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Sample records for hyperspectral information identification

  1. Overhead longwave infrared hyperspectral material identification using radiometric models

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    Zelinski, M. E. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2018-01-09

    Material detection algorithms used in hyperspectral data processing are computationally efficient but can produce relatively high numbers of false positives. Material identification performed as a secondary processing step on detected pixels can help separate true and false positives. This paper presents a material identification processing chain for longwave infrared hyperspectral data of solid materials collected from airborne platforms. The algorithms utilize unwhitened radiance data and an iterative algorithm that determines the temperature, humidity, and ozone of the atmospheric profile. Pixel unmixing is done using constrained linear regression and Bayesian Information Criteria for model selection. The resulting product includes an optimal atmospheric profile and full radiance material model that includes material temperature, abundance values, and several fit statistics. A logistic regression method utilizing all model parameters to improve identification is also presented. This paper details the processing chain and provides justification for the algorithms used. Several examples are provided using modeled data at different noise levels.

  2. Performance metrics for the evaluation of hyperspectral chemical identification systems

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    Truslow, Eric; Golowich, Steven; Manolakis, Dimitris; Ingle, Vinay

    2016-02-01

    Remote sensing of chemical vapor plumes is a difficult but important task for many military and civilian applications. Hyperspectral sensors operating in the long-wave infrared regime have well-demonstrated detection capabilities. However, the identification of a plume's chemical constituents, based on a chemical library, is a multiple hypothesis testing problem which standard detection metrics do not fully describe. We propose using an additional performance metric for identification based on the so-called Dice index. Our approach partitions and weights a confusion matrix to develop both the standard detection metrics and identification metric. Using the proposed metrics, we demonstrate that the intuitive system design of a detector bank followed by an identifier is indeed justified when incorporating performance information beyond the standard detection metrics.

  3. SVM Classifiers: The Objects Identification on the Base of Their Hyperspectral Features

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    Demidova Liliya

    2017-01-01

    Full Text Available The problem of the objects identification on the base of their hyperspectral features has been considered. It is offered to use the SVM classifiers on the base of the modified PSO algorithm, adapted to specifics of the problem of the objects identification on the base of their hyperspectral features. The results of the objects identification on the base of their hyperspectral features with using of the SVM classifiers have been presented.

  4. INFORMATION EXTRACTION IN TOMB PIT USING HYPERSPECTRAL DATA

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    X. Yang

    2018-04-01

    Full Text Available Hyperspectral data has characteristics of multiple bands and continuous, large amount of data, redundancy, and non-destructive. These characteristics make it possible to use hyperspectral data to study cultural relics. In this paper, the hyperspectral imaging technology is adopted to recognize the bottom images of an ancient tomb located in Shanxi province. There are many black remains on the bottom surface of the tomb, which are suspected to be some meaningful texts or paintings. Firstly, the hyperspectral data is preprocessing to get the reflectance of the region of interesting. For the convenient of compute and storage, the original reflectance value is multiplied by 10000. Secondly, this article uses three methods to extract the symbols at the bottom of the ancient tomb. Finally we tried to use morphology to connect the symbols and gave fifteen reference images. The results show that the extraction of information based on hyperspectral data can obtain a better visual experience, which is beneficial to the study of ancient tombs by researchers, and provides some references for archaeological research findings.

  5. Information Extraction in Tomb Pit Using Hyperspectral Data

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    Yang, X.; Hou, M.; Lyu, S.; Ma, S.; Gao, Z.; Bai, S.; Gu, M.; Liu, Y.

    2018-04-01

    Hyperspectral data has characteristics of multiple bands and continuous, large amount of data, redundancy, and non-destructive. These characteristics make it possible to use hyperspectral data to study cultural relics. In this paper, the hyperspectral imaging technology is adopted to recognize the bottom images of an ancient tomb located in Shanxi province. There are many black remains on the bottom surface of the tomb, which are suspected to be some meaningful texts or paintings. Firstly, the hyperspectral data is preprocessing to get the reflectance of the region of interesting. For the convenient of compute and storage, the original reflectance value is multiplied by 10000. Secondly, this article uses three methods to extract the symbols at the bottom of the ancient tomb. Finally we tried to use morphology to connect the symbols and gave fifteen reference images. The results show that the extraction of information based on hyperspectral data can obtain a better visual experience, which is beneficial to the study of ancient tombs by researchers, and provides some references for archaeological research findings.

  6. CLASSIFICATION AND RECOGNITION OF TOMB INFORMATION IN HYPERSPECTRAL IMAGE

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    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.

  7. Classification and Recognition of Tomb Information in Hyperspectral Image

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    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.

  8. Camouflage target detection via hyperspectral imaging plus information divergence measurement

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    Chen, Yuheng; Chen, Xinhua; Zhou, Jiankang; Ji, Yiqun; Shen, Weimin

    2016-01-01

    Target detection is one of most important applications in remote sensing. Nowadays accurate camouflage target distinction is often resorted to spectral imaging technique due to its high-resolution spectral/spatial information acquisition ability as well as plenty of data processing methods. In this paper, hyper-spectral imaging technique together with spectral information divergence measure method is used to solve camouflage target detection problem. A self-developed visual-band hyper-spectral imaging device is adopted to collect data cubes of certain experimental scene before spectral information divergences are worked out so as to discriminate target camouflage and anomaly. Full-band information divergences are measured to evaluate target detection effect visually and quantitatively. Information divergence measurement is proved to be a low-cost and effective tool for target detection task and can be further developed to other target detection applications beyond spectral imaging technique.

  9. Fusion of LBP and SWLD using spatio-spectral information for hyperspectral face recognition

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    Xie, Zhihua; Jiang, Peng; Zhang, Shuai; Xiong, Jinquan

    2018-01-01

    Hyperspectral imaging, recording intrinsic spectral information of the skin cross different spectral bands, become an important issue for robust face recognition. However, the main challenges for hyperspectral face recognition are high data dimensionality, low signal to noise ratio and inter band misalignment. In this paper, hyperspectral face recognition based on LBP (Local binary pattern) and SWLD (Simplified Weber local descriptor) is proposed to extract discriminative local features from spatio-spectral fusion information. Firstly, the spatio-spectral fusion strategy based on statistical information is used to attain discriminative features of hyperspectral face images. Secondly, LBP is applied to extract the orientation of the fusion face edges. Thirdly, SWLD is proposed to encode the intensity information in hyperspectral images. Finally, we adopt a symmetric Kullback-Leibler distance to compute the encoded face images. The hyperspectral face recognition is tested on Hong Kong Polytechnic University Hyperspectral Face database (PolyUHSFD). Experimental results show that the proposed method has higher recognition rate (92.8%) than the state of the art hyperspectral face recognition algorithms.

