.0 software. The AISA system was configured to return 63 bands of digital imagery data with a spectral range of 400 to 970 nm (VNIR), spectral resolution (bandwidth) at 8.76 nm, and 1 m spatial resolution. Minimum Noise Fraction (MNF) and Inverse Minimum Noise Fraction were applied to the data prior to using Spectral Angle Mapper ([SAM] supervised) and ISODATA (unsupervised) classification techniques. The resulting class image was exported to ArcGIS 10.0 and visually inspected and compared with the original imagery as well as auxiliary datasets to assist in the attribution of habitat characteristics to the spectral classes, including: National Agricultural Imagery Program (NAIP) aerial photography, Jackson County, MS, 2010; USFWS National Wetlands Inventory, 2007; an existing GRDNERR habitat map (2004), SAV (2009) and salt panne (2002-2003) GIS produced by GRDNERR; and USACE lidar topo-bathymetry, 2005. A field survey to validate the map's accuracy will take place during the 2012 summer season. ENVI's Random Sample generator was used to generate GIS points for a ground-truth survey. The broad range of coastal estuarine habitats and geomorphological features- many of which are transitional and vulnerable to environmental stressors- that have been identified within the GRDNERR point to the value of the Reserve for

  19. Vehicle Classification Using the Discrete Fourier Transform with Traffic Inductive Sensors

    Directory of Open Access Journals (Sweden)

    José J. Lamas-Seco

    2015-10-01

    Full Text Available Inductive Loop Detectors (ILDs are the most commonly used sensors in traffic management systems. This paper shows that some spectral features extracted from the Fourier Transform (FT of inductive signatures do not depend on the vehicle speed. Such a property is used to propose a novel method for vehicle classification based on only one signature acquired from a sensor single-loop, in contrast to standard methods using two sensor loops. Our proposal will be evaluated by means of real inductive signatures captured with our hardware prototype.

  20. Classification of Grassland Successional Stages Using Airborne Hyperspectral Imagery

    Directory of Open Access Journals (Sweden)

    Thomas Möckel

    2014-08-01

    Full Text Available Plant communities differ in their species composition, and, thus, also in their functional trait composition, at different stages in the succession from arable fields to grazed grassland. We examine whether aerial hyperspectral (414–2501 nm remote sensing can be used to discriminate between grazed vegetation belonging to different grassland successional stages. Vascular plant species were recorded in 104.1 m2 plots on the island of Öland (Sweden and the functional properties of the plant species recorded in the plots were characterized in terms of the ground-cover of grasses, specific leaf area and Ellenberg indicator values. Plots were assigned to three different grassland age-classes, representing 5–15, 16–50 and >50 years of grazing management. Partial least squares discriminant analysis models were used to compare classifications based on aerial hyperspectral data with the age-class classification. The remote sensing data successfully classified the plots into age-classes: the overall classification accuracy was higher for a model based on a pre-selected set of wavebands (85%, Kappa statistic value = 0.77 than one using the full set of wavebands (77%, Kappa statistic value = 0.65. Our results show that nutrient availability and grass cover differences between grassland age-classes are detectable by spectral imaging. These techniques may potentially be used for mapping the spatial distribution of grassland habitats at different successional stages.

  1. Portable Multispectral Colorimeter for Metallic Ion Detection and Classification.

    Science.gov (United States)

    Braga, Mauro S; Jaimes, Ruth F V V; Borysow, Walter; Gomes, Osmar F; Salcedo, Walter J

    2017-07-28

    This work deals with a portable device system applied to detect and classify different metallic ions as proposed and developed, aiming its application for hydrological monitoring systems such as rivers, lakes and groundwater. Considering the system features, a portable colorimetric system was developed by using a multispectral optoelectronic sensor. All the technology of quantification and classification of metallic ions using optoelectronic multispectral sensors was fully integrated in the embedded hardware FPGA ( Field Programmable Gate Array) technology and software based on virtual instrumentation (NI LabView ® ). The system draws on an indicative colorimeter by using the chromogen reagent of 1-(2-pyridylazo)-2-naphthol (PAN). The results obtained with the signal processing and pattern analysis using the method of the linear discriminant analysis, allows excellent results during detection and classification of Pb(II), Cd(II), Zn(II), Cu(II), Fe(III) and Ni(II) ions, with almost the same level of performance as for those obtained from the Ultravioled and visible (UV-VIS) spectrophotometers of high spectral resolution.

  2. Portable Multispectral Colorimeter for Metallic Ion Detection and Classification

    Directory of Open Access Journals (Sweden)

    Mauro S. Braga

    2017-07-01

    Full Text Available This work deals with a portable device system applied to detect and classify different metallic ions as proposed and developed, aiming its application for hydrological monitoring systems such as rivers, lakes and groundwater. Considering the system features, a portable colorimetric system was developed by using a multispectral optoelectronic sensor. All the technology of quantification and classification of metallic ions using optoelectronic multispectral sensors was fully integrated in the embedded hardware FPGA ( Field Programmable Gate Array technology and software based on virtual instrumentation (NI LabView®. The system draws on an indicative colorimeter by using the chromogen reagent of 1-(2-pyridylazo-2-naphthol (PAN. The results obtained with the signal processing and pattern analysis using the method of the linear discriminant analysis, allows excellent results during detection and classification of Pb(II, Cd(II, Zn(II, Cu(II, Fe(III and Ni(II ions, with almost the same level of performance as for those obtained from the Ultravioled and visible (UV-VIS spectrophotometers of high spectral resolution.

  3. A New Classification Approach Based on Multiple Classification Rules

    OpenAIRE

    Zhongmei Zhou

    2014-01-01

    A good classifier can correctly predict new data for which the class label is unknown, so it is important to construct a high accuracy classifier. Hence, classification techniques are much useful in ubiquitous computing. Associative classification achieves higher classification accuracy than some traditional rule-based classification approaches. However, the approach also has two major deficiencies. First, it generates a very large number of association classification rules, especially when t...

  4. Classification of high-resolution multi-swath hyperspectral data using Landsat 8 surface reflectance data as a calibration target and a novel histogram based unsupervised classification technique to determine natural classes from biophysically relevant fit parameters

    Science.gov (United States)

    McCann, C.; Repasky, K. S.; Morin, M.; Lawrence, R. L.; Powell, S. L.

    2016-12-01

    Compact, cost-effective, flight-based hyperspectral imaging systems can provide scientifically relevant data over large areas for a variety of applications such as ecosystem studies, precision agriculture, and land management. To fully realize this capability, unsupervised classification techniques based on radiometrically-calibrated data that cluster based on biophysical similarity rather than simply spectral similarity are needed. An automated technique to produce high-resolution, large-area, radiometrically-calibrated hyperspectral data sets based on the Landsat surface reflectance data product as a calibration target was developed and applied to three subsequent years of data covering approximately 1850 hectares. The radiometrically-calibrated data allows inter-comparison of the temporal series. Advantages of the radiometric calibration technique include the need for minimal site access, no ancillary instrumentation, and automated processing. Fitting the reflectance spectra of each pixel using a set of biophysically relevant basis functions reduces the data from 80 spectral bands to 9 parameters providing noise reduction and data compression. Examination of histograms of these parameters allows for determination of natural splitting into biophysical similar clusters. This method creates clusters that are similar in terms of biophysical parameters, not simply spectral proximity. Furthermore, this method can be applied to other data sets, such as urban scenes, by developing other physically meaningful basis functions. The ability to use hyperspectral imaging for a variety of important applications requires the development of data processing techniques that can be automated. The radiometric-calibration combined with the histogram based unsupervised classification technique presented here provide one potential avenue for managing big-data associated with hyperspectral imaging.

  5. Remote sensing mapping of macroalgal farms by modifying thresholds in the classification tree

    KAUST Repository

    Zheng, Yuhan

    2018-05-07

    Remote sensing is the main approach used to classify and map aquatic vegetation, and classification tree (CT) analysis is superior to various classification methods. Based on previous studies, modified CT can be developed from traditional CT by adjusting the thresholds based on the statistical relationship between spectral features to classify different images without ground-truth data. However, no studies have yet employed this method to resolve marine vegetation. In this study, three Gao-Fen 1 satellite images obtained with the same sensor on January 30, 2014, November 5, 2014, and January 21, 2015 were selected, and two features were then employed to extract macroalgae from aquaculture farms from the seawater background. Besides, object-based classification and other image analysis methods were adopted to improve the classification accuracy in this study. Results show that the overall accuracies of traditional CTs for three images are 92.0%, 94.2% and 93.9%, respectively, whereas the overall accuracies of the two corresponding modified CTs for images obtained on January 21, 2015 and November 5, 2014 are 93.1% and 89.5%, respectively. This indicates modified CTs can help map macroalgae with multi-date imagery and monitor the spatiotemporal distribution of macroalgae in coastal environments.

  6. Remote sensing mapping of macroalgal farms by modifying thresholds in the classification tree

    KAUST Repository

    Zheng, Yuhan; Duarte, Carlos M.; Chen, Jiang; Li, Dan; Lou, Zhaohan; Wu, Jiaping

    2018-01-01

    Remote sensing is the main approach used to classify and map aquatic vegetation, and classification tree (CT) analysis is superior to various classification methods. Based on previous studies, modified CT can be developed from traditional CT by adjusting the thresholds based on the statistical relationship between spectral features to classify different images without ground-truth data. However, no studies have yet employed this method to resolve marine vegetation. In this study, three Gao-Fen 1 satellite images obtained with the same sensor on January 30, 2014, November 5, 2014, and January 21, 2015 were selected, and two features were then employed to extract macroalgae from aquaculture farms from the seawater background. Besides, object-based classification and other image analysis methods were adopted to improve the classification accuracy in this study. Results show that the overall accuracies of traditional CTs for three images are 92.0%, 94.2% and 93.9%, respectively, whereas the overall accuracies of the two corresponding modified CTs for images obtained on January 21, 2015 and November 5, 2014 are 93.1% and 89.5%, respectively. This indicates modified CTs can help map macroalgae with multi-date imagery and monitor the spatiotemporal distribution of macroalgae in coastal environments.

  7. 78 FR 68983 - Cotton Futures Classification: Optional Classification Procedure

    Science.gov (United States)

    2013-11-18

    ...-AD33 Cotton Futures Classification: Optional Classification Procedure AGENCY: Agricultural Marketing... regulations to allow for the addition of an optional cotton futures classification procedure--identified and... response to requests from the U.S. cotton industry and ICE, AMS will offer a futures classification option...

  8. Spectral Skyline Separation: Extended Landmark Databases and Panoramic Imaging

    Directory of Open Access Journals (Sweden)

    Dario Differt

    2016-09-01

    Full Text Available Evidence from behavioral experiments suggests that insects use the skyline as a cue for visual navigation. However, changes of lighting conditions, over hours, days or possibly seasons, significantly affect the appearance of the sky and ground objects. One possible solution to this problem is to extract the “skyline” by an illumination-invariant classification of the environment into two classes, ground objects and sky. In a previous study (Insect models of illumination-invariant skyline extraction from UV (ultraviolet and green channels, we examined the idea of using two different color channels available for many insects (UV and green to perform this segmentation. We found out that for suburban scenes in temperate zones, where the skyline is dominated by trees and artificial objects like houses, a “local” UV segmentation with adaptive thresholds applied to individual images leads to the most reliable classification. Furthermore, a “global” segmentation with fixed thresholds (trained on an image dataset recorded over several days using UV-only information is only slightly worse compared to using both the UV and green channel. In this study, we address three issues: First, to enhance the limited range of environments covered by the dataset collected in the previous study, we gathered additional data samples of skylines consisting of minerals (stones, sand, earth as ground objects. We could show that also for mineral-rich environments, UV-only segmentation achieves a quality comparable to multi-spectral (UV and green segmentation. Second, we collected a wide variety of ground objects to examine their spectral characteristics under different lighting conditions. On the one hand, we found that the special case of diffusely-illuminated minerals increases the difficulty to reliably separate ground objects from the sky. On the other hand, the spectral characteristics of this collection of ground objects covers well with the data collected

  9. Spectral and photoelectric observations of V 407 Cygni and AS 338

    International Nuclear Information System (INIS)

    Esipov, V.F.; Taranova, O.G.; Yudin, B.F.

    1989-01-01

    The results are given of photometric (in the UBVRJHKLMN system) and spectral (range 4,300-7,200 angstrom) observations of the object V 407 Cyg, which is included in the catalog of symbiotic stars, and the symbiotic star AS 338. It is shown that during the time of their observations the radiation of V 407 Cyg, a variable red giant surrounded by a dust envelope, did not reveal definite signs of the presence of a hot source of radiation that would permit classification of the object as a symbiotic star. In the spectrum of V 407 Cyg the lines H α and H β were not observed, whereas the lines (N II) 6548 and 6584 were. During 1986-1987 the brightness of AS 338 in the visible wavelength range continued to decrease. Nevertheless, the spectrum of the star still did not acquire a symbiotic nature, so that for more than four years AS 338 has been in a state in which its hot component can be classified as a star of the spectral class B with envelope

  10. Automated vegetation classification using Thematic Mapper Simulation data

    Science.gov (United States)

    Nedelman, K. S.; Cate, R. B.; Bizzell, R. M.

    1983-01-01

    The present investigation is concerned with the results of a study of Thematic Mapper Simulation (TMS) data. One of the objectives of the study was related to an evaluation of the usefulness of the Thematic Mapper's (TM) improved spatial resolution and spectral coverage. The study was undertaken as part of a preparation for the efficient incorporation of Landsat 4 data into ongoing technology development in remote sensing. The study included an application of automated Landsat vegetation classification technology to TMS data. Results of comparing TMS data to Multispectral Scanner (MSS) data were found to indicate that all field definition, crop type discrimination, and subsequent proportion estimation may be greatly increased with the availability of TM data.

  11. Undertaking and writing research that is important, targeted, and the best you can do.

    Science.gov (United States)

    McLeod, Sharynne

    2014-04-01

    Conducting and writing research is a privilege. It is a privilege because researchers can change lives through their findings and can influence public knowledge and debate. It is also a privilege because researchers are reliant on the time and goodwill of participants (and colleagues), and research is often underpinned by funding raised by the public, either through taxes or philanthropic donations. This privilege comes with responsibility. Researchers have a responsibility to undertake research that is important, targeted, and of high quality. This editorial aims to inspire, challenge, and bolster the research efforts of individuals and teams.

  12. The development of power generation by electricity supply undertakings and industries in Western Europe

    International Nuclear Information System (INIS)

    Cura, H.

    1998-01-01

    Following the events of recent years - the opening up of the east, efforts to stimulate international competition - the Western European electricity industry is strongly on the move. In spite of the non-uniformity of the electricity supply structures in the individual countries, the trend towards liberalization of the electricity market is characterized by different forms of expression. Against this background, this paper provides a review of the status and prospects of electricity demand developments and of primary energy supply. It considers the consequences which thereby arise for the power plant inventory of electricity supply undertakings and industries. (orig.) [de

  13. A simple semi-automatic approach for land cover classification from multispectral remote sensing imagery.

    Directory of Open Access Journals (Sweden)

    Dong Jiang

    Full Text Available Land cover data represent a fundamental data source for various types of scientific research. The classification of land cover based on satellite data is a challenging task, and an efficient classification method is needed. In this study, an automatic scheme is proposed for the classification of land use using multispectral remote sensing images based on change detection and a semi-supervised classifier. The satellite image can be automatically classified using only the prior land cover map and existing images; therefore human involvement is reduced to a minimum, ensuring the operability of the method. The method was tested in the Qingpu District of Shanghai, China. Using Environment Satellite 1(HJ-1 images of 2009 with 30 m spatial resolution, the areas were classified into five main types of land cover based on previous land cover data and spectral features. The results agreed on validation of land cover maps well with a Kappa value of 0.79 and statistical area biases in proportion less than 6%. This study proposed a simple semi-automatic approach for land cover classification by using prior maps with satisfied accuracy, which integrated the accuracy of visual interpretation and performance of automatic classification methods. The method can be used for land cover mapping in areas lacking ground reference information or identifying rapid variation of land cover regions (such as rapid urbanization with convenience.