  10. Hyperspectral Based Skin Detection for Person of Interest Identification

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    2015-03-01

    short-wave infrared VIS visible spectrum PCA principal component analysis FCBF Fast Correlation- Based Filter POI person of interest ANN artificial neural...an artificial neural network (ANN) that is created in MATLAB® using the Neural Network Toolbox to identify a POI based on their skin spectral data. A...identifying a POI based on skin spectral data. She identified an optimal feature subset to be used with the hyperspectral data she collected using a

  11. Identification of early cancerous lesion of esophagus with endoscopic images by hyperspectral image technique (Conference Presentation)

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    Huang, Shih-Wei; Chen, Shih-Hua; Chen, Weichung; Wu, I.-Chen; Wu, Ming Tsang; Kuo, Chie-Tong; Wang, Hsiang-Chen

    2016-03-01

    This study presents a method to identify early esophageal cancer within endoscope using hyperspectral imaging technology. The research samples are three kinds of endoscopic images including white light endoscopic, chromoendoscopic, and narrow-band endoscopic images with different stages of pathological changes (normal, dysplasia, dysplasia - esophageal cancer, and esophageal cancer). Research is divided into two parts: first, we analysis the reflectance spectra of endoscopic images with different stages to know the spectral responses by pathological changes. Second, we identified early cancerous lesion of esophagus by principal component analysis (PCA) of the reflectance spectra of endoscopic images. The results of this study show that the identification of early cancerous lesion is possible achieve from three kinds of images. In which the spectral characteristics of NBI endoscopy images of a gray area than those without the existence of the problem the first two, and the trend is very clear. Therefore, if simply to reflect differences in the degree of spectral identification, chromoendoscopic images are suitable samples. The best identification of early esophageal cancer is using the NBI endoscopic images. Based on the results, the use of hyperspectral imaging technology in the early endoscopic esophageal cancer lesion image recognition helps clinicians quickly diagnose. We hope for the future to have a relatively large amount of endoscopic image by establishing a hyperspectral imaging database system developed in this study, so the clinician can take this repository more efficiently preliminary diagnosis.

  12. Sensitive Wavelengths Selection in Identification of Ophiopogon japonicus Based on Near-Infrared Hyperspectral Imaging Technology

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    Zhengyan Xia

    2017-01-01

    Full Text Available Hyperspectral imaging (HSI technology has increasingly been applied as an analytical tool in fields of agricultural, food, and Traditional Chinese Medicine over the past few years. The HSI spectrum of a sample is typically achieved by a spectroradiometer at hundreds of wavelengths. In recent years, considerable effort has been made towards identifying wavelengths (variables that contribute useful information. Wavelengths selection is a critical step in data analysis for Raman, NIRS, or HSI spectroscopy. In this study, the performances of 10 different wavelength selection methods for the discrimination of Ophiopogon japonicus of different origin were compared. The wavelength selection algorithms tested include successive projections algorithm (SPA, loading weights (LW, regression coefficients (RC, uninformative variable elimination (UVE, UVE-SPA, competitive adaptive reweighted sampling (CARS, interval partial least squares regression (iPLS, backward iPLS (BiPLS, forward iPLS (FiPLS, and genetic algorithms (GA-PLS. One linear technique (partial least squares-discriminant analysis was established for the evaluation of identification. And a nonlinear calibration model, support vector machine (SVM, was also provided for comparison. The results indicate that wavelengths selection methods are tools to identify more concise and effective spectral data and play important roles in the multivariate analysis, which can be used for subsequent modeling analysis.

  13. Progressively expanded neural network for automatic material identification in hyperspectral imagery

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    Paheding, Sidike

    The science of hyperspectral remote sensing focuses on the exploitation of the spectral signatures of various materials to enhance capabilities including object detection, recognition, and material characterization. Hyperspectral imagery (HSI) has been extensively used for object detection and identification applications since it provides plenty of spectral information to uniquely identify materials by their reflectance spectra. HSI-based object detection algorithms can be generally classified into stochastic and deterministic approaches. Deterministic approaches are comparatively simple to apply since it is usually based on direct spectral similarity such as spectral angles or spectral correlation. In contrast, stochastic algorithms require statistical modeling and estimation for target class and non-target class. Over the decades, many single class object detection methods have been proposed in the literature, however, deterministic multiclass object detection in HSI has not been explored. In this work, we propose a deterministic multiclass object detection scheme, named class-associative spectral fringe-adjusted joint transform correlation. Human brain is capable of simultaneously processing high volumes of multi-modal data received every second of the day. In contrast, a machine sees input data simply as random binary numbers. Although machines are computationally efficient, they are inferior when comes to data abstraction and interpretation. Thus, mimicking the learning strength of human brain has been current trend in artificial intelligence. In this work, we present a biological inspired neural network, named progressively expanded neural network (PEN Net), based on nonlinear transformation of input neurons to a feature space for better pattern differentiation. In PEN Net, discrete fixed excitations are disassembled and scattered in the feature space as a nonlinear line. Each disassembled element on the line corresponds to a pattern with similar features

  14. Research on marine and freshwater fish identification model based on hyper-spectral imaging technology

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    Fu, Yan; Guo, Pei-yuan; Xiang, Ling-zi; Bao, Man; Chen, Xing-hai

    2013-08-01

    With the gradually mature of hyper spectral image technology, the application of the meat nondestructive detection and recognition has become one of the current research focuses. This paper for the study of marine and freshwater fish by the pre-processing and feature extraction of the collected spectral curve data, combined with BP network structure and LVQ network structure, a predictive model of hyper spectral image data of marine and freshwater fish has been initially established and finally realized the qualitative analysis and identification of marine and freshwater fish quality. The results of this study show that hyper spectral imaging technology combined with the BP and LVQ Artificial Neural Network Model can be used for the identification of marine and freshwater fish detection. Hyper-spectral data acquisition can be carried out without any pretreatment of the samples, thus hyper-spectral imaging technique is the lossless, high- accuracy and rapid detection method for quality of fish. In this study, only 30 samples are used for the exploratory qualitative identification of research, although the ideal study results are achieved, we will further increase the sample capacity to take the analysis of quantitative identification and verify the feasibility of this theory.

  15. Assessment of spatial information for hyperspectral imaging of lesion

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    Yang, Xue; Li, Gang; Lin, Ling

    2016-10-01

    Multiple diseases such as breast tumor poses a great threat to women's health and life, while the traditional detection method is complex, costly and unsuitable for frequently self-examination, therefore, an inexpensive, convenient and efficient method for tumor self-inspection is needed urgently, and lesion localization is an important step. This paper proposes an self-examination method for positioning of a lesion. The method adopts transillumination to acquire the hyperspectral images and to assess the spatial information of lesion. Firstly, multi-wavelength sources are modulated with frequency division, which is advantageous to separate images of different wavelength, meanwhile, the source serves as fill light to each other to improve the sensitivity in the low-lightlevel imaging. Secondly, the signal-to-noise ratio of transmitted images after demodulation are improved by frame accumulation technology. Next, gray distributions of transmitted images are analyzed. The gray-level differences is constituted by the actual transmitted images and fitting transmitted images of tissue without lesion, which is to rule out individual differences. Due to scattering effect, there will be transition zones between tissue and lesion, and the zone changes with wavelength change, which will help to identify the structure details of lesion. Finally, image segmentation is adopted to extract the lesion and the transition zones, and the spatial features of lesion are confirmed according to the transition zones and the differences of transmitted light intensity distributions. Experiment using flat-shaped tissue as an example shows that the proposed method can extract the space information of lesion.

  16. Growth Identification of Aspergillus flavus and Aspergillus parasiticus by Visible/Near-Infrared Hyperspectral Imaging

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    Xuan Chu

    2018-03-01

    Full Text Available Visible/near-infrared (Vis/NIR hyperspectral imaging (400–1000 nm was applied to identify the growth process of Aspergillus flavus and Aspergillus parasiticus. The hyperspectral images of the two fungi that were growing on rose bengal medium were recorded daily for 6 days. A band ratio using two bands at 446 nm and 460 nm separated A. flavus and A. parasiticus on day 1 from other days. Image at band of 520 nm classified A. parasiticus on day 6. Principle component analysis (PCA was performed on the cleaned hyperspectral images. The score plot of the second to sixth principal components (PC2 to PC6 gave a rough clustering of fungi in the same incubation time. However, in the plot, A. flavus on day 3 and day 4 and A. parasiticus on day 2 and day 3 overlapped. The average spectra of each fungus in each growth day were extracted, then PCA and support vector machine (SVM classifier were applied to the full spectral range. SVM models built by PC2 to PC6 could identify fungal growth days with accuracies of 92.59% and 100% for A. flavus and A. parasiticus individually. In order to simplify the prediction models, competitive adaptive reweighted sampling (CARS was employed to choose optimal wavelengths. As a result, nine (402, 442, 487, 502, 524, 553, 646, 671, 760 nm and seven (461, 538, 542, 742, 753, 756, 919 nm wavelengths were selected for A. flavus and A. parasiticus, respectively. New optimal wavelengths SVM models were built, and the identification accuracies were 83.33% and 98.15% for A. flavus and A. parasiticus, respectively. Finally, the visualized prediction images for A. flavus and A. parasiticus in different growth days were made by applying the optimal wavelength’s SVM models on every pixel of the hyperspectral image.

  17. Nontronite mineral identification in nilgiri hills of tamil nadu using hyperspectral remote sensing

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    Vigneshkumar, M.; Yarakkula, Kiran

    2017-11-01

    Hyperspectral Remote sensing is a tool to identify the minerals along with field investigation. Tamil Nadu has abundant minerals like 30% titanium, 52% molybdenum, 59% garnet, 69% dunite, 75% vermiculite and 81% lignite. To enhance the user and industry requirements, mineral extraction is required. To identify the minerals properly, sophisticated tools are required. Hyperspectral remote sensing provides continuous extraction of earth surface information in an accurate manner. Nontronite is an iron-rich mineral mainly available in Nilgiri hills, Tamil Nadu, India. Due to the large number of bands, hyperspectral data require various preprocessing steps such as bad bands removal, destriping, radiance conversion and atmospheric correction. The atmospheric correction is performed using FLAASH method. The spectral data reduction is carried out with minimum noise fraction (MNF) method. The spatial information is reduced using pixel purity index (PPI) with 10000 iterations. The selected end members are compared with spectral libraries like USGS, JPL, and JHU. In the Nontronite mineral gives the probability of 0.85. Finally the classification is accomplished using spectral angle mapper (SAM) method.

  18. Hyperspectral imager for components identification in the atmosphere

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    Dewandel, Jean-Luc; Beghuin, Didier; Dubois, Xavier; Antoine, Philippe

    2017-11-01

    Several applications require the identification of chemical elements during re-entry of material in the atmosphere. The materials can be from human origin or meteorites. The Automated Transfer Vehicle (ATV) re-entry has been filmed with conventional camera from airborne manual operation. In order to permit the identification of the separate elements from their glow, spectral analysis needs to be added to the video data. In a LET-SME contract with ESA, Lambda-X has built a Fourier Transform Imaging Spectrometer to permit, in a future work, to bring the technology to the readiness level required for the application. In this paper, the principles of the Fourier Transform Imaging spectroscopy are recalled, the different interferometers suitable for supporting the technique are reviewed and the selection process is explained. The final selection of the interferometer corresponds to a birefringent prism based common path shear interferometer. The design of the breadboard and its performances are presented in terms of spatial resolution, aperture, and spectral resolution. A discussion is open regarding perspective of the technique for other remote sensing applications compared to more usual push broom configurations.

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

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    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.

  20. Identification of invasive and expansive plant species based on airborne hyperspectral and ALS data

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    Szporak-Wasilewska, Sylwia; Kuc, Gabriela; Jóźwiak, Jacek; Demarchi, Luca; Chormański, Jarosław; Marcinkowska-Ochtyra, Adriana; Ochtyra, Adrian; Jarocińska, Anna; Sabat, Anita; Zagajewski, Bogdan; Tokarska-Guzik, Barbara; Bzdęga, Katarzyna; Pasierbiński, Andrzej; Fojcik, Barbara; Jędrzejczyk-Korycińska, Monika; Kopeć, Dominik; Wylazłowska, Justyna; Woziwoda, Beata; Michalska-Hejduk, Dorota; Halladin-Dąbrowska, Anna

    2017-04-01

    The aim of Natura 2000 network is to ensure the long term survival of most valuable and threatened species and habitats in Europe. The encroachment of invasive alien and expansive native plant species is among the most essential threat that can cause significant damage to protected habitats and their biodiversity. The phenomenon requires comprehensive and efficient repeatable solutions that can be applied to various areas in order to assess the impact on habitats. The aim of this study is to investigate of the issue of invasive and expansive plant species as they affect protected areas at a larger scale of Natura 2000 network in Poland. In order to determine the scale of the problem we have been developing methods of identification of invasive and expansive species and then detecting their occurrence and mapping their distribution in selected protected areas within Natura 2000 network using airborne hyperspectral and airborne laser scanning data. The aerial platform used consists of hyperspectral HySpex scanner (451 bands in VNIR and SWIR), Airborne Laser Scanner (FWF) Riegl Lite Mapper and RGB camera. It allowed to obtain simultaneous 1 meter resolution hyperspectral image, 0.1 m resolution orthophotomaps and point cloud data acquired with 7 points/m2. Airborne images were acquired three times per year during growing season to account for plant seasonal change (in May/June, July/August and September/October 2016). The hyperspectral images were radiometrically, geometrically and atmospherically corrected. Atmospheric correction was performed and validated using ASD FieldSpec 4 measurements. ALS point cloud data were used to generate several different topographic, vegetation and intensity products with 1 m spatial resolution. Acquired data (both hyperspectral and ALS) were used to test different classification methods including Mixture Tuned Matched Filtering (MTMF), Spectral Angle Mapper (SAM), Random Forest (RF), Support Vector Machines (SVM), among others

  1. Hyperspectral image processing methods

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    Hyperspectral image processing refers to the use of computer algorithms to extract, store and manipulate both spatial and spectral information contained in hyperspectral images across the visible and near-infrared portion of the electromagnetic spectrum. A typical hyperspectral image processing work...

  2. Hyperspectral imaging flow cytometer

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    Sinclair, Michael B.; Jones, Howland D. T.

    2017-10-25

    A hyperspectral imaging flow cytometer can acquire high-resolution hyperspectral images of particles, such as biological cells, flowing through a microfluidic system. The hyperspectral imaging flow cytometer can provide detailed spatial maps of multiple emitting species, cell morphology information, and state of health. An optimized system can image about 20 cells per second. The hyperspectral imaging flow cytometer enables many thousands of cells to be characterized in a single session.