  14. Improved Wetland Classification Using Eight-Band High Resolution Satellite Imagery and a Hybrid Approach

    Directory of Open Access Journals (Sweden)

    Charles R. Lane

    2014-12-01

    Full Text Available Although remote sensing technology has long been used in wetland inventory and monitoring, the accuracy and detail level of wetland maps derived with moderate resolution imagery and traditional techniques have been limited and often unsatisfactory. We explored and evaluated the utility of a newly launched high-resolution, eight-band satellite system (Worldview-2; WV2 for identifying and classifying freshwater deltaic wetland vegetation and aquatic habitats in the Selenga River Delta of Lake Baikal, Russia, using a hybrid approach and a novel application of Indicator Species Analysis (ISA. We achieved an overall classification accuracy of 86.5% (Kappa coefficient: 0.85 for 22 classes of aquatic and wetland habitats and found that additional metrics, such as the Normalized Difference Vegetation Index and image texture, were valuable for improving the overall classification accuracy and particularly for discriminating among certain habitat classes. Our analysis demonstrated that including WV2’s four spectral bands from parts of the spectrum less commonly used in remote sensing analyses, along with the more traditional bandwidths, contributed to the increase in the overall classification accuracy by ~4% overall, but with considerable increases in our ability to discriminate certain communities. The coastal band improved differentiating open water and aquatic (i.e., vegetated habitats, and the yellow, red-edge, and near-infrared 2 bands improved discrimination among different vegetated aquatic and terrestrial habitats. The use of ISA provided statistical rigor in developing associations between spectral classes and field-based data. Our analyses demonstrated the utility of a hybrid approach and the benefit of additional bands and metrics in providing the first spatially explicit mapping of a large and heterogeneous wetland system.

  15. Rapid identification and classification of Listeria spp. and serotype assignment of Listeria monocytogenes using fourier transform-infrared spectroscopy and artificial neural network analysis

    Science.gov (United States)

    The use of Fourier Transform-Infrared Spectroscopy (FT-IR) in conjunction with Artificial Neural Network software, NeuroDeveloper™ was examined for the rapid identification and classification of Listeria species and serotyping of Listeria monocytogenes. A spectral library was created for 245 strains...

  16. Examination of Spectral Transformations on Spectral Mixture Analysis

    Science.gov (United States)

    Deng, Y.; Wu, C.

    2018-04-01

    While many spectral transformation techniques have been applied on spectral mixture analysis (SMA), few study examined their necessity and applicability. This paper focused on exploring the difference between spectrally transformed schemes and untransformed scheme to find out which transformed scheme performed better in SMA. In particular, nine spectrally transformed schemes as well as untransformed scheme were examined in two study areas. Each transformed scheme was tested 100 times using different endmember classes' spectra under the endmember model of vegetation- high albedo impervious surface area-low albedo impervious surface area-soil (V-ISAh-ISAl-S). Performance of each scheme was assessed based on mean absolute error (MAE). Statistical analysis technique, Paired-Samples T test, was applied to test the significance of mean MAEs' difference between transformed and untransformed schemes. Results demonstrated that only NSMA could exceed the untransformed scheme in all study areas. Some transformed schemes showed unstable performance since they outperformed the untransformed scheme in one area but weakened the SMA result in another region.

  17. Clastic compaction unit classification based on clay content and integrated compaction recovery using well and seismic data

    Directory of Open Access Journals (Sweden)

    Zhong Hong

    2016-11-01

    Full Text Available Abstract Compaction correction is a key part of paleo-geomorphic recovery methods. Yet, the influence of lithology on the porosity evolution is not usually taken into account. Present methods merely classify the lithologies as sandstone and mudstone to undertake separate porosity-depth compaction modeling. However, using just two lithologies is an oversimplification that cannot represent the compaction history. In such schemes, the precision of the compaction recovery is inadequate. To improve the precision of compaction recovery, a depth compaction model has been proposed that involves both porosity and clay content. A clastic lithological compaction unit classification method, based on clay content, has been designed to identify lithological boundaries and establish sets of compaction units. Also, on the basis of the clastic compaction unit classification, two methods of compaction recovery that integrate well and seismic data are employed to extrapolate well-based compaction information outward along seismic lines and recover the paleo-topography of the clastic strata in the region. The examples presented here show that a better understanding of paleo-geomorphology can be gained by applying the proposed compaction recovery technology.

  18. What factors influence community-dwelling older people’s intent to undertake multifactorial fall prevention programs?

    Directory of Open Access Journals (Sweden)

    Hill KD

    2014-11-01

    Full Text Available Keith D Hill,1,2 Lesley Day,3 Terry P Haines4,5 1School of Physiotherapy and Exercise Science, Faculty of Health Sciences, Curtin University, Perth, WA, Australia; 2National Ageing Research Institute, Royal Melbourne Hospital, Parkville, VIC, Australia; 3Falls Prevention Research Unit, Monash Injury Research Institute, Monash University, VIC, Australia; 4Allied Health Research Unit, Southern Health, Cheltenham, VIC, Australia; 5Physiotherapy Department, Faculty of Medicine, Nursing, and Health Sciences, Monash University, VIC, Australia Purpose: To investigate previous, current, or planned participation in, and perceptions toward, multifactorial fall prevention programs such as those delivered through a falls clinic in the community setting, and to identify factors influencing older people’s intent to undertake these interventions.Design and methods: Community-dwelling people aged >70 years completed a telephone survey. Participants were randomly selected from an electronic residential telephone listing, but purposeful sampling was used to include equal numbers with and without common chronic health conditions associated with fall-related hospitalization. The survey included scenarios for fall prevention interventions, including assessment/multifactorial interventions, such as those delivered through a falls clinic. Participants were asked about previous exposure to, or intent to participate in, the interventions. A path model analysis was used to identify factors associated with intent to participate in assessment/multifactorial interventions.Results: Thirty of 376 participants (8.0% reported exposure to a multifactorial falls clinic-type intervention in the past 5 years, and 16.0% expressed intention to undertake this intervention. Of the 132 participants who reported one or more falls in the past 12 months, over one-third were undecided or disagreed that a falls clinic type of intervention would be of benefit to them. Four elements

  19. Optimal Decision Fusion for Urban Land-Use/Land-Cover Classification Based on Adaptive Differential Evolution Using Hyperspectral and LiDAR Data

    Directory of Open Access Journals (Sweden)

    Yanfei Zhong

    2017-08-01

    Full Text Available Hyperspectral images and light detection and ranging (LiDAR data have, respectively, the high spectral resolution and accurate elevation information required for urban land-use/land-cover (LULC classification. To combine the respective advantages of hyperspectral and LiDAR data, this paper proposes an optimal decision fusion method based on adaptive differential evolution, namely ODF-ADE, for urban LULC classification. In the ODF-ADE framework the normalized difference vegetation index (NDVI, gray-level co-occurrence matrix (GLCM and digital surface model (DSM are extracted to form the feature map. The three different classifiers of the maximum likelihood classifier (MLC, support vector machine (SVM and multinomial logistic regression (MLR are used to classify the extracted features. To find the optimal weights for the different classification maps, weighted voting is used to obtain the classification result and the weights of each classification map are optimized by the differential evolution algorithm which uses a self-adaptive strategy to obtain the parameter adaptively. The final classification map is obtained after post-processing based on conditional random fields (CRF. The experimental results confirm that the proposed algorithm is very effective in urban LULC classification.

  20. Analysis and Evaluation of IKONOS Image Fusion Algorithm Based on Land Cover Classification

    Institute of Scientific and Technical Information of China (English)

    Xia; JING; Yan; BAO

    2015-01-01

    Different fusion algorithm has its own advantages and limitations,so it is very difficult to simply evaluate the good points and bad points of the fusion algorithm. Whether an algorithm was selected to fuse object images was also depended upon the sensor types and special research purposes. Firstly,five fusion methods,i. e. IHS,Brovey,PCA,SFIM and Gram-Schmidt,were briefly described in the paper. And then visual judgment and quantitative statistical parameters were used to assess the five algorithms. Finally,in order to determine which one is the best suitable fusion method for land cover classification of IKONOS image,the maximum likelihood classification( MLC) was applied using the above five fusion images. The results showed that the fusion effect of SFIM transform and Gram-Schmidt transform were better than the other three image fusion methods in spatial details improvement and spectral information fidelity,and Gram-Schmidt technique was superior to SFIM transform in the aspect of expressing image details. The classification accuracy of the fused image using Gram-Schmidt and SFIM algorithms was higher than that of the other three image fusion methods,and the overall accuracy was greater than 98%. The IHS-fused image classification accuracy was the lowest,the overall accuracy and kappa coefficient were 83. 14% and 0. 76,respectively. Thus the IKONOS fusion images obtained by the Gram-Schmidt and SFIM were better for improving the land cover classification accuracy.

  1. Development of classification models to detect Salmonella Enteritidis and Salmonella Typhimurium found in poultry carcass rinses by visible-near infrared hyperspectral imaging

    Science.gov (United States)

    Seo, Young Wook; Yoon, Seung Chul; Park, Bosoon; Hinton, Arthur; Windham, William R.; Lawrence, Kurt C.

    2013-05-01

    Salmonella is a major cause of foodborne disease outbreaks resulting from the consumption of contaminated food products in the United States. This paper reports the development of a hyperspectral imaging technique for detecting and differentiating two of the most common Salmonella serotypes, Salmonella Enteritidis (SE) and Salmonella Typhimurium (ST), from background microflora that are often found in poultry carcass rinse. Presumptive positive screening of colonies with a traditional direct plating method is a labor intensive and time consuming task. Thus, this paper is concerned with the detection of differences in spectral characteristics among the pure SE, ST, and background microflora grown on brilliant green sulfa (BGS) and xylose lysine tergitol 4 (XLT4) agar media with a spread plating technique. Visible near-infrared hyperspectral imaging, providing the spectral and spatial information unique to each microorganism, was utilized to differentiate SE and ST from the background microflora. A total of 10 classification models, including five machine learning algorithms, each without and with principal component analysis (PCA), were validated and compared to find the best model in classification accuracy. The five machine learning (classification) algorithms used in this study were Mahalanobis distance (MD), k-nearest neighbor (kNN), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and support vector machine (SVM). The average classification accuracy of all 10 models on a calibration (or training) set of the pure cultures on BGS agar plates was 98% (Kappa coefficient = 0.95) in determining the presence of SE and/or ST although it was difficult to differentiate between SE and ST. The average classification accuracy of all 10 models on a training set for ST detection on XLT4 agar was over 99% (Kappa coefficient = 0.99) although SE colonies on XLT4 agar were difficult to differentiate from background microflora. The average classification

  2. Potential of VIS-NIR-SWIR Spectroscopy from the Chinese Soil Spectral Library for Assessment of Nitrogen Fertilization Rates in the Paddy-Rice Region, China

    Directory of Open Access Journals (Sweden)

    Shuo Li

    2015-05-01

    Full Text Available To meet growing food demand with limited land and reduced environmental impact, soil testing and formulated fertilization methods have been widely adopted around the world. However, conventional technology for investigating nitrogen fertilization rates (NFR is time consuming and expensive. Here, we evaluated the use of visible near-infrared shortwave-infrared (VIS-NIR-SWIR: 400–2500 nm spectroscopy for the assessment of NFR to provide necessary information for fast, cost-effective and precise fertilization rating. Over 2000 samples were collected from paddy-rice fields in 10 Chinese provinces; samples were added to the Chinese Soil Spectral Library (CSSL. Two kinds of modeling strategies for NFR, quantitative estimation of soil N prior to classification and qualitative by classification, were employed using partial least squares regression (PLSR, locally weighted regression (LWR, and support vector machine discriminant analogy (SVMDA. Overall, both LWR and SVMDA had moderate accuracies with Cohen’s kappa coefficients of 0.47 and 0.48, respectively, while PLSR had fair accuracy (0.37. We conclude that VIS-NIR-SWIR spectroscopy coupled with the CSSL appears to be a viable, rapid means for the assessment of NFR in paddy-rice soil. Based on qualitative classification of soil spectral data only, it is recommended that the SVMDA be adopted for rapid implementation.

  3. Is overall similarity classification less effortful than single-dimension classification?

    Science.gov (United States)

    Wills, Andy J; Milton, Fraser; Longmore, Christopher A; Hester, Sarah; Robinson, Jo

    2013-01-01

    It is sometimes argued that the implementation of an overall similarity classification is less effortful than the implementation of a single-dimension classification. In the current article, we argue that the evidence securely in support of this view is limited, and report additional evidence in support of the opposite proposition--overall similarity classification is more effortful than single-dimension classification. Using a match-to-standards procedure, Experiments 1A, 1B and 2 demonstrate that concurrent load reduces the prevalence of overall similarity classification, and that this effect is robust to changes in the concurrent load task employed, the level of time pressure experienced, and the short-term memory requirements of the classification task. Experiment 3 demonstrates that participants who produced overall similarity classifications from the outset have larger working memory capacities than those who produced single-dimension classifications initially, and Experiment 4 demonstrates that instructions to respond meticulously increase the prevalence of overall similarity classification.

  4. Effective Sequential Classifier Training for SVM-Based Multitemporal Remote Sensing Image Classification

    Science.gov (United States)

    Guo, Yiqing; Jia, Xiuping; Paull, David

    2018-06-01

    The explosive availability of remote sensing images has challenged supervised classification algorithms such as Support Vector Machines (SVM), as training samples tend to be highly limited due to the expensive and laborious task of ground truthing. The temporal correlation and spectral similarity between multitemporal images have opened up an opportunity to alleviate this problem. In this study, a SVM-based Sequential Classifier Training (SCT-SVM) approach is proposed for multitemporal remote sensing image classification. The approach leverages the classifiers of previous images to reduce the required number of training samples for the classifier training of an incoming image. For each incoming image, a rough classifier is firstly predicted based on the temporal trend of a set of previous classifiers. The predicted classifier is then fine-tuned into a more accurate position with current training samples. This approach can be applied progressively to sequential image data, with only a small number of training samples being required from each image. Experiments were conducted with Sentinel-2A multitemporal data over an agricultural area in Australia. Results showed that the proposed SCT-SVM achieved better classification accuracies compared with two state-of-the-art model transfer algorithms. When training data are insufficient, the overall classification accuracy of the incoming image was improved from 76.18% to 94.02% with the proposed SCT-SVM, compared with those obtained without the assistance from previous images. These results demonstrate that the leverage of a priori information from previous images can provide advantageous assistance for later images in multitemporal image classification.

  5. A ROUGH SET DECISION TREE BASED MLP-CNN FOR VERY HIGH RESOLUTION REMOTELY SENSED IMAGE CLASSIFICATION

    Directory of Open Access Journals (Sweden)

    C. Zhang

    2017-09-01

    Full Text Available Recent advances in remote sensing have witnessed a great amount of very high resolution (VHR images acquired at sub-metre spatial resolution. These VHR remotely sensed data has post enormous challenges in processing, analysing and classifying them effectively due to the high spatial complexity and heterogeneity. Although many computer-aid classification methods that based on machine learning approaches have been developed over the past decades, most of them are developed toward pixel level spectral differentiation, e.g. Multi-Layer Perceptron (MLP, which are unable to exploit abundant spatial details within VHR images. This paper introduced a rough set model as a general framework to objectively characterize the uncertainty in CNN classification results, and further partition them into correctness and incorrectness on the map. The correct classification regions of CNN were trusted and maintained, whereas the misclassification areas were reclassified using a decision tree with both CNN and MLP. The effectiveness of the proposed rough set decision tree based MLP-CNN was tested using an urban area at Bournemouth, United Kingdom. The MLP-CNN, well capturing the complementarity between CNN and MLP through the rough set based decision tree, achieved the best classification performance both visually and numerically. Therefore, this research paves the way to achieve fully automatic and effective VHR image classification.