  3. A hyperspectral X-ray computed tomography system for enhanced material identification

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    Wu, Xiaomei; Wang, Qian; Ma, Jinlei; Zhang, Wei; Li, Po; Fang, Zheng

    2017-08-01

    X-ray computed tomography (CT) can distinguish different materials according to their absorption characteristics. The hyperspectral X-ray CT (HXCT) system proposed in the present work reconstructs each voxel according to its X-ray absorption spectral characteristics. In contrast to a dual-energy or multi-energy CT system, HXCT employs cadmium telluride (CdTe) as the x-ray detector, which provides higher spectral resolution and separate spectral lines according to the material's photon-counter working principle. In this paper, a specimen containing ten different polymer materials randomly arranged was adopted for material identification by HXCT. The filtered back-projection algorithm was applied for image and spectral reconstruction. The first step was to sort the individual material components of the specimen according to their cross-sectional image intensity. The second step was to classify materials with similar intensities according to their reconstructed spectral characteristics. The results demonstrated the feasibility of the proposed material identification process and indicated that the proposed HXCT system has good prospects for a wide range of biomedical and industrial nondestructive testing applications.

  4. Validation of Spectral Unmixing Results from Informed Non-Negative Matrix Factorization (INMF) of Hyperspectral Imagery

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    Wright, L.; Coddington, O.; Pilewskie, P.

    2017-12-01

    Hyperspectral instruments are a growing class of Earth observing sensors designed to improve remote sensing capabilities beyond discrete multi-band sensors by providing tens to hundreds of continuous spectral channels. Improved spectral resolution, range and radiometric accuracy allow the collection of large amounts of spectral data, facilitating thorough characterization of both atmospheric and surface properties. We describe the development of an Informed Non-Negative Matrix Factorization (INMF) spectral unmixing method to exploit this spectral information and separate atmospheric and surface signals based on their physical sources. INMF offers marked benefits over other commonly employed techniques including non-negativity, which avoids physically impossible results; and adaptability, which tailors the method to hyperspectral source separation. The INMF algorithm is adapted to separate contributions from physically distinct sources using constraints on spectral and spatial variability, and library spectra to improve the initial guess. Using this INMF algorithm we decompose hyperspectral imagery from the NASA Hyperspectral Imager for the Coastal Ocean (HICO), with a focus on separating surface and atmospheric signal contributions. HICO's coastal ocean focus provides a dataset with a wide range of atmospheric and surface conditions. These include atmospheres with varying aerosol optical thicknesses and cloud cover. HICO images also provide a range of surface conditions including deep ocean regions, with only minor contributions from the ocean surfaces; and more complex shallow coastal regions with contributions from the seafloor or suspended sediments. We provide extensive comparison of INMF decomposition results against independent measurements of physical properties. These include comparison against traditional model-based retrievals of water-leaving, aerosol, and molecular scattering radiances and other satellite products, such as aerosol optical thickness from

  5. Phytoplankton Group Identification Using Simulated and In situ Hyperspectral Remote Sensing Reflectance

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    Hongyan Xi

    2017-08-01

    Full Text Available In the present study we investigate the bio-geo-optical boundaries for the possibility to identify dominant phytoplankton groups from hyperspectral ocean color data. A large dataset of simulated remote sensing reflectance spectra, Rrs(λ, was used. The simulation was based on measured inherent optical properties of natural water and measurements of five phytoplankton light absorption spectra representing five major phytoplankton spectral groups. These simulated data, named as C2X data, contain more than 105 different water cases, including cases typical for clearest natural waters as well as for extreme absorbing and extreme scattering waters. For the simulation the used concentrations of chlorophyll a (representing phytoplankton abundance, Chl, are ranging from 0 to 200 mg m−3, concentrations of non-algal particles, NAP, from 0 to 1,500 g m−3, and absorption coefficients of chromophoric dissolved organic matter (CDOM at 440 nm from 0 to 20 m−1. A second, independent, smaller dataset of simulated Rrs(λ used light absorption spectra of 128 cultures from six phytoplankton taxonomic groups to represent natural variability. Spectra of this test dataset are compared with spectra from the C2X data in order to evaluate to which extent the five spectral groups can be correctly identified as dominant under different optical conditions. The results showed that the identification accuracy is highly subject to the water optical conditions, i.e., contribution of and covariance in Chl, NAP, and CDOM. The identification in the simulated data is generally effective, except for waters with very low contribution by phytoplankton and for waters dominated by NAP, whereas contribution by CDOM plays only a minor role. To verify the applicability of the presented approach for natural waters, a test using in situ Rrs(λ dataset collected during a cyanobacterial bloom in Lake Taihu (China is carried out and the approach predicts blue cyanobacteria to be dominant

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

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    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.

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

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    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.

  8. Hyperspectral material identification on radiance data using single-atmosphere or multiple-atmosphere modeling

    Science.gov (United States)

    Mariano, Adrian V.; Grossmann, John M.

    2010-11-01

    Reflectance-domain methods convert hyperspectral data from radiance to reflectance using an atmospheric compensation model. Material detection and identification are performed by comparing the compensated data to target reflectance spectra. We introduce two radiance-domain approaches, Single atmosphere Adaptive Cosine Estimator (SACE) and Multiple atmosphere ACE (MACE) in which the target reflectance spectra are instead converted into sensor-reaching radiance using physics-based models. For SACE, known illumination and atmospheric conditions are incorporated in a single atmospheric model. For MACE the conditions are unknown so the algorithm uses many atmospheric models to cover the range of environmental variability, and it approximates the result using a subspace model. This approach is sometimes called the invariant method, and requires the choice of a subspace dimension for the model. We compare these two radiance-domain approaches to a Reflectance-domain ACE (RACE) approach on a HYDICE image featuring concealed materials. All three algorithms use the ACE detector, and all three techniques are able to detect most of the hidden materials in the imagery. For MACE we observe a strong dependence on the choice of the material subspace dimension. Increasing this value can lead to a decline in performance.

  9. Analytic Hyperspectral Sensing

    National Research Council Canada - National Science Library

    Coifman, Ronald R

    2005-01-01

    In the last year (no-cost extension), Plain Sight Systems reached the goal of successfully building its second NIR standoff hyperspectral imaging system, NSTIS, the Near-Infrared Spectral Target Identification System...

  10. Hyperspectral sensing of forests

    Science.gov (United States)

    Goodenough, David G.; Dyk, Andrew; Chen, Hao; Hobart, Geordie; Niemann, K. Olaf; Richardson, Ash

    2007-11-01

    Canada contains 10% of the world's forests covering an area of 418 million hectares. The sustainable management of these forest resources has become increasingly complex. Hyperspectral remote sensing can provide a wealth of new and improved information products to resource managers to make more informed decisions. Research in this area has demonstrated that hyperspectral remote sensing can be used to create more accurate products for forest inventory, forest health, foliar biochemistry, biomass, and aboveground carbon than are currently available. This paper surveys recent methods and results in hyperspectral sensing of forests and describes space initiatives for hyperspectral sensing.

  11. Hyperspectral imaging for presumptive identification of bacterial colonies on solid chromogenic culture media

    Science.gov (United States)

    Guillemot, Mathilde; Midahuen, Rony; Archeny, Delpine; Fulchiron, Corine; Montvernay, Regis; Perrin, Guillaume; Leroux, Denis F.