  6. Simultaneous data pre-processing and SVM classification model selection based on a parallel genetic algorithm applied to spectroscopic data of olive oils.

    Science.gov (United States)

    Devos, Olivier; Downey, Gerard; Duponchel, Ludovic

    2014-04-01

    Classification is an important task in chemometrics. For several years now, support vector machines (SVMs) have proven to be powerful for infrared spectral data classification. However such methods require optimisation of parameters in order to control the risk of overfitting and the complexity of the boundary. Furthermore, it is established that the prediction ability of classification models can be improved using pre-processing in order to remove unwanted variance in the spectra. In this paper we propose a new methodology based on genetic algorithm (GA) for the simultaneous optimisation of SVM parameters and pre-processing (GENOPT-SVM). The method has been tested for the discrimination of the geographical origin of Italian olive oil (Ligurian and non-Ligurian) on the basis of near infrared (NIR) or mid infrared (FTIR) spectra. Different classification models (PLS-DA, SVM with mean centre data, GENOPT-SVM) have been tested and statistically compared using McNemar's statistical test. For the two datasets, SVM with optimised pre-processing give models with higher accuracy than the one obtained with PLS-DA on pre-processed data. In the case of the NIR dataset, most of this accuracy improvement (86.3% compared with 82.8% for PLS-DA) occurred using only a single pre-processing step. For the FTIR dataset, three optimised pre-processing steps are required to obtain SVM model with significant accuracy improvement (82.2%) compared to the one obtained with PLS-DA (78.6%). Furthermore, this study demonstrates that even SVM models have to be developed on the basis of well-corrected spectral data in order to obtain higher classification rates. Copyright © 2013 Elsevier Ltd. All rights reserved.

  7. Application of higher order spectral features and support vector machines for bearing faults classification.

    Science.gov (United States)

    Saidi, Lotfi; Ben Ali, Jaouher; Fnaiech, Farhat

    2015-01-01

    Condition monitoring and fault diagnosis of rolling element bearings timely and accurately are very important to ensure the reliability of rotating machinery. This paper presents a novel pattern classification approach for bearings diagnostics, which combines the higher order spectra analysis features and support vector machine classifier. The use of non-linear features motivated by the higher order spectra has been reported to be a promising approach to analyze the non-linear and non-Gaussian characteristics of the mechanical vibration signals. The vibration bi-spectrum (third order spectrum) patterns are extracted as the feature vectors presenting different bearing faults. The extracted bi-spectrum features are subjected to principal component analysis for dimensionality reduction. These principal components were fed to support vector machine to distinguish four kinds of bearing faults covering different levels of severity for each fault type, which were measured in the experimental test bench running under different working conditions. In order to find the optimal parameters for the multi-class support vector machine model, a grid-search method in combination with 10-fold cross-validation has been used. Based on the correct classification of bearing patterns in the test set, in each fold the performance measures are computed. The average of these performance measures is computed to report the overall performance of the support vector machine classifier. In addition, in fault detection problems, the performance of a detection algorithm usually depends on the trade-off between robustness and sensitivity. The sensitivity and robustness of the proposed method are explored by running a series of experiments. A receiver operating characteristic (ROC) curve made the results more convincing. The results indicated that the proposed method can reliably identify different fault patterns of rolling element bearings based on vibration signals. Copyright © 2014 ISA

  8. A High Throughput Ambient Mass Spectrometric Approach to Species Identification and Classification from Chemical Fingerprint Signatures

    OpenAIRE

    Musah, Rabi A.; Espinoza, Edgard O.; Cody, Robert B.; Lesiak, Ashton D.; Christensen, Earl D.; Moore, Hannah E.; Maleknia, Simin; Drijfhout, Falko P.

    2015-01-01

    A high throughput method for species identification and classification through chemometric processing of direct analysis in real time (DART) mass spectrometry-derived fingerprint signatures has been developed. The method entails introduction of samples to the open air space between the DART ion source and the mass spectrometer inlet, with the entire observed mass spectral fingerprint subjected to unsupervised hierarchical clustering processing. A range of both polar and non-polar chemotypes a...

  9. Spectral analysis of point-vortex dynamics: first application to vortex polygons in a circular domain

    International Nuclear Information System (INIS)

    Speetjens, M F M; Meleshko, V V; Van Heijst, G J F

    2014-01-01

    The present study addresses the classical problem of the dynamics and stability of a cluster of N-point vortices of equal strength arranged in a polygonal configuration (‘N-vortex polygons’). In unbounded domains, such N-vortex polygons are unconditionally stable for N⩽7. Confinement in a circular domain tightens the stability conditions to N⩽6 and a maximum polygon size relative to the domain radius. This work expands on existing studies on stability and integrability by a first giving an exploratory spectral analysis of the dynamics of N vortex polygons in circular domains. Key to this is that the spectral signature of the time evolution of vortex positions reflects their qualitative behaviour. Expressing vortex motion by a generic evolution operator (the so-called Koopman operator) provides a rigorous framework for such spectral analyses. This paves the way to further differentiation and classification of point-vortex behaviour beyond stability and integrability. The concept of Koopman-based spectral analysis is demonstrated for N-vortex polygons. This reveals that conditional stability can be seen as a local form of integrability and confirms an important generic link between spectrum and dynamics: discrete spectra imply regular (quasi-periodic) motion; continuous (sub-)spectra imply chaotic motion. Moreover, this exposes rich nonlinear dynamics as intermittency between regular and chaotic motion and quasi-coherent structures formed by chaotic vortices. (ss 1)

  10. Comparison of the Spectral Properties of Pansharpened Images Generated from AVNIR-2 and Prism Onboard Alos

    Science.gov (United States)

    Matsuoka, M.

    2012-07-01

    A considerable number of methods for pansharpening remote-sensing images have been developed to generate higher spatial resolution multispectral images by the fusion of lower resolution multispectral images and higher resolution panchromatic images. Because pansharpening alters the spectral properties of multispectral images, method selection is one of the key factors influencing the accuracy of subsequent analyses such as land-cover classification or change detection. In this study, seven pixel-based pansharpening methods (additive wavelet intensity, additive wavelet principal component, generalized Laplacian pyramid with spectral distortion minimization, generalized intensity-hue-saturation (GIHS) transform, GIHS adaptive, Gram-Schmidt spectral sharpening, and block-based synthetic variable ratio) were compared using AVNIR-2 and PRISM onboard ALOS from the viewpoint of the preservation of spectral properties of AVNIR-2. A visual comparison was made between pansharpened images generated from spatially degraded AVNIR-2 and original images over urban, agricultural, and forest areas. The similarity of the images was evaluated in terms of the image contrast, the color distinction, and the brightness of the ground objects. In the quantitative assessment, three kinds of statistical indices, correlation coefficient, ERGAS, and Q index, were calculated by band and land-cover type. These scores were relatively superior in bands 2 and 3 compared with the other two bands, especially over urban and agricultural areas. Band 4 showed a strong dependency on the land-cover type. This was attributable to the differences in the observing spectral wavelengths of the sensors and local scene variances.

  11. Undertaking qualitative health research in social virtual worlds.

    Science.gov (United States)

    McElhinney, Evelyn; Cheater, Francine M; Kidd, Lisa

    2014-06-01

    This paper discusses the methodological challenges of using the 3D social virtual world Second Life for research and offers some solutions on a range of research issues including research ethics committee approval, gaining consent, recruitment of sample, data collection and engagement with 'in - world culture'. The attraction of social virtual worlds to researchers is their ability to mimic the physical world, as they, are seen as 'places' where people have a feeling of presence (being there) and social presence (being there with others) through the use of a 'customisable' avatar (digital self-representation). Emerging research demonstrating the persuasive nature of avatars on health behaviours through virtual worlds, online games and the 3D web has increased the use of and interest in these areas for delivering health information, advice and support. However, conducting research can be challenging in a 3D world where people are represented as anonymous avatars in an environment unlike any other online media. 25 semi-structured interviews were conducted in Second Life from September 2011-June 2012. Nurses wishing to undertake research in social virtual worlds should spend time in-world to acquire technical skills and gain an understanding of the culture of the world. Our experience of an interview-based study in virtual worlds indicates that researchers require several virtual world technical skills to create innovative tools to recruit, gain consent and collect data and an understanding of in-world culture, language and social norms to increase the chances of successful research. © 2013 John Wiley & Sons Ltd.

  12. SAW Classification Algorithm for Chinese Text Classification

    OpenAIRE

    Xiaoli Guo; Huiyu Sun; Tiehua Zhou; Ling Wang; Zhaoyang Qu; Jiannan Zang

    2015-01-01

    Considering the explosive growth of data, the increased amount of text data’s effect on the performance of text categorization forward the need for higher requirements, such that the existing classification method cannot be satisfied. Based on the study of existing text classification technology and semantics, this paper puts forward a kind of Chinese text classification oriented SAW (Structural Auxiliary Word) algorithm. The algorithm uses the special space effect of Chinese text where words...

  13. Classification in context

    DEFF Research Database (Denmark)

    Mai, Jens Erik

    2004-01-01

    This paper surveys classification research literature, discusses various classification theories, and shows that the focus has traditionally been on establishing a scientific foundation for classification research. This paper argues that a shift has taken place, and suggests that contemporary...... classification research focus on contextual information as the guide for the design and construction of classification schemes....

  14. Photon level chemical classification using digital compressive detection

    International Nuclear Information System (INIS)

    Wilcox, David S.; Buzzard, Gregery T.; Lucier, Bradley J.; Wang Ping; Ben-Amotz, Dor

    2012-01-01

    Highlights: ► A new digital compressive detection strategy is developed. ► Chemical classification demonstrated using as few as ∼10 photons. ► Binary filters are optimal when taking few measurements. - Abstract: A key bottleneck to high-speed chemical analysis, including hyperspectral imaging and monitoring of dynamic chemical processes, is the time required to collect and analyze hyperspectral data. Here we describe, both theoretically and experimentally, a means of greatly speeding up the collection of such data using a new digital compressive detection strategy. Our results demonstrate that detecting as few as ∼10 Raman scattered photons (in as little time as ∼30 μs) can be sufficient to positively distinguish chemical species. This is achieved by measuring the Raman scattered light intensity transmitted through programmable binary optical filters designed to minimize the error in the chemical classification (or concentration) variables of interest. The theoretical results are implemented and validated using a digital compressive detection instrument that incorporates a 785 nm diode excitation laser, digital micromirror spatial light modulator, and photon counting photodiode detector. Samples consisting of pairs of liquids with different degrees of spectral overlap (including benzene/acetone and n-heptane/n-octane) are used to illustrate how the accuracy of the present digital compressive detection method depends on the correlation coefficients of the corresponding spectra. Comparisons of measured and predicted chemical classification score plots, as well as linear and non-linear discriminant analyses, demonstrate that this digital compressive detection strategy is Poisson photon noise limited and outperforms total least squares-based compressive detection with analog filters.

  15. Spectral image reconstruction using an edge preserving spatio-spectral Wiener estimation.

    Science.gov (United States)

    Urban, Philipp; Rosen, Mitchell R; Berns, Roy S

    2009-08-01

    Reconstruction of spectral images from camera responses is investigated using an edge preserving spatio-spectral Wiener estimation. A Wiener denoising filter and a spectral reconstruction Wiener filter are combined into a single spatio-spectral filter using local propagation of the noise covariance matrix. To preserve edges the local mean and covariance matrix of camera responses is estimated by bilateral weighting of neighboring pixels. We derive the edge-preserving spatio-spectral Wiener estimation by means of Bayesian inference and show that it fades into the standard Wiener reflectance estimation shifted by a constant reflectance in case of vanishing noise. Simulation experiments conducted on a six-channel camera system and on multispectral test images show the performance of the filter, especially for edge regions. A test implementation of the method is provided as a MATLAB script at the first author's website.

  16. Classification

    DEFF Research Database (Denmark)

    Hjørland, Birger

    2017-01-01

    This article presents and discusses definitions of the term “classification” and the related concepts “Concept/conceptualization,”“categorization,” “ordering,” “taxonomy” and “typology.” It further presents and discusses theories of classification including the influences of Aristotle...... and Wittgenstein. It presents different views on forming classes, including logical division, numerical taxonomy, historical classification, hermeneutical and pragmatic/critical views. Finally, issues related to artificial versus natural classification and taxonomic monism versus taxonomic pluralism are briefly...

  17. Application of PCA and SIMCA statistical analysis of FT-IR spectra for the classification and identification of different slag types with environmental origin.

    Science.gov (United States)

    Stumpe, B; Engel, T; Steinweg, B; Marschner, B

    2012-04-03

    In the past, different slag materials were often used for landscaping and construction purposes or simply dumped. Nowadays German environmental laws strictly control the use of slags, but there is still a remaining part of 35% which is uncontrolled dumped in landfills. Since some slags have high heavy metal contents and different slag types have typical chemical and physical properties that will influence the risk potential and other characteristics of the deposits, an identification of the slag types is needed. We developed a FT-IR-based statistical method to identify different slags classes. Slags samples were collected at different sites throughout various cities within the industrial Ruhr area. Then, spectra of 35 samples from four different slags classes, ladle furnace (LF), blast furnace (BF), oxygen furnace steel (OF), and zinc furnace slags (ZF), were determined in the mid-infrared region (4000-400 cm(-1)). The spectra data sets were subject to statistical classification methods for the separation of separate spectral data of different slag classes. Principal component analysis (PCA) models for each slag class were developed and further used for soft independent modeling of class analogy (SIMCA). Precise classification of slag samples into four different slag classes were achieved using two different SIMCA models stepwise. At first, SIMCA 1 was used for classification of ZF as well as OF slags over the total spectral range. If no correct classification was found, then the spectrum was analyzed with SIMCA 2 at reduced wavenumbers for the classification of LF as well as BF spectra. As a result, we provide a time- and cost-efficient method based on FT-IR spectroscopy for processing and identifying large numbers of environmental slag samples.

  18. Spectral features based tea garden extraction from digital orthophoto maps

    Science.gov (United States)

    Jamil, Akhtar; Bayram, Bulent; Kucuk, Turgay; Zafer Seker, Dursun

    2018-05-01

    The advancements in the photogrammetry and remote sensing technologies has made it possible to extract useful tangible information from data which plays a pivotal role in various application such as management and monitoring of forests and agricultural lands etc. This study aimed to evaluate the effectiveness of spectral signatures for extraction of tea gardens from 1 : 5000 scaled digital orthophoto maps obtained from Rize city in Turkey. First, the normalized difference vegetation index (NDVI) was derived from the input images to suppress the non-vegetation areas. NDVI values less than zero were discarded and the output images was normalized in the range 0-255. Individual pixels were then mapped into meaningful objects using global region growing technique. The resulting image was filtered and smoothed to reduce the impact of noise. Furthermore, geometrical constraints were applied to remove small objects (less than 500 pixels) followed by morphological opening operator to enhance the results. These objects served as building blocks for further image analysis. Finally, for the classification stage, a range of spectral values were empirically calculated for each band and applied on candidate objects to extract tea gardens. For accuracy assessment, we employed an area based similarity metric by overlapping obtained tea garden boundaries with the manually digitized tea garden boundaries created by experts of photogrammetry. The overall accuracy of the proposed method scored 89 % for tea gardens from 10 sample orthophoto maps. We concluded that exploiting the spectral signatures using object based analysis is an effective technique for extraction of dominant tree species from digital orthophoto maps.

  19. Fourier-transform-infrared-spectroscopy based spectral-biomarker selection towards optimum diagnostic differentiation of oral leukoplakia and cancer.

    Science.gov (United States)

    Banerjee, Satarupa; Pal, Mousumi; Chakrabarty, Jitamanyu; Petibois, Cyril; Paul, Ranjan Rashmi; Giri, Amita; Chatterjee, Jyotirmoy

    2015-10-01

    In search of specific label-free biomarkers for differentiation of two oral lesions, namely oral leukoplakia (OLK) and oral squamous-cell carcinoma (OSCC), Fourier-transform infrared (FTIR) spectroscopy was performed on paraffin-embedded tissue sections from 47 human subjects (eight normal (NOM), 16 OLK, and 23 OSCC). Difference between mean spectra (DBMS), Mann-Whitney's U test, and forward feature selection (FFS) techniques were used for optimising spectral-marker selection. Classification of diseases was performed with linear and quadratic support vector machine (SVM) at 10-fold cross-validation, using different combinations of spectral features. It was observed that six features obtained through FFS enabled differentiation of NOM and OSCC tissue (1782, 1713, 1665, 1545, 1409, and 1161 cm(-1)) and were most significant, able to classify OLK and OSCC with 81.3 % sensitivity, 95.7 % specificity, and 89.7 % overall accuracy. The 43 spectral markers extracted through Mann-Whitney's U Test were the least significant when quadratic SVM was used. Considering the high sensitivity and specificity of the FFS technique, extracting only six spectral biomarkers was thus most useful for diagnosis of OLK and OSCC, and to overcome inter and intra-observer variability experienced in diagnostic best-practice histopathological procedure. By considering the biochemical assignment of these six spectral signatures, this work also revealed altered glycogen and keratin content in histological sections which could able to discriminate OLK and OSCC. The method was validated through spectral selection by the DBMS technique. Thus this method has potential for diagnostic cost minimisation for oral lesions by label-free biomarker identification.

  20. 78 FR 54970 - Cotton Futures Classification: Optional Classification Procedure

    Science.gov (United States)

    2013-09-09

    ... Service 7 CFR Part 27 [AMS-CN-13-0043] RIN 0581-AD33 Cotton Futures Classification: Optional Classification Procedure AGENCY: Agricultural Marketing Service, USDA. ACTION: Proposed rule. SUMMARY: The... optional cotton futures classification procedure--identified and known as ``registration'' by the U.S...

  1. Multiplex coherent anti-Stokes Raman scattering microspectroscopy of brain tissue with higher ranking data classification for biomedical imaging

    Science.gov (United States)

    Pohling, Christoph; Bocklitz, Thomas; Duarte, Alex S.; Emmanuello, Cinzia; Ishikawa, Mariana S.; Dietzeck, Benjamin; Buckup, Tiago; Uckermann, Ortrud; Schackert, Gabriele; Kirsch, Matthias; Schmitt, Michael; Popp, Jürgen; Motzkus, Marcus

    2017-06-01

    Multiplex coherent anti-Stokes Raman scattering (MCARS) microscopy was carried out to map a solid tumor in mouse brain tissue. The border between normal and tumor tissue was visualized using support vector machines (SVM) as a higher ranking type of data classification. Training data were collected separately in both tissue types, and the image contrast is based on class affiliation of the single spectra. Color coding in the image generated by SVM is then related to pathological information instead of single spectral intensities or spectral differences within the data set. The results show good agreement with the H&E stained reference and spontaneous Raman microscopy, proving the validity of the MCARS approach in combination with SVM.

  2. Study of old ecological hazards, oil seeps and contaminations using earth observation methods – spectral library for oil seep

    Directory of Open Access Journals (Sweden)

    Smejkalová Eva

    2017-03-01

    Full Text Available The possibilities of remote sensing techniques in the field of the Earth surface monitoring and protection specifically for the problems caused by petroleum contaminations, for the mapping of insufficiently plugged and abandoned old oil wells and for the analysis of onshore oil seeps are described. Explained is the methodology for analyzing and detection of potential hydrocarbon contaminations using the Earth observation in the area of interest in Slovakia (Korňa and in Czech Republic (Nesyt, mainly building and calibrating the spectral library for oil seeps. The acquisition of the in-situ field data (ASD, Cropscan spectroradiometers for this purpose, the successful building and verification of hydrocarbon spectral library, the application of hydrocarbon indexes and use of shift in red-edge part of electromagnetic spectra, the spectral analysis of input data are clarified in the paper. Described is approach which could innovate the routine methods for investigating the occurrence of hydrocarbons and can assist during the mapping and locating the potential oil seep sites. Important outcome is the successful establishment of a spectral library (database with calibration data suitable for further application in data classification for identifying the occurrence of hydrocarbons.

  3. Spectrally selective glazings

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1998-08-01

    Spectrally selective glazing is window glass that permits some portions of the solar spectrum to enter a building while blocking others. This high-performance glazing admits as much daylight as possible while preventing transmission of as much solar heat as possible. By controlling solar heat gains in summer, preventing loss of interior heat in winter, and allowing occupants to reduce electric lighting use by making maximum use of daylight, spectrally selective glazing significantly reduces building energy consumption and peak demand. Because new spectrally selective glazings can have a virtually clear appearance, they admit more daylight and permit much brighter, more open views to the outside while still providing the solar control of the dark, reflective energy-efficient glass of the past. This Federal Technology Alert provides detailed information and procedures for Federal energy managers to consider spectrally selective glazings. The principle of spectrally selective glazings is explained. Benefits related to energy efficiency and other architectural criteria are delineated. Guidelines are provided for appropriate application of spectrally selective glazing, and step-by-step instructions are given for estimating energy savings. Case studies are also presented to illustrate actual costs and energy savings. Current manufacturers, technology users, and references for further reading are included for users who have questions not fully addressed here.

  4. Spectral Electroencephalogram Analysis for the Evaluation of Encephalopathy Grade in Children With Acute Liver Failure.

    Science.gov (United States)

    Press, Craig A; Morgan, Lindsey; Mills, Michele; Stack, Cynthia V; Goldstein, Joshua L; Alonso, Estella M; Wainwright, Mark S

    2017-01-01

    Spectral electroencephalogram analysis is a method for automated analysis of electroencephalogram patterns, which can be performed at the bedside. We sought to determine the utility of spectral electroencephalogram for grading hepatic encephalopathy in children with acute liver failure. Retrospective cohort study. Tertiary care pediatric hospital. Patients between 0 and 18 years old who presented with acute liver failure and were admitted to the PICU. None. Electroencephalograms were analyzed by spectral analysis including total power, relative δ, relative θ, relative α, relative β, θ-to-Δ ratio, and α-to-Δ ratio. Normal values and ranges were first derived using normal electroencephalograms from 70 children of 0-18 years old. Age had a significant effect on each variable measured (p liver failure were available for spectral analysis. The median age was 4.3 years, 14 of 33 were male, and the majority had an indeterminate etiology of acute liver failure. Neuroimaging was performed in 26 cases and was normal in 20 cases (77%). The majority (64%) survived, and 82% had a good outcome with a score of 1-3 on the Pediatric Glasgow Outcome Scale-Extended at the time of discharge. Hepatic encephalopathy grade correlated with the qualitative visual electroencephalogram scores assigned by blinded neurophysiologists (rs = 0.493; p encephalopathy was correlated with a total power of less than or equal to 50% of normal for children 0-3 years old, and with a relative θ of less than or equal to 50% normal for children more than 3 years old (p > 0.05). Spectral electroencephalogram classification correlated with outcome (p encephalopathy and correlates with outcome. Spectral electroencephalogram may allow improved quantitative and reproducible assessment of hepatic encephalopathy grade in children with acute liver failure.

  5. Adaptive Spectral Doppler Estimation

    DEFF Research Database (Denmark)

    Gran, Fredrik; Jakobsson, Andreas; Jensen, Jørgen Arendt

    2009-01-01

    . The methods can also provide better quality of the estimated power spectral density (PSD) of the blood signal. Adaptive spectral estimation techniques are known to pro- vide good spectral resolution and contrast even when the ob- servation window is very short. The 2 adaptive techniques are tested......In this paper, 2 adaptive spectral estimation techniques are analyzed for spectral Doppler ultrasound. The purpose is to minimize the observation window needed to estimate the spectrogram to provide a better temporal resolution and gain more flexibility when designing the data acquisition sequence...... and compared with the averaged periodogram (Welch’s method). The blood power spectral capon (BPC) method is based on a standard minimum variance technique adapted to account for both averaging over slow-time and depth. The blood amplitude and phase estimation technique (BAPES) is based on finding a set...

  6. Real-time classification of humans versus animals using profiling sensors and hidden Markov tree model

    Science.gov (United States)

    Hossen, Jakir; Jacobs, Eddie L.; Chari, Srikant

    2015-07-01

    Linear pyroelectric array sensors have enabled useful classifications of objects such as humans and animals to be performed with relatively low-cost hardware in border and perimeter security applications. Ongoing research has sought to improve the performance of these sensors through signal processing algorithms. In the research presented here, we introduce the use of hidden Markov tree (HMT) models for object recognition in images generated by linear pyroelectric sensors. HMTs are trained to statistically model the wavelet features of individual objects through an expectation-maximization learning process. Human versus animal classification for a test object is made by evaluating its wavelet features against the trained HMTs using the maximum-likelihood criterion. The classification performance of this approach is compared to two other techniques; a texture, shape, and spectral component features (TSSF) based classifier and a speeded-up robust feature (SURF) classifier. The evaluation indicates that among the three techniques, the wavelet-based HMT model works well, is robust, and has improved classification performance compared to a SURF-based algorithm in equivalent computation time. When compared to the TSSF-based classifier, the HMT model has a slightly degraded performance but almost an order of magnitude improvement in computation time enabling real-time implementation.

  7. Computer implemented land cover classification using LANDSAT MSS digital data: A cooperative research project between the National Park Service and NASA. 3: Vegetation and other land cover analysis of Shenandoah National Park

    Science.gov (United States)

    Cibula, W. G.

    1981-01-01

    Four LANDSAT frames, each corresponding to one of the four seasons were spectrally classified and processed using NASA-developed computer programs. One data set was selected or two or more data sets were marged to improve surface cover classifications. Selected areas representing each spectral class were chosen and transferred to USGS 1:62,500 topographic maps for field use. Ground truth data were gathered to verify the accuracy of the classifications. Acreages were computed for each of the land cover types. The application of elevational data to seasonal LANDSAT frames resulted in the separation of high elevation meadows (both with and without recently emergent perennial vegetation) as well as areas in oak forests which have an evergreen understory as opposed to other areas which do not.

  8. Classification of refrigerants; Classification des fluides frigorigenes

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2001-07-01

    This document was made from the US standard ANSI/ASHRAE 34 published in 2001 and entitled 'designation and safety classification of refrigerants'. This classification allows to clearly organize in an international way the overall refrigerants used in the world thanks to a codification of the refrigerants in correspondence with their chemical composition. This note explains this codification: prefix, suffixes (hydrocarbons and derived fluids, azeotropic and non-azeotropic mixtures, various organic compounds, non-organic compounds), safety classification (toxicity, flammability, case of mixtures). (J.S.)

  9. Segmentation, Inference and Classification of Partially Overlapping Nanoparticles

    KAUST Repository

    Chiwoo Park,

    2013-03-01

    This paper presents a method that enables automated morphology analysis of partially overlapping nanoparticles in electron micrographs. In the undertaking of morphology analysis, three tasks appear necessary: separate individual particles from an agglomerate of overlapping nano-objects; infer the particle\\'s missing contours; and ultimately, classify the particles by shape based on their complete contours. Our specific method adopts a two-stage approach: the first stage executes the task of particle separation, and the second stage conducts simultaneously the tasks of contour inference and shape classification. For the first stage, a modified ultimate erosion process is developed for decomposing a mixture of particles into markers, and then, an edge-to-marker association method is proposed to identify the set of evidences that eventually delineate individual objects. We also provided theoretical justification regarding the separation capability of the first stage. In the second stage, the set of evidences become inputs to a Gaussian mixture model on B-splines, the solution of which leads to the joint learning of the missing contour and the particle shape. Using twelve real electron micrographs of overlapping nanoparticles, we compare the proposed method with seven state-of-the-art methods. The results show the superiority of the proposed method in terms of particle recognition rate.

  10. Single and Multi-Date Landsat Classifications of Basalt to Support Soil Survey Efforts

    Directory of Open Access Journals (Sweden)

    Jessica J. Mitchell

    2013-10-01

    Full Text Available Basalt outcrops are significant features in the Western United States and consistently present challenges to Natural Resources Conservation Service (NRCS soil mapping efforts. Current soil survey methods to estimate basalt outcrops involve field transects and are impractical for mapping regionally extensive areas. The purpose of this research was to investigate remote sensing methods to effectively determine the presence of basalt rock outcrops. Five Landsat 5 TM scenes (path 39, row 29 over the year 2007 growing season were processed and analyzed to detect and quantify basalt outcrops across the Clark Area Soil Survey, ID, USA (4,570 km2. The Robust Classification Method (RCM using the Spectral Angle Mapper (SAM method and Random Forest (RF classifications was applied to individual scenes and to a multitemporal stack of the five images. The highest performing RCM basalt classification was obtained using the 18 July scene, which yielded an overall accuracy of 60.45%. The RF classifications applied to the same datasets yielded slightly better overall classification rates when using the multitemporal stack (72.35% than when using the 18 July scene (71.13% and the same rate of successfully predicting basalt (61.76% using out-of-bag sampling. For optimal RCM and RF classifications, uncertainty tended to be lowest in irrigated areas; however, the RCM uncertainty map included more extensive areas of low uncertainty that also encompassed forested hillslopes and riparian areas. RCM uncertainty was sensitive to the influence of bright soil reflectance, while RF uncertainty was sensitive to the influence of shadows. Quantification of basalt requires continued investigation to reduce the influence of vegetation, lichen and loess on basalt detection. With further development, remote sensing tools have the potential to support soil survey mapping of lava fields covering expansive areas in the Western United States and other regions of the world with similar

  11. Algorithms for Hyperspectral Endmember Extraction and Signature Classification with Morphological Dendritic Networks

    Science.gov (United States)

    Schmalz, M.; Ritter, G.

    Accurate multispectral or hyperspectral signature classification is key to the nonimaging detection and recognition of space objects. Additionally, signature classification accuracy depends on accurate spectral endmember determination [1]. Previous approaches to endmember computation and signature classification were based on linear operators or neural networks (NNs) expressed in terms of the algebra (R, +, x) [1,2]. Unfortunately, class separation in these methods tends to be suboptimal, and the number of signatures that can be accurately classified often depends linearly on the number of NN inputs. This can lead to poor endmember distinction, as well as potentially significant classification errors in the presence of noise or densely interleaved signatures. In contrast to traditional CNNs, autoassociative morphological memories (AMM) are a construct similar to Hopfield autoassociatived memories defined on the (R, +, ?,?) lattice algebra [3]. Unlimited storage and perfect recall of noiseless real valued patterns has been proven for AMMs [4]. However, AMMs suffer from sensitivity to specific noise models, that can be characterized as erosive and dilative noise. On the other hand, the prior definition of a set of endmembers corresponds to material spectra lying on vertices of the minimum convex region covering the image data. These vertices can be characterized as morphologically independent patterns. It has further been shown that AMMs can be based on dendritic computation [3,6]. These techniques yield improved accuracy and class segmentation/separation ability in the presence of highly interleaved signature data. In this paper, we present a procedure for endmember determination based on AMM noise sensitivity, which employs morphological dendritic computation. We show that detected endmembers can be exploited by AMM based classification techniques, to achieve accurate signature classification in the presence of noise, closely spaced or interleaved signatures, and

  12. A hierarchical classification approach for recognition of low-density (LDPE) and high-density polyethylene (HDPE) in mixed plastic waste based on short-wave infrared (SWIR) hyperspectral imaging

    Science.gov (United States)

    Bonifazi, Giuseppe; Capobianco, Giuseppe; Serranti, Silvia

    2018-06-01

    The aim of this work was to recognize different polymer flakes from mixed plastic waste through an innovative hierarchical classification strategy based on hyperspectral imaging, with particular reference to low density polyethylene (LDPE) and high-density polyethylene (HDPE). A plastic waste composition assessment, including also LDPE and HDPE identification, may help to define optimal recycling strategies for product quality control. Correct handling of plastic waste is essential for its further "sustainable" recovery, maximizing the sorting performance in particular for plastics with similar characteristics as LDPE and HDPE. Five different plastic waste samples were chosen for the investigation: polypropylene (PP), LDPE, HDPE, polystyrene (PS) and polyvinyl chloride (PVC). A calibration dataset was realized utilizing the corresponding virgin polymers. Hyperspectral imaging in the short-wave infrared range (1000-2500 nm) was thus applied to evaluate the different plastic spectral attributes finalized to perform their recognition/classification. After exploring polymer spectral differences by principal component analysis (PCA), a hierarchical partial least squares discriminant analysis (PLS-DA) model was built allowing the five different polymers to be recognized. The proposed methodology, based on hierarchical classification, is very powerful and fast, allowing to recognize the five different polymers in a single step.