    2016-04-01

    BioMérieux is automating the microbiology laboratory in order to reduce cost (less manpower and consumables), to improve performance (increased sensitivity, machine algorithms) and to gain traceability through optimization of the clinical laboratory workflow. In this study, we evaluate the potential of Hyperspectral imaging (HSI) as a substitute to human visual observation when performing the task of microbiological culture interpretation. Microbial colonies from 19 strains subcategorized in 6 chromogenic classes were analyzed after a 24h-growth on a chromogenic culture medium (chromID® CPS Elite, bioMérieux, France). The HSI analysis was performed in the VNIR region (400-900 nm) using a linescan configuration. Using algorithms relying on Linear Spectral Unmixing, and using exclusively Diffuse Reflectance Spectra (DRS) as input data, we report interclass classification accuracies of 100% using a fully automatable approach and no use of morphological information. In order to eventually simplify the instrument, the performance of degraded DRS was also evaluated using only the most discriminant 14 spectral channels (a model for a multispectral approach) or 3 channels (model of a RGB image). The overall classification performance remains unchanged for our multispectral model but is degraded for the predicted RGB model, hints that a multispectral solution might bring the answer for an improved colony recognition.

  12. Rapid identification of salmonella serotypes with stereo and hyperspectral microscope imaging Methods

    Science.gov (United States)

    The hyperspectral microscope imaging (HMI) method can reduce detection time within 8 hours including incubation process. The early and rapid detection with this method in conjunction with the high throughput capabilities makes HMI method a prime candidate for implementation for the food industry. Th...

  13. Estimation for sparse vegetation information in desertification region based on Tiangong-1 hyperspectral image.

    Science.gov (United States)

    Wu, Jun-Jun; Gao, Zhi-Hai; Li, Zeng-Yuan; Wang, Hong-Yan; Pang, Yong; Sun, Bin; Li, Chang-Long; Li, Xu-Zhi; Zhang, Jiu-Xing

    2014-03-01

    In order to estimate the sparse vegetation information accurately in desertification region, taking southeast of Sunite Right Banner, Inner Mongolia, as the test site and Tiangong-1 hyperspectral image as the main data, sparse vegetation coverage and biomass were retrieved based on normalized difference vegetation index (NDVI) and soil adjusted vegetation index (SAVI), combined with the field investigation data. Then the advantages and disadvantages between them were compared. Firstly, the correlation between vegetation indexes and vegetation coverage under different bands combination was analyzed, as well as the biomass. Secondly, the best bands combination was determined when the maximum correlation coefficient turned up between vegetation indexes (VI) and vegetation parameters. It showed that the maximum correlation coefficient between vegetation parameters and NDVI could reach as high as 0.7, while that of SAVI could nearly reach 0.8. The center wavelength of red band in the best bands combination for NDVI was 630nm, and that of the near infrared (NIR) band was 910 nm. Whereas, when the center wavelength was 620 and 920 nm respectively, they were the best combination for SAVI. Finally, the linear regression models were established to retrieve vegetation coverage and biomass based on Tiangong-1 VIs. R2 of all models was more than 0.5, while that of the model based on SAVI was higher than that based on NDVI, especially, the R2 of vegetation coverage retrieve model based on SAVI was as high as 0.59. By intersection validation, the standard errors RMSE based on SAVI models were lower than that of the model based on NDVI. The results showed that the abundant spectral information of Tiangong-1 hyperspectral image can reflect the actual vegetaion condition effectively, and SAVI can estimate the sparse vegetation information more accurately than NDVI in desertification region.

  14. System parameter identification information criteria and algorithms

    CERN Document Server

    Chen, Badong; Hu, Jinchun; Principe, Jose C

    2013-01-01

    Recently, criterion functions based on information theoretic measures (entropy, mutual information, information divergence) have attracted attention and become an emerging area of study in signal processing and system identification domain. This book presents a systematic framework for system identification and information processing, investigating system identification from an information theory point of view. The book is divided into six chapters, which cover the information needed to understand the theory and application of system parameter identification. The authors' research pr

  15. [Retrieval of Copper Pollution Information from Hyperspectral Satellite Data in a Vegetation Cover Mining Area].

    Science.gov (United States)

    Qu, Yong-hua; Jiao, Si-hong; Liu, Su-hong; Zhu, Ye-qing

    2015-11-01

    Heavy metal mining activities have caused the complex influence on the ecological environment of the mining regions. For example, a large amount of acidic waste water containing heavy metal ions have be produced in the process of copper mining which can bring serious pollution to the ecological environment of the region. In the previous research work, bare soil is mainly taken as the research target when monitoring environmental pollution, and thus the effects of land surface vegetation have been ignored. It is well known that vegetation condition is one of the most important indictors to reflect the ecological change in a certain region and there is a significant linkage between the vegetation spectral characteristics and the heavy metal when the vegetation is effected by the heavy metal pollution. It means the vegetation is sensitive to heavy metal pollution by their physiological behaviors in response to the physiological ecology change of their growing environment. The conventional methods, which often rely on large amounts of field survey data and laboratorial chemical analysis, are time consuming and costing a lot of material resources. The spectrum analysis method using remote sensing technology can acquire the information of the heavy mental content in the vegetation without touching it. However, the retrieval of that information from the hyperspectral data is not an easy job due to the difficulty in figuring out the specific band, which is sensitive to the specific heavy metal, from a huge number of hyperspectral bands. Thus the selection of the sensitive band is the key of the spectrum analysis method. This paper proposed a statistical analysis method to find the feature band sensitive to heavy metal ion from the hyperspectral data and to then retrieve the metal content using the field survey data and the hyperspectral images from China Environment Satellite HJ-1. This method selected copper ion content in the leaves as the indicator of copper pollution

  16. Multi-information fusion sparse coding with preserving local structure for hyperspectral image classification

    Science.gov (United States)

    Wei, Xiaohui; Zhu, Wen; Liao, Bo; Gu, Changlong; Li, Weibiao

    2017-10-01

    The key question of sparse coding (SC) is how to exploit the information that already exists to acquire the robust sparse representations (SRs) of distinguishing different objects for hyperspectral image (HSI) classification. We propose a multi-information fusion SC framework, which fuses the spectral, spatial, and label information in the same level, to solve the above question. In particular, pixels from disjointed spatial clusters, which are obtained by cutting the given HSI in space, are individually and sparsely encoded. Then, due to the importance of spatial structure, graph- and hypergraph-based regularizers are enforced to motivate the obtained representations smoothness and to preserve the local consistency for each spatial cluster. The latter simultaneously considers the spectrum, spatial, and label information of multiple pixels that have a great probability with the same label. Finally, a linear support vector machine is selected as the final classifier with the learned SRs as input. Experiments conducted on three frequently used real HSIs show that our methods can achieve satisfactory results compared with other state-of-the-art methods.