  13. Using LUCAS topsoil database to estimate soil organic carbon content in local spectral libraries

    Science.gov (United States)

    Castaldi, Fabio; van Wesemael, Bas; Chabrillat, Sabine; Chartin, Caroline

    2017-04-01

    The quantification of the soil organic carbon (SOC) content over large areas is mandatory to obtain accurate soil characterization and classification, which can improve site specific management at local or regional scale exploiting the strong relationship between SOC and crop growth. The estimation of the SOC is not only important for agricultural purposes: in recent years, the increasing attention towards global warming highlighted the crucial role of the soil in the global carbon cycle. In this context, soil spectroscopy is a well consolidated and widespread method to estimate soil variables exploiting the interaction between chromophores and electromagnetic radiation. The importance of spectroscopy in soil science is reflected by the increasing number of large soil spectral libraries collected in the world. These large libraries contain soil samples derived from a consistent number of pedological regions and thus from different parent material and soil types; this heterogeneity entails, in turn, a large variability in terms of mineralogical and organic composition. In the light of the huge variability of the spectral responses to SOC content and composition, a rigorous classification process is necessary to subset large spectral libraries and to avoid the calibration of global models failing to predict local variation in SOC content. In this regard, this study proposes a method to subset the European LUCAS topsoil database into soil classes using a clustering analysis based on a large number of soil properties. The LUCAS database was chosen to apply a standardized multivariate calibration approach valid for large areas without the need for extensive field and laboratory work for calibration of local models. Seven soil classes were detected by the clustering analyses and the samples belonging to each class were used to calibrate specific partial least square regression (PLSR) models to estimate SOC content of three local libraries collected in Belgium (Loam belt

  14. Spectral biclustering of microarray data: coclustering genes and conditions.

    Science.gov (United States)

    Kluger, Yuval; Basri, Ronen; Chang, Joseph T; Gerstein, Mark

    2003-04-01

    Global analyses of RNA expression levels are useful for classifying genes and overall phenotypes. Often these classification problems are linked, and one wants to find "marker genes" that are differentially expressed in particular sets of "conditions." We have developed a method that simultaneously clusters genes and conditions, finding distinctive "checkerboard" patterns in matrices of gene expression data, if they exist. In a cancer context, these checkerboards correspond to genes that are markedly up- or downregulated in patients with particular types of tumors. Our method, spectral biclustering, is based on the observation that checkerboard structures in matrices of expression data can be found in eigenvectors corresponding to characteristic expression patterns across genes or conditions. In addition, these eigenvectors can be readily identified by commonly used linear algebra approaches, in particular the singular value decomposition (SVD), coupled with closely integrated normalization steps. We present a number of variants of the approach, depending on whether the normalization over genes and conditions is done independently or in a coupled fashion. We then apply spectral biclustering to a selection of publicly available cancer expression data sets, and examine the degree to which the approach is able to identify checkerboard structures. Furthermore, we compare the performance of our biclustering methods against a number of reasonable benchmarks (e.g., direct application of SVD or normalized cuts to raw data).

  15. Comparing the performance of flat and hierarchical Habitat/Land-Cover classification models in a NATURA 2000 site

    Science.gov (United States)

    Gavish, Yoni; O'Connell, Jerome; Marsh, Charles J.; Tarantino, Cristina; Blonda, Palma; Tomaselli, Valeria; Kunin, William E.

    2018-02-01

    The increasing need for high quality Habitat/Land-Cover (H/LC) maps has triggered considerable research into novel machine-learning based classification models. In many cases, H/LC classes follow pre-defined hierarchical classification schemes (e.g., CORINE), in which fine H/LC categories are thematically nested within more general categories. However, none of the existing machine-learning algorithms account for this pre-defined hierarchical structure. Here we introduce a novel Random Forest (RF) based application of hierarchical classification, which fits a separate local classification model in every branching point of the thematic tree, and then integrates all the different local models to a single global prediction. We applied the hierarchal RF approach in a NATURA 2000 site in Italy, using two land-cover (CORINE, FAO-LCCS) and one habitat classification scheme (EUNIS) that differ from one another in the shape of the class hierarchy. For all 3 classification schemes, both the hierarchical model and a flat model alternative provided accurate predictions, with kappa values mostly above 0.9 (despite using only 2.2-3.2% of the study area as training cells). The flat approach slightly outperformed the hierarchical models when the hierarchy was relatively simple, while the hierarchical model worked better under more complex thematic hierarchies. Most misclassifications came from habitat pairs that are thematically distant yet spectrally similar. In 2 out of 3 classification schemes, the additional constraints of the hierarchical model resulted with fewer such serious misclassifications relative to the flat model. The hierarchical model also provided valuable information on variable importance which can shed light into "black-box" based machine learning algorithms like RF. We suggest various ways by which hierarchical classification models can increase the accuracy and interpretability of H/LC classification maps.

  16. Building confidence: an exploration of nurses undertaking a postgraduate biological science course.

    Science.gov (United States)

    Van Wissen, Kim; McBride-Henry, Karen

    2010-01-01

    This study aimed to explore the impact of studying biological science at a postgraduate level and how this impacted on nursing practice. The term biological sciences in this research encompasses elements of physiology, genetics, biochemistry and pathophysiology. A qualitative research study was designed, that involved the dissemination of a pre- and post-course semi-structured questionnaire for a biological science course, as part of a Master of Nursing programme at a New Zealand University, thus exploring the impact of undertaking a postgraduate biological sciences course. The responses were analysed into themes, based on interpretive concepts. The primary themes revealed improvement in confidence as: confidence in communication, confidence in linking nursing theoretical knowledge to practice and confidence in clinical nursing knowledge. This study highlights the need to privilege clinically-derived nursing knowledge, and that confidence in this nursing knowledge and clinical practice can be instilled through employing the model of theory-guided practice.

  17. Effects of Two Linguistically Proximal Varieties on the Spectral and Coarticulatory Properties of Fricatives: Evidence from Athenian Greek and Cypriot Greek

    Directory of Open Access Journals (Sweden)

    Charalambos Themistocleous

    2017-11-01

    Full Text Available Several studies have explored the acoustic structure of fricatives, yet there has been very little acoustic research on the effects of dialects on the production of fricatives. This article investigates the effects of two linguistically proximal Modern Greek dialects, Athenian Greek and Cypriot Greek on the temporal, spectral, and coarticulatory properties of fricatives and aims to determine the acoustic properties that convey information about these two dialects. Productions of voiced and voiceless labiodental, dental, alveolar, palatal, and velar fricatives were extracted from a speaking task from typically speaking female adult speakers (25 Cypriot Greek and 20 Athenian Greek speakers. Measures were made of spectral properties, using a spectral moments analysis. The formants of the following vowel were measured and second degree polynomials of the formant contours were calculated. The findings showed that Athenian Greek and Cypriot Greek fricatives differ in all spectral properties across all places of articulation. Also, the co-articulatory effects of fricatives on following vowel were different depending on the dialect. Duration, spectral moments, and the starting frequencies of F1, F2, F3, and F4 contributed the most to the classification of dialect. These findings provide a solid evidence base for the manifestation of dialectal information in the acoustic structure of fricatives.

  18. Spectral multitude and spectral dynamics reflect changing conjugation length in single molecules of oligophenylenevinylenes

    KAUST Repository

    Kobayashi, Hiroyuki; Tsuchiya, Kousuke; Ogino, Kenji; Vacha, Martin

    2012-01-01

    Single-molecule study of phenylenevinylene oligomers revealed distinct spectral forms due to different conjugation lengths which are determined by torsional defects. Large spectral jumps between different spectral forms were ascribed to torsional flips of a single phenylene ring. These spectral changes reflect the dynamic nature of electron delocalization in oligophenylenevinylenes and enable estimation of the phenylene torsional barriers. © 2012 The Owner Societies.

  19. Spectral characterization of volcanic rocks in the VIS-NIR for martian exploration

    Science.gov (United States)

    De Angelis, Simone; Carli, Cristian; Manzari, Paola; De Sanctis, Maria Cristina; Capaccioni, Fabrizio

    2016-10-01

    Igneous effusive rocks cover much of the surface of Mars [1,2,3]. Initially only two types of lithologies were thought to constitute the Martian crust, i.e. a basaltic one and a more andesitic one [1,2], while more evolved lithologies were ruled out.Nevertheless a more complex situation is appearing in the last years. Recently several observations have highlighted the presence of evolved, acidic rocks. High-silica dacite units were identified in Syrtis Major caldera by thermal IR data [4]. Outcrops in Noachis Terra were interpreted as constituted of felsic (i.e. feldspar-rich) rocks essentially by the observation of a 1.3-µm spectral feature in CRISM data, attributed to Fe2+ in feldspars [5]. However different interpretations exist, invoking plagioclase-enriched basalts [6] rather than felsic products.The increasing of high-resolution and in-situ rover-based observations datasets and the changing of the initial paradigm justify a new systematic spectral study of igneous effusive rocks. In this work we focus on the spectral characterization of volcanic effusive rocks in the 0.35-2.5-µm range. We are carrying out measurements and spectral analyses on a wide ensemble of effusive samples, from mafic to sialic, with variable alkali contents, following the classification in the Total-Alkali-Silica diagram, and discussing the influence on spectral characteristics of different mineral assemblages and/or texture ([7], [8]). [1] Bandfield J.L., et al., Science, 287, 1626, 2000; [2] Christensen P.R., et al., J. Geophys. Res., 105, N.E4, 9609-9621, 2000; [3] Ehlmann B.L. & Edwards C.S., Annu. Rev. Earth Planet. Sci., 42, 291-315, 2014; [4] Christensen P.R., et al., Nature, 436, 504-509, 2005; [5] Wray J.J., et al., 44th LPSC, abs. n.3065, 2013; [6] Rogers A.D. & Nekvasil H., Geophys. Res. Lett., 42, 2619-2626, 2015; [7] Carli C. and Sgavetti M.,Icarus, 211, 1034-1048, 2011; [7] Carli C. et al., SGL, doi 10.1144/SP401.19, 2015.

  20. 3D Spatial and Spectral Fusion of Terrestrial Hyperspectral Imagery and Lidar for Hyperspectral Image Shadow Restoration Applied to a Geologic Outcrop

    Science.gov (United States)

    Hartzell, P. J.; Glennie, C. L.; Hauser, D. L.; Okyay, U.; Khan, S.; Finnegan, D. C.

    2016-12-01

    Recent advances in remote sensing technology have expanded the acquisition and fusion of active lidar and passive hyperspectral imagery (HSI) from an exclusively airborne technique to terrestrial modalities. This enables high resolution 3D spatial and spectral quantification of vertical geologic structures for applications such as virtual 3D rock outcrop models for hydrocarbon reservoir analog analysis and mineral quantification in open pit mining environments. In contrast to airborne observation geometry, the vertical surfaces observed by horizontal-viewing terrestrial HSI sensors are prone to extensive topography-induced solar shadowing, which leads to reduced pixel classification accuracy or outright removal of shadowed pixels from analysis tasks. Using a precisely calibrated and registered offset cylindrical linear array camera model, we demonstrate the use of 3D lidar data for sub-pixel HSI shadow detection and the restoration of the shadowed pixel spectra via empirical methods that utilize illuminated and shadowed pixels of similar material composition. We further introduce a new HSI shadow restoration technique that leverages collocated backscattered lidar intensity, which is resistant to solar conditions, obtained by projecting the 3D lidar points through the HSI camera model into HSI pixel space. Using ratios derived from the overlapping lidar laser and HSI wavelengths, restored shadow pixel spectra are approximated using a simple scale factor. Simulations of multiple lidar wavelengths, i.e., multi-spectral lidar, indicate the potential for robust HSI spectral restoration that is independent of the complexity and costs associated with rigorous radiometric transfer models, which have yet to be developed for horizontal-viewing terrestrial HSI sensors. The spectral restoration performance is quantified through HSI pixel classification consistency between full sun and partial sun exposures of a single geologic outcrop.

  1. A multi-tier higher order Conditional Random Field for land cover classification of multi-temporal multi-spectral Landsat imagery

    CSIR Research Space (South Africa)

    Salmon, BP

    2015-07-01

    Full Text Available In this paper the authors present a 2-tier higher order Conditional Random Field which is used for land cover classification. The Conditional Random Field is based on probabilistic messages being passed along a graph to compute efficiently...

  2. SPECTRAL CLASSIFICATION OF GALAXIES AT 0.5 ≤ z ≤ 1 IN THE CDFS: THE ARTIFICIAL NEURAL NETWORK APPROACH

    International Nuclear Information System (INIS)

    Teimoorinia, H.

    2012-01-01

    The aim of this work is to combine spectral energy distribution (SED) fitting with artificial neural network techniques to assign spectral characteristics to a sample of galaxies at 0.5 < z < 1. The sample is selected from the spectroscopic campaign of the ESO/GOODS-South field, with 142 sources having photometric data from the GOODS-MUSIC catalog covering bands between ∼0.4 and 24 μm in 10-13 filters. We use the CIGALE code to fit photometric data to Maraston's synthesis spectra to derive mass, specific star formation rate, and age, as well as the best SED of the galaxies. We use the spectral models presented by Kinney et al. as targets in the wavelength interval ∼1200-7500 Å. Then a series of neural networks are trained, with average performance ∼90%, to classify the best SED in a supervised manner. We consider the effects of the prominent features of the best SED on the performance of the trained networks and also test networks on the galaxy spectra of Coleman et al., which have a lower resolution than the target models. In this way, we conclude that the trained networks take into account all the features of the spectra simultaneously. Using the method, 105 out of 142 galaxies of the sample are classified with high significance. The locus of the classified galaxies in the three graphs of the physical parameters of mass, age, and specific star formation rate appears consistent with the morphological characteristics of the galaxies.

  3. Classification of hydrocephalus: critical analysis of classification categories and advantages of "Multi-categorical Hydrocephalus Classification" (Mc HC).