  17. Hyperspectral image compressing using wavelet-based method

    Science.gov (United States)

    Yu, Hui; Zhang, Zhi-jie; Lei, Bo; Wang, Chen-sheng

    2017-10-01

    Hyperspectral imaging sensors can acquire images in hundreds of continuous narrow spectral bands. Therefore each object presented in the image can be identified from their spectral response. However, such kind of imaging brings a huge amount of data, which requires transmission, processing, and storage resources for both airborne and space borne imaging. Due to the high volume of hyperspectral image data, the exploration of compression strategies has received a lot of attention in recent years. Compression of hyperspectral data cubes is an effective solution for these problems. Lossless compression of the hyperspectral data usually results in low compression ratio, which may not meet the available resources; on the other hand, lossy compression may give the desired ratio, but with a significant degradation effect on object identification performance of the hyperspectral data. Moreover, most hyperspectral data compression techniques exploits the similarities in spectral dimensions; which requires bands reordering or regrouping, to make use of the spectral redundancy. In this paper, we explored the spectral cross correlation between different bands, and proposed an adaptive band selection method to obtain the spectral bands which contain most of the information of the acquired hyperspectral data cube. The proposed method mainly consist three steps: First, the algorithm decomposes the original hyperspectral imagery into a series of subspaces based on the hyper correlation matrix of the hyperspectral images between different bands. And then the Wavelet-based algorithm is applied to the each subspaces. At last the PCA method is applied to the wavelet coefficients to produce the chosen number of components. The performance of the proposed method was tested by using ISODATA classification method.

  18. Use of infrared hyperspectral imaging as an aid for paint identification

    Directory of Open Access Journals (Sweden)

    A. Polak

    2016-10-01

    Full Text Available Art authentication is a complicated process that often requires the extensive study of high value objects. Although a series of non-destructive techniques is already available for art scientists, new techniques, extending current possibilities, are still required. In this paper, the use of a novel mid-infrared tunable imager is proposed as an active hyperspectral imaging system for art work analysis. The system provides access to a range of wavelengths in the electromagnetic spectrum (2500–3750 nm which are otherwise difficult to access using conventional hyperspectral imaging (HSI equipment. The use of such a tool could be beneficial if applied to the paint classification problem and could help analysts map the diversity of pigments within a given painting. The performance of this tool is demonstrated and compared with a conventional, off-the-shelf HSI system operating in the near infrared spectral region (900–1700 nm. Various challenges associated with laser-based imaging are demonstrated and solutions to these challenges as well as the results of applying classification algorithms to datasets captured using both HSI systems are presented. While the conventional HSI system provides data in which more pigments can be accurately classified, the result of applying the proposed laser-based imaging system demonstrates the validity of this technique for application in art authentication tasks.

  19. Monitoring Deformation in Graphene Through Hyperspectral Synchrotron Spectroscopy to Inform Fabrication

    Energy Technology Data Exchange (ETDEWEB)

    Winter, Allen Douglas [School; Rojas, Wudmir Y. [School; Williams, Adrienne D. [Materials; Kim, Steve S. [Materials; Ouchen, Fahima [Materials; Fischer, Daniel A. [National Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States; Weiland, Conan [Synchrotron Research Inc., Melbourne, Florida 32901, United States; Principe, Edward [Synchrotron Research Inc., Melbourne, Florida 32901, United States; Banerjee, Sarbajit [Department; amp,M University, College Station, Texas 77840, United States; Huynh, Chuong [Carl Zeiss Microscopy, LLC, One Corporation Way, Peabody, Massachusetts 01960, United States; Naik, Rajesh R. [Materials; Liu, Yijin [Stanford; Mehta, Apurva [Stanford; Grote, James [Materials; Prendergast, David [Molecular; Campo, Eva M. [School; Department

    2017-07-13

    The promise from graphene to produce devices with high mobilities and detectors with fast response times is truncated in practice by strain and deformation originating during growth and subsequent processing. This work describes effects from graphene growth, multiple layer transfer, and substrate termination on out of plane deformation, critical to device performance. Synchrotron spectroscopy data was acquired with a state-of-the-art hyperspectral large-area detector to describe growth and processing with molecular sensitivity at wafer length scales. A study of methodologies used in data analysis discouraged dichroic ratio approaches in favor of orbital vector approximations and data mining algorithms. Orbital vector methods provide a physical insight into mobility-detrimental rippling by identifying ripple frequency as main actor, rather than intensity; which was confirmed by data mining algorithms, and in good agreement with electron scattering theories of corrugation in graphene. This work paves the way to efficient information from mechanical properties in graphene in a high throughput mode throughout growth and processing in a materials by design approach.

  20. Identification of species and geographical strains of Sitophilus oryzae and Sitophilus zeamais using the visible/near-infrared hyperspectral imaging technique.

    Science.gov (United States)

    Cao, Yang; Zhang, Chaojie; Chen, Quansheng; Li, Yanyu; Qi, Shuai; Tian, Lin; Ren, YongLin

    2015-08-01

    Identifying stored-product insects is essential for granary management. Automated, computer-based classification methods are rapidly developing in many areas. A hyperspectral imaging technique could potentially be developed to identify stored-product insect species and geographical strains. This study tested and adapted the technique using four geographical strains of each of two insect species, the rice weevil and maize weevil, to collect and analyse the resultant hyperspectral data. Three characteristic images that corresponded to the dominant wavelengths, 505, 659 and 955 nm, were selected by multivariate image analysis. Each image was processed, and 22 morphological and textural features from regions of interest were extracted as the inputs for an identification model. We found the backpropagation neural network model to be the superior method for distinguishing between the insect species and geographical strains. The overall recognition rates of the classification model for insect species were 100 and 98.13% for the calibration and prediction sets respectively, while the rates of the model for geographical strains were 94.17 and 86.88% respectively. This study has demonstrated that hyperspectral imaging, together with the appropriate recognition method, could provide a potential instrument for identifying insects and could become a useful tool for identification of Sitophilus oryzae and Sitophilus zeamais to aid in the management of stored-product insects. © 2014 Society of Chemical Industry.

  1. Improvement to the PhytoDOAS method for identification of coccolithophores using hyper-spectral satellite data

    Directory of Open Access Journals (Sweden)

    A. Sadeghi

    2012-11-01

    Full Text Available The goal of this study was to improve PhytoDOAS, which is a new retrieval method for quantitative identification of major phytoplankton functional types (PFTs using hyper-spectral satellite data. PhytoDOAS is an extension of the Differential Optical Absorption Spectroscopy (DOAS, a method for detection of atmospheric trace gases, developed for remote identification of oceanic phytoplankton groups. Thus far, PhytoDOAS has been successfully exploited to identify cyanobacteria and diatoms over the global ocean from SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY hyper-spectral data. This study aimed to improve PhytoDOAS for remote identification of coccolithophores, another functional group of phytoplankton. The main challenge for retrieving more PFTs by PhytoDOAS is to overcome the correlation effects between different PFT absorption spectra. Different PFTs are composed of different types and amounts of pigments, but also have pigments in common, e.g. chl a, causing correlation effects in the usual performance of the PhytoDOAS retrieval. Two ideas have been implemented to improve PhytoDOAS for the PFT retrieval of more phytoplankton groups. Firstly, using the fourth-derivative spectroscopy, the peak positions of the main pigment components in each absorption spectrum have been derived. After comparing the corresponding results of major PFTs, the optimized fit-window for the PhytoDOAS retrieval of each PFT was determined. Secondly, based on the results from derivative spectroscopy, a simultaneous fit of PhytoDOAS has been proposed and tested for a selected set of PFTs (coccolithophores, diatoms and dinoflagellates within an optimized fit-window, proven by spectral orthogonality tests. The method was then applied to the processing of SCIAMACHY data over the year 2005. Comparisons of the PhytoDOAS coccolithophore retrievals in 2005 with other coccolithophore-related data showed similar patterns in their

  2. ℓ0 -based sparse hyperspectral unmixing using spectral information and a multi-objectives formulation

    Science.gov (United States)

    Xu, Xia; Shi, Zhenwei; Pan, Bin

    2018-07-01

    Sparse unmixing aims at recovering pure materials from hyperpspectral images and estimating their abundance fractions. Sparse unmixing is actually ℓ0 problem which is NP-h ard, and a relaxation is often used. In this paper, we attempt to deal with ℓ0 problem directly via a multi-objective based method, which is a non-convex manner. The characteristics of hyperspectral images are integrated into the proposed method, which leads to a new spectra and multi-objective based sparse unmixing method (SMoSU). In order to solve the ℓ0 norm optimization problem, the spectral library is encoded in a binary vector, and a bit-wise flipping strategy is used to generate new individuals in the evolution process. However, a multi-objective method usually produces a number of non-dominated solutions, while sparse unmixing requires a single solution. How to make the final decision for sparse unmixing is challenging. To handle this problem, we integrate the spectral characteristic of hyperspectral images into SMoSU. By considering the spectral correlation in hyperspectral data, we improve the Tchebycheff decomposition function in SMoSU via a new regularization item. This regularization item is able to enforce the individual divergence in the evolution process of SMoSU. In this way, the diversity and convergence of population is further balanced, which is beneficial to the concentration of individuals. In the experiments part, three synthetic datasets and one real-world data are used to analyse the effectiveness of SMoSU, and several state-of-art sparse unmixing algorithms are compared.

  3. 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.

  4. Hyperspectral image processing

    CERN Document Server

    Wang, Liguo

    2016-01-01

    Based on the authors’ research, this book introduces the main processing techniques in hyperspectral imaging. In this context, SVM-based classification, distance comparison-based endmember extraction, SVM-based spectral unmixing, spatial attraction model-based sub-pixel mapping, and MAP/POCS-based super-resolution reconstruction are discussed in depth. Readers will gain a comprehensive understanding of these cutting-edge hyperspectral imaging techniques. Researchers and graduate students in fields such as remote sensing, surveying and mapping, geosciences and information systems will benefit from this valuable resource.

  5. System identification with information theoretic criteria

    NARCIS (Netherlands)

    A.A. Stoorvogel; J.H. van Schuppen (Jan)

    1995-01-01

    textabstractAttention is focused in this paper on the approximation problem of system identification with information theoretic criteria. For a class of problems it is shown that the criterion of mutual information rate is identical to the criterion of exponential-of-quadratic cost and to

  6. 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.

  7. Hyperspectral fluorescence imaging using violet LEDs as excitation sources for fecal matter contaminate identification on spinach leaves

    Science.gov (United States)

    Food safety in the production of fresh produce for human consumption is a worldwide issue and needs to be addressed to decrease foodborne illnesses and resulting costs. Hyperspectral fluorescence imaging coupled with multivariate image analysis techniques for detection of fecal contaminates on spina...

  8. Identification and Severity Determination of Wheat Stripe Rust and Wheat Leaf Rust Based on Hyperspectral Data Acquired Using a Black-Paper-Based Measuring Method

    Science.gov (United States)

    Ruan, Liu; Wang, Rui; Liu, Qi; Ma, Zhanhong; Li, Xiaolong; Cheng, Pei; Wang, Haiguang

    2016-01-01

    It is important to implement detection and assessment of plant diseases based on remotely sensed data for disease monitoring and control. Hyperspectral data of healthy leaves, leaves in incubation period and leaves in diseased period of wheat stripe rust and wheat leaf rust were collected under in-field conditions using a black-paper-based measuring method developed in this study. After data preprocessing, the models to identify the diseases were built using distinguished partial least squares (DPLS) and support vector machine (SVM), and the disease severity inversion models of stripe rust and the disease severity inversion models of leaf rust were built using quantitative partial least squares (QPLS) and support vector regression (SVR). All the models were validated by using leave-one-out cross validation and external validation. The diseases could be discriminated using both distinguished partial least squares and support vector machine with the accuracies of more than 99%. For each wheat rust, disease severity levels were accurately retrieved using both the optimal QPLS models and the optimal SVR models with the coefficients of determination (R2) of more than 0.90 and the root mean square errors (RMSE) of less than 0.15. The results demonstrated that identification and severity evaluation of stripe rust and leaf rust at the leaf level could be implemented based on the hyperspectral data acquired using the developed method. A scientific basis was provided for implementing disease monitoring by using aerial and space remote sensing technologies. PMID:27128464

  9. Identification and Severity Determination of Wheat Stripe Rust and Wheat Leaf Rust Based on Hyperspectral Data Acquired Using a Black-Paper-Based Measuring Method.

    Science.gov (United States)

    Wang, Hui; Qin, Feng; Ruan, Liu; Wang, Rui; Liu, Qi; Ma, Zhanhong; Li, Xiaolong; Cheng, Pei; Wang, Haiguang

    2016-01-01

    It is important to implement detection and assessment of plant diseases based on remotely sensed data for disease monitoring and control. Hyperspectral data of healthy leaves, leaves in incubation period and leaves in diseased period of wheat stripe rust and wheat leaf rust were collected under in-field conditions using a black-paper-based measuring method developed in this study. After data preprocessing, the models to identify the diseases were built using distinguished partial least squares (DPLS) and support vector machine (SVM), and the disease severity inversion models of stripe rust and the disease severity inversion models of leaf rust were built using quantitative partial least squares (QPLS) and support vector regression (SVR). All the models were validated by using leave-one-out cross validation and external validation. The diseases could be discriminated using both distinguished partial least squares and support vector machine with the accuracies of more than 99%. For each wheat rust, disease severity levels were accurately retrieved using both the optimal QPLS models and the optimal SVR models with the coefficients of determination (R2) of more than 0.90 and the root mean square errors (RMSE) of less than 0.15. The results demonstrated that identification and severity evaluation of stripe rust and leaf rust at the leaf level could be implemented based on the hyperspectral data acquired using the developed method. A scientific basis was provided for implementing disease monitoring by using aerial and space remote sensing technologies.

  10. Classification of gram-positive and gram-negative foodborne pathogenic bacteria with hyperspectral microscope imaging

    Science.gov (United States)

    Optical method with hyperspectral microscope imaging (HMI) has potential for identification of foodborne pathogenic bacteria from microcolonies rapidly with a cell level. A HMI system that provides both spatial and spectral information could be an effective tool for analyzing spectral characteristic...

  11. Hyperspectral Imaging and SPA-LDA Quantitative Analysis for Detection of Colon Cancer Tissue

    Science.gov (United States)

    Yuan, X.; Zhang, D.; Wang, Ch.; Dai, B.; Zhao, M.; Li, B.