    Science.gov (United States)

    Oi, Shizuo

    2011-10-01

    Hydrocephalus is a complex pathophysiology with disturbed cerebrospinal fluid (CSF) circulation. There are numerous numbers of classification trials published focusing on various criteria, such as associated anomalies/underlying lesions, CSF circulation/intracranial pressure patterns, clinical features, and other categories. However, no definitive classification exists comprehensively to cover the variety of these aspects. The new classification of hydrocephalus, "Multi-categorical Hydrocephalus Classification" (Mc HC), was invented and developed to cover the entire aspects of hydrocephalus with all considerable classification items and categories. Ten categories include "Mc HC" category I: onset (age, phase), II: cause, III: underlying lesion, IV: symptomatology, V: pathophysiology 1-CSF circulation, VI: pathophysiology 2-ICP dynamics, VII: chronology, VII: post-shunt, VIII: post-endoscopic third ventriculostomy, and X: others. From a 100-year search of publication related to the classification of hydrocephalus, 14 representative publications were reviewed and divided into the 10 categories. The Baumkuchen classification graph made from the round o'clock classification demonstrated the historical tendency of deviation to the categories in pathophysiology, either CSF or ICP dynamics. In the preliminary clinical application, it was concluded that "Mc HC" is extremely effective in expressing the individual state with various categories in the past and present condition or among the compatible cases of hydrocephalus along with the possible chronological change in the future.

  4. Classification

    Science.gov (United States)

    Clary, Renee; Wandersee, James

    2013-01-01

    In this article, Renee Clary and James Wandersee describe the beginnings of "Classification," which lies at the very heart of science and depends upon pattern recognition. Clary and Wandersee approach patterns by first telling the story of the "Linnaean classification system," introduced by Carl Linnacus (1707-1778), who is…

  5. Spectral discrimination of giant reed (Arundo donax L.): A seasonal study in riparian areas

    Science.gov (United States)

    Fernandes, Maria Rosário; Aguiar, Francisca C.; Silva, João M. N.; Ferreira, Maria Teresa; Pereira, José M. C.

    2013-06-01

    The giant reed (Arundo donax L.) is amongst the one hundred worst invasive alien species of the world, and it is responsible for biodiversity loss and failure of ecosystem functions in riparian habitats. In this work, field spectroradiometry was used to assess the spectral separability of the giant reed from the adjacent vegetation and from the common reed, a native similar species. The study was conducted at different phenological periods and also for the giant reed stands regenerated after mechanical cutting (giant reed_RAC). A hierarchical procedure using Kruskal-Wallis test followed by Classification and Regression Trees (CART) was used to select the minimum number of optimal bands that discriminate the giant reed from the adjacent vegetation. A new approach was used to identify sets of wavelengths - wavezones - that maximize the spectral separability beyond the minimum number of optimal bands. Jeffries Matusita and Bhattacharya distance were used to evaluate the spectral separability using the minimum optimal bands and in three simulated satellite images, namely Landsat, IKONOS and SPOT. Giant reed was spectrally separable from the adjacent vegetation, both at the vegetative and the senescent period, exception made to the common reed at the vegetative period. The red edge region was repeatedly selected, although the visible region was also important to separate the giant reed from the herbaceous vegetation and the mid infrared region to the discrimination from the woody vegetation. The highest separability was obtained for the giant reed_RAC stands, due to its highly homogeneous, dense and dark-green stands. Results are discussed by relating the phenological, morphological and structural features of the giant reed stands and the adjacent vegetation with their optical traits. Weaknesses and strengths of the giant reed spectral discrimination are highlighted and implications of imagery selection for mapping purposes are argued based on present results.

  6. [Review of digital ground object spectral library].

    Science.gov (United States)

    Zhou, Xiao-Hu; Zhou, Ding-Wu

    2009-06-01

    A higher spectral resolution is the main direction of developing remote sensing technology, and it is quite important to set up the digital ground object reflectance spectral database library, one of fundamental research fields in remote sensing application. Remote sensing application has been increasingly relying on ground object spectral characteristics, and quantitative analysis has been developed to a new stage. The present article summarized and systematically introduced the research status quo and development trend of digital ground object reflectance spectral libraries at home and in the world in recent years. Introducing the spectral libraries has been established, including desertification spectral database library, plants spectral database library, geological spectral database library, soil spectral database library, minerals spectral database library, cloud spectral database library, snow spectral database library, the atmosphere spectral database library, rocks spectral database library, water spectral database library, meteorites spectral database library, moon rock spectral database library, and man-made materials spectral database library, mixture spectral database library, volatile compounds spectral database library, and liquids spectral database library. In the process of establishing spectral database libraries, there have been some problems, such as the lack of uniform national spectral database standard and uniform standards for the ground object features as well as the comparability between different databases. In addition, data sharing mechanism can not be carried out, etc. This article also put forward some suggestions on those problems.

  7. A study of earthquake-induced building detection by object oriented classification approach

    Science.gov (United States)

    Sabuncu, Asli; Damla Uca Avci, Zehra; Sunar, Filiz

    2017-04-01

    Among the natural hazards, earthquakes are the most destructive disasters and cause huge loss of lives, heavily infrastructure damages and great financial losses every year all around the world. According to the statistics about the earthquakes, more than a million earthquakes occur which is equal to two earthquakes per minute in the world. Natural disasters have brought more than 780.000 deaths approximately % 60 of all mortality is due to the earthquakes after 2001. A great earthquake took place at 38.75 N 43.36 E in the eastern part of Turkey in Van Province on On October 23th, 2011. 604 people died and about 4000 buildings seriously damaged and collapsed after this earthquake. In recent years, the use of object oriented classification approach based on different object features, such as spectral, textural, shape and spatial information, has gained importance and became widespread for the classification of high-resolution satellite images and orthophotos. The motivation of this study is to detect the collapsed buildings and debris areas after the earthquake by using very high-resolution satellite images and orthophotos with the object oriented classification and also see how well remote sensing technology was carried out in determining the collapsed buildings. In this study, two different land surfaces were selected as homogenous and heterogeneous case study areas. In the first step of application, multi-resolution segmentation was applied and optimum parameters were selected to obtain the objects in each area after testing different color/shape and compactness/smoothness values. In the next step, two different classification approaches, namely "supervised" and "unsupervised" approaches were applied and their classification performances were compared. Object-based Image Analysis (OBIA) was performed using e-Cognition software.

  8. Multilabel user classification using the community structure of online networks.

    Science.gov (United States)

    Rizos, Georgios; Papadopoulos, Symeon; Kompatsiaris, Yiannis

    2017-01-01

    We study the problem of semi-supervised, multi-label user classification of networked data in the online social platform setting. We propose a framework that combines unsupervised community extraction and supervised, community-based feature weighting before training a classifier. We introduce Approximate Regularized Commute-Time Embedding (ARCTE), an algorithm that projects the users of a social graph onto a latent space, but instead of packing the global structure into a matrix of predefined rank, as many spectral and neural representation learning methods do, it extracts local communities for all users in the graph in order to learn a sparse embedding. To this end, we employ an improvement of personalized PageRank algorithms for searching locally in each user's graph structure. Then, we perform supervised community feature weighting in order to boost the importance of highly predictive communities. We assess our method performance on the problem of user classification by performing an extensive comparative study among various recent methods based on graph embeddings. The comparison shows that ARCTE significantly outperforms the competition in almost all cases, achieving up to 35% relative improvement compared to the second best competing method in terms of F1-score.

  9. Multilabel user classification using the community structure of online networks.

    Directory of Open Access Journals (Sweden)

    Georgios Rizos

    Full Text Available We study the problem of semi-supervised, multi-label user classification of networked data in the online social platform setting. We propose a framework that combines unsupervised community extraction and supervised, community-based feature weighting before training a classifier. We introduce Approximate Regularized Commute-Time Embedding (ARCTE, an algorithm that projects the users of a social graph onto a latent space, but instead of packing the global structure into a matrix of predefined rank, as many spectral and neural representation learning methods do, it extracts local communities for all users in the graph in order to learn a sparse embedding. To this end, we employ an improvement of personalized PageRank algorithms for searching locally in each user's graph structure. Then, we perform supervised community feature weighting in order to boost the importance of highly predictive communities. We assess our method performance on the problem of user classification by performing an extensive comparative study among various recent methods based on graph embeddings. The comparison shows that ARCTE significantly outperforms the competition in almost all cases, achieving up to 35% relative improvement compared to the second best competing method in terms of F1-score.

  10. Multi-agent Negotiation Mechanisms for Statistical Target Classification in Wireless Multimedia Sensor Networks

    Directory of Open Access Journals (Sweden)

    Sheng Wang

    2007-10-01

    Full Text Available The recent availability of low cost and miniaturized hardware has allowedwireless sensor networks (WSNs to retrieve audio and video data in real worldapplications, which has fostered the development of wireless multimedia sensor networks(WMSNs. Resource constraints and challenging multimedia data volume makedevelopment of efficient algorithms to perform in-network processing of multimediacontents imperative. This paper proposes solving problems in the domain of WMSNs fromthe perspective of multi-agent systems. The multi-agent framework enables flexible networkconfiguration and efficient collaborative in-network processing. The focus is placed ontarget classification in WMSNs where audio information is retrieved by microphones. Todeal with the uncertainties related to audio information retrieval, the statistical approachesof power spectral density estimates, principal component analysis and Gaussian processclassification are employed. A multi-agent negotiation mechanism is specially developed toefficiently utilize limited resources and simultaneously enhance classification accuracy andreliability. The negotiation is composed of two phases, where an auction based approach isfirst exploited to allocate the classification task among the agents and then individual agentdecisions are combined by the committee decision mechanism. Simulation experiments withreal world data are conducted and the results show that the proposed statistical approachesand negotiation mechanism not only reduce memory and computation requi

  11. Using a Combination of Spectral and Textural Data to Measure Water-Holding Capacity in Fresh Chicken Breast Fillets

    Directory of Open Access Journals (Sweden)

    Beibei Jia

    2018-02-01

    Full Text Available The aim here was to explore the potential of visible and near-infrared (Vis/NIR hyperspectral imaging (400–1000 nm to classify fresh chicken breast fillets into different water-holding capacity (WHC groups. Initially, the extracted spectra and image textural features, as well as the mixed data of the two, were used to develop partial least square-discriminant analysis (PLS-DA classification models. Smoothing, a first derivative process, and principle component analysis (PCA were carried out sequentially on the mean spectra of all samples to deal with baseline offsets and identify outlier data. Six samples located outside the confidence ellipses of 95% confidence level in the score plot were defined as outliers. A PLS-DA model based on the outlier-free spectra provided a correct classification rate (CCR value of 78% in the prediction set. Then, seven optimal wavelengths selected using a successive projections algorithm (SPA were used to develop a simplified PLS-DA model that obtained a slightly reduced CCR with a value of 73%. Moreover, the gray-level co-occurrence matrix (GLCM was implemented on the first principle component image (with 98.13% of variance of the hyperspectral image to extract textural features (contrast, correlation, energy, and homogeneity. The CCR of the model developed using textural variables was less optimistic with a value of 59%. Compared to results of models based on spectral or textural data individually, the performance of the model based on the mixed data of optimal spectral and textural features was the best with an improved CCR of 86%. The results showed that the spectral and textural data of hyperspectral images together can be integrated in order to measure and classify the WHC of fresh chicken breast fillets.

  12. Spatio-Spectral Method for Estimating Classified Regions with High Confidence using MODIS Data

    International Nuclear Information System (INIS)

    Katiyal, Anuj; Rajan, Dr K S

    2014-01-01

    In studies like change analysis, the availability of very high resolution (VHR)/high resolution (HR) imagery for a particular period and region is a challenge due to the sensor revisit times and high cost of acquisition. Therefore, most studies prefer lower resolution (LR) sensor imagery with frequent revisit times, in addition to their cost and computational advantages. Further, the classification techniques provide us a global estimate of the class accuracy, which limits its utility if the accuracy is low. In this work, we focus on the sub-classification problem of LR images and estimate regions of higher confidence than the global classification accuracy within its classified region. The spectrally classified data was mined into spatially clustered regions and further refined and processed using statistical measures to arrive at local high confidence regions (LHCRs), for every class. Rabi season MODIS data of January 2006 and 2007 was used for this study and the evaluation of LHCR was done using the APLULC 2005 classified data. For Jan-2007, the global class accuracies for water bodies (WB), forested regions (FR) and Kharif crops and barren lands (KB) were 89%, 71.7% and 71.23% respectively, while the respective LHCRs had accuracies of 96.67%, 89.4% and 80.9% covering an area of 46%, 29% and 14.5% of the initially classified areas. Though areas are reduced, LHCRs with higher accuracies help in extracting more representative class regions. Identification of such regions can facilitate in improving the classification time and processing for HR images when combined with the more frequently acquired LR imagery, isolate pure vs. mixed/impure pixels and as training samples locations for HR imagery

  13. SpectralNET – an application for spectral graph analysis and visualization

    Directory of Open Access Journals (Sweden)

    Schreiber Stuart L

    2005-10-01

    Full Text Available Abstract Background Graph theory provides a computational framework for modeling a variety of datasets including those emerging from genomics, proteomics, and chemical genetics. Networks of genes, proteins, small molecules, or other objects of study can be represented as graphs of nodes (vertices and interactions (edges that can carry different weights. SpectralNET is a flexible application for analyzing and visualizing these biological and chemical networks. Results Available both as a standalone .NET executable and as an ASP.NET web application, SpectralNET was designed specifically with the analysis of graph-theoretic metrics in mind, a computational task not easily accessible using currently available applications. Users can choose either to upload a network for analysis using a variety of input formats, or to have SpectralNET generate an idealized random network for comparison to a real-world dataset. Whichever graph-generation method is used, SpectralNET displays detailed information about each connected component of the graph, including graphs of degree distribution, clustering coefficient by degree, and average distance by degree. In addition, extensive information about the selected vertex is shown, including degree, clustering coefficient, various distance metrics, and the corresponding components of the adjacency, Laplacian, and normalized Laplacian eigenvectors. SpectralNET also displays several graph visualizations, including a linear dimensionality reduction for uploaded datasets (Principal Components Analysis and a non-linear dimensionality reduction that provides an elegant view of global graph structure (Laplacian eigenvectors. Conclusion SpectralNET provides an easily accessible means of analyzing graph-theoretic metrics for data modeling and dimensionality reduction. SpectralNET is publicly available as both a .NET application and an ASP.NET web application from http://chembank.broad.harvard.edu/resources/. Source code is

  14. (LMRG): Microscope Resolution, Objective Quality, Spectral Accuracy and Spectral Un-mixing

    Science.gov (United States)

    Bayles, Carol J.; Cole, Richard W.; Eason, Brady; Girard, Anne-Marie; Jinadasa, Tushare; Martin, Karen; McNamara, George; Opansky, Cynthia; Schulz, Katherine; Thibault, Marc; Brown, Claire M.

    2012-01-01

    The second study by the LMRG focuses on measuring confocal laser scanning microscope (CLSM) resolution, objective lens quality, spectral imaging accuracy and spectral un-mixing. Affordable test samples for each aspect of the study were designed, prepared and sent to 116 labs from 23 countries across the globe. Detailed protocols were designed for the three tests and customized for most of the major confocal instruments being used by the study participants. One protocol developed for measuring resolution and objective quality was recently published in Nature Protocols (Cole, R. W., T. Jinadasa, et al. (2011). Nature Protocols 6(12): 1929–1941). The first study involved 3D imaging of sub-resolution fluorescent microspheres to determine the microscope point spread function. Results of the resolution studies as well as point spread function quality (i.e. objective lens quality) from 140 different objective lenses will be presented. The second study of spectral accuracy looked at the reflection of the laser excitation lines into the spectral detection in order to determine the accuracy of these systems to report back the accurate laser emission wavelengths. Results will be presented from 42 different spectral confocal systems. Finally, samples with double orange beads (orange core and orange coating) were imaged spectrally and the imaging software was used to un-mix fluorescence signals from the two orange dyes. Results from 26 different confocal systems will be summarized. Time will be left to discuss possibilities for the next LMRG study.

  15. Characterizing CDOM Spectral Variability Across Diverse Regions and Spectral Ranges

    Science.gov (United States)

    Grunert, Brice K.; Mouw, Colleen B.; Ciochetto, Audrey B.

    2018-01-01

    Satellite remote sensing of colored dissolved organic matter (CDOM) has focused on CDOM absorption (aCDOM) at a reference wavelength, as its magnitude provides insight into the underwater light field and large-scale biogeochemical processes. CDOM spectral slope, SCDOM, has been treated as a constant or semiconstant parameter in satellite retrievals of aCDOM despite significant regional and temporal variabilities. SCDOM and other optical metrics provide insights into CDOM composition, processing, food web dynamics, and carbon cycling. To date, much of this work relies on fluorescence techniques or aCDOM in spectral ranges unavailable to current and planned satellite sensors (e.g., global variability in SCDOM and fit deviations in the aCDOM spectra using the recently proposed Gaussian decomposition method. From this, we investigate if global variability in retrieved SCDOM and Gaussian components is significant and regionally distinct. We iteratively decreased the spectral range considered and analyzed the number, location, and magnitude of fitted Gaussian components to understand if a reduced spectral range impacts information obtained within a common spectral window. We compared the fitted slope from the Gaussian decomposition method to absorption-based indices that indicate CDOM composition to determine the ability of satellite-derived slope to inform the analysis and modeling of large-scale biogeochemical processes. Finally, we present implications of the observed variability for remote sensing of CDOM characteristics via SCDOM.

  16. Decree No. 67/77 of 6 May establishing a National Uranium Undertaking as a public body

    International Nuclear Information System (INIS)

    1977-01-01

    This Decree, promulgated on 29 March 1977, sets up a National Uranium Undertaking (ENU). The ENU Statute which is attached to the Decree lays down that its main purpose is to prospect for and inventory uranium deposits, to explore known deposits, to set up facilities for recovery and treatment of uranium ores, and finally, to market the products obtained. The ENU has taken over the work which, until now, had been carried out in that field by the Junta de Energia Nuclear and it is placed under the authority of the Minister of Industry and Technology. (NEA) [fr

  17. Hand eczema classification

    DEFF Research Database (Denmark)

    Diepgen, T L; Andersen, Klaus Ejner; Brandao, F M

    2008-01-01

    of the disease is rarely evidence based, and a classification system for different subdiagnoses of hand eczema is not agreed upon. Randomized controlled trials investigating the treatment of hand eczema are called for. For this, as well as for clinical purposes, a generally accepted classification system...... A classification system for hand eczema is proposed. Conclusions It is suggested that this classification be used in clinical work and in clinical trials....

  18. Classification of the web

    DEFF Research Database (Denmark)

    Mai, Jens Erik

    2004-01-01

    This paper discusses the challenges faced by investigations into the classification of the Web and outlines inquiries that are needed to use principles for bibliographic classification to construct classifications of the Web. This paper suggests that the classification of the Web meets challenges...... that call for inquiries into the theoretical foundation of bibliographic classification theory....

  19. Security classification of information

    Energy Technology Data Exchange (ETDEWEB)

    Quist, A.S.

    1993-04-01

    This document is the second of a planned four-volume work that comprehensively discusses the security classification of information. The main focus of Volume 2 is on the principles for classification of information. Included herein are descriptions of the two major types of information that governments classify for national security reasons (subjective and objective information), guidance to use when determining whether information under consideration for classification is controlled by the government (a necessary requirement for classification to be effective), information disclosure risks and benefits (the benefits and costs of classification), standards to use when balancing information disclosure risks and benefits, guidance for assigning classification levels (Top Secret, Secret, or Confidential) to classified information, guidance for determining how long information should be classified (classification duration), classification of associations of information, classification of compilations of information, and principles for declassifying and downgrading information. Rules or principles of certain areas of our legal system (e.g., trade secret law) are sometimes mentioned to .provide added support to some of those classification principles.

  20. Identification of spectral units on Phoebe

    Science.gov (United States)

    Coradini, A.; Tosi, F.; Gavrishin, A.I.; Capaccioni, F.; Cerroni, P.; Filacchione, G.; Adriani, A.; Brown, R.H.; Bellucci, G.; Formisano, V.; D'Aversa, E.; Lunine, J.I.; Baines, K.H.; Bibring, J.-P.; Buratti, B.J.; Clark, R.N.; Cruikshank, D.P.; Combes, M.; Drossart, P.; Jaumann, R.; Langevin, Y.; Matson, D.L.; McCord, T.B.; Mennella, V.; Nelson, R.M.; Nicholson, P.D.; Sicardy, B.; Sotin, Christophe; Hedman, M.M.; Hansen, G.B.; Hibbitts, C.A.; Showalter, M.; Griffith, C.; Strazzulla, G.

    2008-01-01

    We apply a multivariate statistical method to the Phoebe spectra collected by the VIMS experiment onboard the Cassini spacecraft during the flyby of June 2004. The G-mode clustering method, which permits identification of the most important features in a spectrum, is used on a small subset of data, characterized by medium and high spatial resolution, to perform a raw spectral classification of the surface of Phoebe. The combination of statistics and comparative analysis of the different areas using both the VIMS and ISS data is explored in order to highlight possible correlations with the surface geology. In general, the results by Clark et al. [Clark, R.N., Brown, R.H., Jaumann, R., Cruikshank, D.P., Nelson, R.M., Buratti, B.J., McCord, T.B., Lunine, J., Hoefen, T., Curchin, J.M., Hansen, G., Hibbitts, K., Matz, K.-D., Baines, K.H., Bellucci, G., Bibring, J.-P., Capaccioni, F., Cerroni, P., Coradini, A., Formisano, V., Langevin, Y., Matson, D.L., Mennella, V., Nicholson, P.D., Sicardy, B., Sotin, C., 2005. Nature 435, 66-69] are confirmed; but we also identify new signatures not reported before, such as the aliphatic CH stretch at 3.53 ??m and the ???4.4 ??m feature possibly related to cyanide compounds. On the basis of the band strengths computed for several absorption features and for the homogeneous spectral types isolated by the G-mode, a strong correlation of CO2 and aromatic hydrocarbons with exposed water ice, where the uniform layer covering Phoebe has been removed, is established. On the other hand, an anti-correlation of cyanide compounds with CO2 is suggested at a medium resolution scale. ?? 2007 Elsevier Inc. All rights reserved.

  1. Angular difference feature extraction for urban scene classification using ZY-3 multi-angle high-resolution satellite imagery

    Science.gov (United States)

    Huang, Xin; Chen, Huijun; Gong, Jianya

    2018-01-01

    Spaceborne multi-angle images with a high-resolution are capable of simultaneously providing spatial details and three-dimensional (3D) information to support detailed and accurate classification of complex urban scenes. In recent years, satellite-derived digital surface models (DSMs) have been increasingly utilized to provide height information to complement spectral properties for urban classification. However, in such a way, the multi-angle information is not effectively exploited, which is mainly due to the errors and difficulties of the multi-view image matching and the inaccuracy of the generated DSM over complex and dense urban scenes. Therefore, it is still a challenging task to effectively exploit the available angular information from high-resolution multi-angle images. In this paper, we investigate the potential for classifying urban scenes based on local angular properties characterized from high-resolution ZY-3 multi-view images. Specifically, three categories of angular difference features (ADFs) are proposed to describe the angular information at three levels (i.e., pixel, feature, and label levels): (1) ADF-pixel: the angular information is directly extrapolated by pixel comparison between the multi-angle images; (2) ADF-feature: the angular differences are described in the feature domains by comparing the differences between the multi-angle spatial features (e.g., morphological attribute profiles (APs)). (3) ADF-label: label-level angular features are proposed based on a group of urban primitives (e.g., buildings and shadows), in order to describe the specific angular information related to the types of primitive classes. In addition, we utilize spatial-contextual information to refine the multi-level ADF features using superpixel segmentation, for the purpose of alleviating the effects of salt-and-pepper noise and representing the main angular characteristics within a local area. The experiments on ZY-3 multi-angle images confirm that the proposed

  2. Classification of Ultra-High Resolution Orthophotos Combined with DSM Using a Dual Morphological Top Hat Profile

    Directory of Open Access Journals (Sweden)

    Qian Zhang

    2015-12-01

    Full Text Available New aerial sensors and platforms (e.g., unmanned aerial vehicles (UAVs are capable of providing ultra-high resolution remote sensing data (less than a 30-cm ground sampling distance (GSD. This type of data is an important source for interpreting sub-building level objects; however, it has not yet been explored. The large-scale differences of urban objects, the high spectral variability and the large perspective effect bring difficulties to the design of descriptive features. Therefore, features representing the spatial information of the objects are essential for dealing with the spectral ambiguity. In this paper, we proposed a dual morphology top-hat profile (DMTHP using both morphology reconstruction and erosion with different granularities. Due to the high dimensional feature space, we have proposed an adaptive scale selection procedure to reduce the feature dimension according to the training samples. The DMTHP is extracted from both images and Digital Surface Models (DSM to obtain complimentary information. The random forest classifier is used to classify the features hierarchically. Quantitative experimental results on aerial images with 9-cm and UAV images with 5-cm GSD are performed. Under our experiments, improvements of 10% and 2% in overall accuracy are obtained in comparison with the well-known differential morphological profile (DMP feature, and superior performance is observed over other tested features. Large format data with 20,000 × 20,000 pixels are used to perform a qualitative experiment using the proposed method, which shows its promising potential. The experiments also demonstrate that the DSM information has greatly enhanced the classification accuracy. In the best case in our experiment, it gives rise to a classification accuracy from 63.93% (spectral information only to 94.48% (the proposed method.

  3. [Object-oriented segmentation and classification of forest gap based on QuickBird remote sensing image.

    Science.gov (United States)

    Mao, Xue Gang; Du, Zi Han; Liu, Jia Qian; Chen, Shu Xin; Hou, Ji Yu

    2018-01-01

    Traditional field investigation and artificial interpretation could not satisfy the need of forest gaps extraction at regional scale. High spatial resolution remote sensing image provides the possibility for regional forest gaps extraction. In this study, we used object-oriented classification method to segment and classify forest gaps based on QuickBird high resolution optical remote sensing image in Jiangle National Forestry Farm of Fujian Province. In the process of object-oriented classification, 10 scales (10-100, with a step length of 10) were adopted to segment QuickBird remote sensing image; and the intersection area of reference object (RA or ) and intersection area of segmented object (RA os ) were adopted to evaluate the segmentation result at each scale. For segmentation result at each scale, 16 spectral characteristics and support vector machine classifier (SVM) were further used to classify forest gaps, non-forest gaps and others. The results showed that the optimal segmentation scale was 40 when RA or was equal to RA os . The accuracy difference between the maximum and minimum at different segmentation scales was 22%. At optimal scale, the overall classification accuracy was 88% (Kappa=0.82) based on SVM classifier. Combining high resolution remote sensing image data with object-oriented classification method could replace the traditional field investigation and artificial interpretation method to identify and classify forest gaps at regional scale.

  4. Undertaking cause-specific mortality measurement in an unregistered population: an example from Tigray Region, Ethiopia

    Directory of Open Access Journals (Sweden)

    Hagos Godefay

    2014-09-01

    Full Text Available Background: The lack of adequate documentation of deaths, and particularly their cause, is often noted in African and Asian settings, but practical solutions for addressing the problem are not always clear. Verbal autopsy methods (interviewing witnesses after a death have developed rapidly, but there remains a lack of clarity as to how these methods can be effectively applied to large unregistered populations. This paper sets out practical details for undertaking a representative survey of cause-specific mortality in a population of several million, taking Tigray Region in Ethiopia as a prototype. Sampling: Sampling was designed around an expected level of maternal mortality ratio of 400 per 100,000 live births, which needed measuring within a 95% confidence interval of approximately ±100. Taking a stratified cluster sample within the region at the district level for logistic reasons, and allowing for a design effect of 2, this required a population of around 900,000 people, equating to six typical districts. Since the region is administered in six geographic zones, one district per zone was randomly selected. Implementation: The survey was implemented as a two-stage process: first, to trace deaths that occurred in the sampled districts within the preceding year, and second to follow them up with verbal autopsy interviews. The field work for both stages was undertaken by health extension workers, working in their normally assigned areas. Most of the work was associated with tracing the deaths, rather than undertaking the verbal autopsy interviews. Discussion: This approach to measuring cause-specific mortality in an unregistered Ethiopian population proved to be feasible and effective. Although it falls short of the ideal situation of continuous civil registration and vital statistics, a survey-based strategy of this kind may prove to be a useful intermediate step on the road towards full civil registration and vital statistics implementation.

  5. [Study of near infrared spectral preprocessing and wavelength selection methods for endometrial cancer tissue].

    Science.gov (United States)

    Zhao, Li-Ting; Xiang, Yu-Hong; Dai, Yin-Mei; Zhang, Zhuo-Yong

    2010-04-01

    Near infrared spectroscopy was applied to measure the tissue slice of endometrial tissues for collecting the spectra. A total of 154 spectra were obtained from 154 samples. The number of normal, hyperplasia, and malignant samples was 36, 60, and 58, respectively. Original near infrared spectra are composed of many variables, for example, interference information including instrument errors and physical effects such as particle size and light scatter. In order to reduce these influences, original spectra data should be performed with different spectral preprocessing methods to compress variables and extract useful information. So the methods of spectral preprocessing and wavelength selection have played an important role in near infrared spectroscopy technique. In the present paper the raw spectra were processed using various preprocessing methods including first derivative, multiplication scatter correction, Savitzky-Golay first derivative algorithm, standard normal variate, smoothing, and moving-window median. Standard deviation was used to select the optimal spectral region of 4 000-6 000 cm(-1). Then principal component analysis was used for classification. Principal component analysis results showed that three types of samples could be discriminated completely and the accuracy almost achieved 100%. This study demonstrated that near infrared spectroscopy technology and chemometrics method could be a fast, efficient, and novel means to diagnose cancer. The proposed methods would be a promising and significant diagnosis technique of early stage cancer.

  6. Spectral and spectral-frequency methods of investigating atmosphereless bodies of the Solar system

    International Nuclear Information System (INIS)

    Busarev, Vladimir V; Prokof'eva-Mikhailovskaya, Valentina V; Bochkov, Valerii V

    2007-01-01

    A method of reflectance spectrophotometry of atmosphereless bodies of the Solar system, its specificity, and the means of eliminating basic spectral noise are considered. As a development, joining the method of reflectance spectrophotometry with the frequency analysis of observational data series is proposed. The combined spectral-frequency method allows identification of formations with distinctive spectral features, and estimations of their sizes and distribution on the surface of atmospherelss celestial bodies. As applied to investigations of asteroids 21 Lutetia and 4 Vesta, the spectral frequency method has given us the possibility of obtaining fundamentally new information about minor planets. (instruments and methods of investigation)

  7. Classification in medical images using adaptive metric k-NN

    Science.gov (United States)

    Chen, C.; Chernoff, K.; Karemore, G.; Lo, P.; Nielsen, M.; Lauze, F.

    2010-03-01

    The performance of the k-nearest neighborhoods (k-NN) classifier is highly dependent on the distance metric used to identify the k nearest neighbors of the query points. The standard Euclidean distance is commonly used in practice. This paper investigates the performance of k-NN classifier with respect to different adaptive metrics in the context of medical imaging. We propose using adaptive metrics such that the structure of the data is better described, introducing some unsupervised learning knowledge in k-NN. We investigated four different metrics are estimated: a theoretical metric based on the assumption that images are drawn from Brownian Image Model (BIM), the normalized metric based on variance of the data, the empirical metric is based on the empirical covariance matrix of the unlabeled data, and an optimized metric obtained by minimizing the classification error. The spectral structure of the empirical covariance also leads to Principal Component Analysis (PCA) performed on it which results the subspace metrics. The metrics are evaluated on two data sets: lateral X-rays of the lumbar aortic/spine region, where we use k-NN for performing abdominal aorta calcification detection; and mammograms, where we use k-NN for breast cancer risk assessment. The results show that appropriate choice of metric can improve classification.

  8. Multi-Temporal Land Cover Classification with Sequential Recurrent Encoders

    Science.gov (United States)

    Rußwurm, Marc; Körner, Marco

    2018-03-01

    Earth observation (EO) sensors deliver data with daily or weekly temporal resolution. Most land use and land cover (LULC) approaches, however, expect cloud-free and mono-temporal observations. The increasing temporal capabilities of today's sensors enables the use of temporal, along with spectral and spatial features. Domains, such as speech recognition or neural machine translation, work with inherently temporal data and, today, achieve impressive results using sequential encoder-decoder structures. Inspired by these sequence-to-sequence models, we adapt an encoder structure with convolutional recurrent layers in order to approximate a phenological model for vegetation classes based on a temporal sequence of Sentinel 2 (S2) images. In our experiments, we visualize internal activations over a sequence of cloudy and non-cloudy images and find several recurrent cells, which reduce the input activity for cloudy observations. Hence, we assume that our network has learned cloud-filtering schemes solely from input data, which could alleviate the need for tedious cloud-filtering as a preprocessing step for many EO approaches. Moreover, using unfiltered temporal series of top-of-atmosphere (TOA) reflectance data, we achieved in our experiments state-of-the-art classification accuracies on a large number of crop classes with minimal preprocessing compared to other classification approaches.

  9. Hazard classification methodology

    International Nuclear Information System (INIS)

    Brereton, S.J.

    1996-01-01

    This document outlines the hazard classification methodology used to determine the hazard classification of the NIF LTAB, OAB, and the support facilities on the basis of radionuclides and chemicals. The hazard classification determines the safety analysis requirements for a facility

  10. Classification of Parameters Extracted from Cardiotocographic Signals for Early Detection of Metabolic Acidemia in Newborns

    Directory of Open Access Journals (Sweden)

    ROTARIU, C.

    2015-08-01

    Full Text Available Fetal acidosis is reflected by the values of umbilical cord pH and base deficit (BDecf: normal recordings (pH over 7.2 and BDecf under 8 mmol/l and abnormal recordings (pH under 7.2 and BDecf over 8 mmol/l. The purpose of this paper is to present the implementation of an automated system for detecting fetal acidosis in cardiotocographic recordings. The method uses spectral analysis of medium (0.07-0.13 Hz and high (0.13-1 Hz frequency spectrum. We implement the algorithm for segments of the recordings without signal loss for better classification. We determined the normalized medium and high frequency components and mid to high frequency ratio. The recordings in the database are divided into a control group (100 normal recordings and a test group (431 normal or abnormal recordings. A t-test with the p value under 0.05 between the two groups is used to classify the test group. The classification is improved by including the presence of late and prolonged decelerations in the classification process, obtaining the final results, which are comparable to the best ones in current literature.

  11. Comparative study of wine tannin classification using Fourier transform mid-infrared spectrometry and sensory analysis.

    Science.gov (United States)

    Fernández, Katherina; Labarca, Ximena; Bordeu, Edmundo; Guesalaga, Andrés; Agosin, Eduardo

    2007-11-01

    Wine tannins are fundamental to the determination of wine quality. However, the chemical and sensorial analysis of these compounds is not straightforward and a simple and rapid technique is necessary. We analyzed the mid-infrared spectra of white, red, and model wines spiked with known amounts of skin or seed tannins, collected using Fourier transform mid-infrared (FT-MIR) transmission spectroscopy (400-4000 cm(-1)). The spectral data were classified according to their tannin source, skin or seed, and tannin concentration by means of discriminant analysis (DA) and soft independent modeling of class analogy (SIMCA) to obtain a probabilistic classification. Wines were also classified sensorially by a trained panel and compared with FT-MIR. SIMCA models gave the most accurate classification (over 97%) and prediction (over 60%) among the wine samples. The prediction was increased (over 73%) using the leave-one-out cross-validation technique. Sensory classification of the wines was less accurate than that obtained with FT-MIR and SIMCA. Overall, these results show the potential of FT-MIR spectroscopy, in combination with adequate statistical tools, to discriminate wines with different tannin levels.

  12. Laser Raman detection for oral cancer based on an adaptive Gaussian process classification method with posterior probabilities

    International Nuclear Information System (INIS)

    Du, Zhanwei; Yang, Yongjian; Bai, Yuan; Wang, Lijun; Su, Le; Chen, Yong; Li, Xianchang; Zhou, Xiaodong; Shen, Aiguo; Hu, Jiming; Jia, Jun

    2013-01-01

    The existing methods for early and differential diagnosis of oral cancer are limited due to the unapparent early symptoms and the imperfect imaging examination methods. In this paper, the classification models of oral adenocarcinoma, carcinoma tissues and a control group with just four features are established by utilizing the hybrid Gaussian process (HGP) classification algorithm, with the introduction of the mechanisms of noise reduction and posterior probability. HGP shows much better performance in the experimental results. During the experimental process, oral tissues were divided into three groups, adenocarcinoma (n = 87), carcinoma (n = 100) and the control group (n = 134). The spectral data for these groups were collected. The prospective application of the proposed HGP classification method improved the diagnostic sensitivity to 56.35% and the specificity to about 70.00%, and resulted in a Matthews correlation coefficient (MCC) of 0.36. It is proved that the utilization of HGP in LRS detection analysis for the diagnosis of oral cancer gives accurate results. The prospect of application is also satisfactory. (paper)

  13. Laser Raman detection for oral cancer based on an adaptive Gaussian process classification method with posterior probabilities

    Science.gov (United States)

    Du, Zhanwei; Yang, Yongjian; Bai, Yuan; Wang, Lijun; Su, Le; Chen, Yong; Li, Xianchang; Zhou, Xiaodong; Jia, Jun; Shen, Aiguo; Hu, Jiming

    2013-03-01

    The existing methods for early and differential diagnosis of oral cancer are limited due to the unapparent early symptoms and the imperfect imaging examination methods. In this paper, the classification models of oral adenocarcinoma, carcinoma tissues and a control group with just four features are established by utilizing the hybrid Gaussian process (HGP) classification algorithm, with the introduction of the mechanisms of noise reduction and posterior probability. HGP shows much better performance in the experimental results. During the experimental process, oral tissues were divided into three groups, adenocarcinoma (n = 87), carcinoma (n = 100) and the control group (n = 134). The spectral data for these groups were collected. The prospective application of the proposed HGP classification method improved the diagnostic sensitivity to 56.35% and the specificity to about 70.00%, and resulted in a Matthews correlation coefficient (MCC) of 0.36. It is proved that the utilization of HGP in LRS detection analysis for the diagnosis of oral cancer gives accurate results. The prospect of application is also satisfactory.

  14. Classification of hyperspectral imagery using MapReduce on a NVIDIA graphics processing unit (Conference Presentation)

    Science.gov (United States)

    Ramirez, Andres; Rahnemoonfar, Maryam

    2017-04-01

    A hyperspectral image provides multidimensional figure rich in data consisting of hundreds of spectral dimensions. Analyzing the spectral and spatial information of such image with linear and non-linear algorithms will result in high computational time. In order to overcome this problem, this research presents a system using a MapReduce-Graphics Processing Unit (GPU) model that can help analyzing a hyperspectral image through the usage of parallel hardware and a parallel programming model, which will be simpler to handle compared to other low-level parallel programming models. Additionally, Hadoop was used as an open-source version of the MapReduce parallel programming model. This research compared classification accuracy results and timing results between the Hadoop and GPU system and tested it against the following test cases: the CPU and GPU test case, a CPU test case and a test case where no dimensional reduction was applied.

  15. From the Icy Satellites to Small Moons and Rings: Spectral Indicators by Cassini-VIMS Unveil Compositional Trends in the Saturnian System

    Science.gov (United States)

    Filacchione, G.; Capaccioni, F.; Ciarniello, M.; Nicholson, P. D.; Clark, R. N.; Cuzzi, J. N.; Buratti, B. B.; Cruikshank, D. P.; Brown, R. H.

    2017-01-01

    Despite water ice being the most abundant species on Saturn satellites' surfaces and ring particles, remarkable spectral differences in the 0.35-5.0 μm range are observed among these objects. Here we report about the results of a comprehensive analysis of more than 3000 disk-integrated observations of regular satellites and small moons acquired by VIMS aboard Cassini mission between 2004 and 2016. These observations, taken from very different illumination and viewing geometries, allow us to classify satellites' and rings' compositions by means of spectral indicators, e.g. 350-550 nm - 550-950 nm spectral slopes and water ice band parameters [1,2,3]. Spectral classification is further supported by indirect retrieval of temperature by means of the 3.6 μm I/F peak wavelength [4,5]. The comparison with syntethic spectra modeled by means of Hapke's theory point to different compositional classes where water ice, amorphous carbon, tholins and CO2 ice in different quantities and mixing modalities are the principal endmembers [3, 6]. When compared to satellites, rings appear much more red at visible wavelengths and show more intense 1.5-2.0 μm band depths [7]. Our analysis shows that spectral classes are detected among the principal satellites with Enceladus and Tethys the ones with stronger water ice band depths and more neutral spectral slopes while Rhea evidences less intense band depths and more red visible spectra. Even more intense reddening in the 0.55-0.95 μm range is observed on Iapetus leading hemisphere [8] and on Hyperion [9]. With an intermediate reddening, the minor moons seems to be the spectral link between the principal satellites and main rings [10]: Prometheus and Pandora appear similar to Cassini Division ring particles. Epimetheus shows more intense water ice bands than Janus. Epimetheus' visible colors are similar to water ice rich moons while Janus is more similar to C ring particles. Finally, Dione and Tethys lagrangian satellites show a very

  16. Index finger motor imagery EEG pattern recognition in BCI applications using dictionary cleaned sparse representation-based classification for healthy people

    Science.gov (United States)

    Miao, Minmin; Zeng, Hong; Wang, Aimin; Zhao, Fengkui; Liu, Feixiang

    2017-09-01

    Electroencephalogram (EEG)-based motor imagery (MI) brain-computer interface (BCI) has shown its effectiveness for the control of rehabilitation devices designed for large body parts of the patients with neurologic impairments. In order to validate the feasibility of using EEG to decode the MI of a single index finger and constructing a BCI-enhanced finger rehabilitation system, we collected EEG data during right hand index finger MI and rest state for five healthy subjects and proposed a pattern recognition approach for classifying these two mental states. First, Fisher's linear discriminant criteria and power spectral density analysis were used to analyze the event-related desynchronization patterns. Second, both band power and approximate entropy were extracted as features. Third, aiming to eliminate the abnormal samples in the dictionary and improve the classification performance of the conventional sparse representation-based classification (SRC) method, we proposed a novel dictionary cleaned sparse representation-based classification (DCSRC) method for final classification. The experimental results show that the proposed DCSRC method gives better classification accuracies than SRC and an average classification accuracy of 81.32% is obtained for five subjects. Thus, it is demonstrated that single right hand index finger MI can be decoded from the sensorimotor rhythms, and the feature patterns of index finger MI and rest state can be well recognized for robotic exoskeleton initiation.

  17. Estimation of spectral kurtosis

    Science.gov (United States)

    Sutawanir

    2017-03-01

    Rolling bearings are the most important elements in rotating machinery. Bearing frequently fall out of service for various reasons: heavy loads, unsuitable lubrications, ineffective sealing. Bearing faults may cause a decrease in performance. Analysis of bearing vibration signals has attracted attention in the field of monitoring and fault diagnosis. Bearing vibration signals give rich information for early detection of bearing failures. Spectral kurtosis, SK, is a parameter in frequency domain indicating how the impulsiveness of a signal varies with frequency. Faults in rolling bearings give rise to a series of short impulse responses as the rolling elements strike faults, SK potentially useful for determining frequency bands dominated by bearing fault signals. SK can provide a measure of the distance of the analyzed bearings from a healthy one. SK provides additional information given by the power spectral density (psd). This paper aims to explore the estimation of spectral kurtosis using short time Fourier transform known as spectrogram. The estimation of SK is similar to the estimation of psd. The estimation falls in model-free estimation and plug-in estimator. Some numerical studies using simulations are discussed to support the methodology. Spectral kurtosis of some stationary signals are analytically obtained and used in simulation study. Kurtosis of time domain has been a popular tool for detecting non-normality. Spectral kurtosis is an extension of kurtosis in frequency domain. The relationship between time domain and frequency domain analysis is establish through power spectrum-autocovariance Fourier transform. Fourier transform is the main tool for estimation in frequency domain. The power spectral density is estimated through periodogram. In this paper, the short time Fourier transform of the spectral kurtosis is reviewed, a bearing fault (inner ring and outer ring) is simulated. The bearing response, power spectrum, and spectral kurtosis are plotted to

  18. A Comparative Observational Study of YSO Classification in Four Small Star-forming H ii Regions

    Energy Technology Data Exchange (ETDEWEB)

    Kang, Sung-Ju; Choi, Minho; Kang, Miju [Korea Astronomy and Space Science Institute, 776, Daedeokdae-ro, Yuseong-gu, Daejeon, 34055 (Korea, Republic of); Kerton, C. R., E-mail: sjkang@kasi.re.kr, E-mail: kerton@iastate.edu [Department of Physics and Astronomy, Iowa State University, Ames, IA 50011 (United States)

    2017-08-10

    We have developed a new young stellar object (YSO) identification and classification technique using mid-infrared Wide-field Infrared Survey Explorer (WISE) data. We compare this new technique with previous WISE YSO detection and classification methods that used either infrared colors or spectral energy distribution slopes. In this study, we also use the new technique to detect and examine the YSO population associated with four small H ii regions: KR 7, KR 81, KR 120, and KR 140. The relatively simple structure of these regions allows us to effectively use both spatial and temporal constraints to identify YSOs that are potential products of triggered star formation. We are also able to identify regions of active star formation around these H ii regions that are clearly not influenced by the H ii region expansion, and thus demonstrate that star formation is on-going on megayear timescales in some of these molecular clouds.

  19. Spectral stratigraphy

    Science.gov (United States)

    Lang, Harold R.

    1991-01-01

    A new approach to stratigraphic analysis is described which uses photogeologic and spectral interpretation of multispectral remote sensing data combined with topographic information to determine the attitude, thickness, and lithology of strata exposed at the surface. The new stratigraphic procedure is illustrated by examples in the literature. The published results demonstrate the potential of spectral stratigraphy for mapping strata, determining dip and strike, measuring and correlating stratigraphic sequences, defining lithofacies, mapping biofacies, and interpreting geological structures.

  20. Optimisation of material discrimination using spectral CT

    International Nuclear Information System (INIS)

    Nik, S.J.; Meyer, J.; Watts, R.

    2010-01-01

    Full text: Spectral computed tomography (CT) using novel X-ray photon counting detectors (PCDs) with energy resolving capabilities is capable of providing energy-selective images. This extra energy information may allow materials such as iodine and calcium, or water and fat to be distinguished. PCDs have energy thresholds, enabling the classification of photons into multiple energy bins. The inform tion content of spectral CT images depends on how the photons are grouped together. [n this work, a method is presented to optimise energy windows for maximum material discrimination. Given a combination of thicknesses, the reference number of expected photons in each energy bin is computed using the Bee Lambert equation. A similar calculation is performed for an exhaustive range of thicknesses and the number of photons in each case is com pared to the reference, allowing a statistical map of the uncertainty in thickness parameters to be constructed. The 63%-confidence region in the two-dimensional thickness space is a representation of how optimal the bins are for material separation. The model is demonstrated with 0.1 mm of iodine and 2.2 mm of calcium using two adjacent bins encompassing the entire energy range. Bins bordering at the iodine k-edge of 33.2 keY are found to be optimal. When compared to two abutted energy bins with equal incident counts as used in the literature (bordering at 54 keY), the thickness uncertainties are reduced from approximately 4% to less than I % (see Figure). This approach has been developed for two materials and is expandable to an arbitrary number of materials and bins.