    2018-05-01

    Hyperspectral imaging (HSI) has been demonstrated to provide a rapid, precise, and noninvasive method for cancer detection. However, because HSI contains many data, quantitative analysis is often necessary to distill information useful for distinguishing cancerous from normal tissue. To demonstrate that HSI with our proposed algorithm can make this distinction, we built a Vis-NIR HSI setup and made many spectral images of colon tissues, and then used a successive projection algorithm (SPA) to analyze the hyperspectral image data of the tissues. This was used to build an identification model based on linear discrimination analysis (LDA) using the relative reflectance values of the effective wavelengths. Other tissues were used as a prediction set to verify the reliability of the identification model. The results suggest that Vis-NIR hyperspectral images, together with the spectroscopic classification method, provide a new approach for reliable and safe diagnosis of colon cancer and could lead to advances in cancer diagnosis generally.

  12. Multipurpose Hyperspectral Imaging System

    Science.gov (United States)

    Mao, Chengye; Smith, David; Lanoue, Mark A.; Poole, Gavin H.; Heitschmidt, Jerry; Martinez, Luis; Windham, William A.; Lawrence, Kurt C.; Park, Bosoon

    2005-01-01

    A hyperspectral imaging system of high spectral and spatial resolution that incorporates several innovative features has been developed to incorporate a focal plane scanner (U.S. Patent 6,166,373). This feature enables the system to be used for both airborne/spaceborne and laboratory hyperspectral imaging with or without relative movement of the imaging system, and it can be used to scan a target of any size as long as the target can be imaged at the focal plane; for example, automated inspection of food items and identification of single-celled organisms. The spectral resolution of this system is greater than that of prior terrestrial multispectral imaging systems. Moreover, unlike prior high-spectral resolution airborne and spaceborne hyperspectral imaging systems, this system does not rely on relative movement of the target and the imaging system to sweep an imaging line across a scene. This compact system (see figure) consists of a front objective mounted at a translation stage with a motorized actuator, and a line-slit imaging spectrograph mounted within a rotary assembly with a rear adaptor to a charged-coupled-device (CCD) camera. Push-broom scanning is carried out by the motorized actuator which can be controlled either manually by an operator or automatically by a computer to drive the line-slit across an image at a focal plane of the front objective. To reduce the cost, the system has been designed to integrate as many as possible off-the-shelf components including the CCD camera and spectrograph. The system has achieved high spectral and spatial resolutions by using a high-quality CCD camera, spectrograph, and front objective lens. Fixtures for attachment of the system to a microscope (U.S. Patent 6,495,818 B1) make it possible to acquire multispectral images of single cells and other microscopic objects.

  13. Medical hyperspectral imaging: a review

    Science.gov (United States)

    Lu, Guolan; Fei, Baowei

    2014-01-01

    Abstract. Hyperspectral imaging (HSI) is an emerging imaging modality for medical applications, especially in disease diagnosis and image-guided surgery. HSI acquires a three-dimensional dataset called hypercube, with two spatial dimensions and one spectral dimension. Spatially resolved spectral imaging obtained by HSI provides diagnostic information about the tissue physiology, morphology, and composition. This review paper presents an overview of the literature on medical hyperspectral imaging technology and its applications. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application. PMID:24441941

  14. Hyperspectral remote sensing

    National Research Council Canada - National Science Library

    Eismann, Michael Theodore

    2012-01-01

    ..., and hyperspectral data processing. While there are many resources that suitably cover these areas individually and focus on specific aspects of the hyperspectral remote sensing field, this book provides a holistic treatment...

  15. Infrared upconversion hyperspectral imaging

    DEFF Research Database (Denmark)

    Kehlet, Louis Martinus; Tidemand-Lichtenberg, Peter; Dam, Jeppe Seidelin

    2015-01-01

    In this Letter, hyperspectral imaging in the mid-IR spectral region is demonstrated based on nonlinear frequency upconversion and subsequent imaging using a standard Si-based CCD camera. A series of upconverted images are acquired with different phase match conditions for the nonlinear frequency...... conversion process. From this, a sequence of monochromatic images in the 3.2-3.4 mu m range is generated. The imaged object consists of a standard United States Air Force resolution target combined with a polystyrene film, resulting in the presence of both spatial and spectral information in the infrared...... image. (C) 2015 Optical Society of America...

  16. Hyperspectral remote sensing

    CERN Document Server

    Eismann, Michael

    2012-01-01

    Hyperspectral remote sensing is an emerging, multidisciplinary field with diverse applications that builds on the principles of material spectroscopy, radiative transfer, imaging spectrometry, and hyperspectral data processing. This book provides a holistic treatment that captures its multidisciplinary nature, emphasizing the physical principles of hyperspectral remote sensing.

  17. Hyperspectral information identification for characterizing metallogenic factors of Taoshan uranium ore-field

    International Nuclear Information System (INIS)

    Zhang Jielin

    2007-01-01

    The application of high resolution remote sensing technology to uranium mineralization is discussed in detail. By utilizing field hyperspectrum measurement method (VIS/NIR-SWIR-FIR) and fusion technology of ETM image and Radarsat data, the spectral characteristic of uranium mineralization factors including rock mass, ore-control fault and hydrothermal alteration are studied in Taoshan granite type uranium deposits. The main ore-control structure has been identified based on remote sensing image feature. Those results and experiences provide the new remote sensing approaches to the prospecting and evaluation of uranium resources. (authors)

  18. THE BENEFITS OF TERRESTRIAL LASER SCANNING AND HYPERSPECTRAL DATA FUSION PRODUCTS

    Directory of Open Access Journals (Sweden)

    S. J. Buckley

    2012-10-01

    Full Text Available Close range hyperspectral imaging is a developing method for the analysis and identification of material composition in many applications, such as in within the earth sciences. Using compact imaging devices in the field allows near-vertical topography to be imaged, thus bypassing the key limitations of viewing angle and resolution that preclude the use of airborne and spaceborne platforms. Terrestrial laser scanning allows 3D topography to be captured with high precision and spatial resolution. The combination of 3D geometry from laser scanning, and material properties from hyperspectral imaging allows new fusion products to be created, adding new information for solving application problems. This paper highlights the advantages of terrestrial lidar and hyperspectral integration, focussing on the qualitative and quantitative aspects, with examples from a geological field application. Accurate co-registration of the two data types is required. This allows 2D pixels to be linked to the 3D lidar geometry, giving increased quantitative analysis as classified material vectors are projected to 3D space for calculation of areas and examination of spatial relationships. User interpretation of hyperspectral results in a spatially-meaningful manner is facilitated using visual methods that combine the geometric and mineralogical products in a 3D environment. Point cloud classification and the use of photorealistic modelling enhance qualitative validation and interpretation, and allow image registration accuracy to be checked. A method for texture mapping of lidar meshes with multiple image textures, both conventional digital photos and hyperspectral results, is described. The integration of terrestrial laser scanning and hyperspectral imaging is a valuable means of providing new analysis methods, suitable for many applications requiring linked geometric and chemical information.

  19. 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...

  20. Hyperspectral forest monitoring and imaging implications

    Science.gov (United States)

    Goodenough, David G.; Bannon, David

    2014-05-01

    The forest biome is vital to the health of the earth. Canada and the United States have a combined forest area of 4.68 Mkm2. The monitoring of these forest resources has become increasingly complex. Hyperspectral remote sensing can provide a wealth of improved information products to land managers to make more informed decisions. Research in this area has demonstrated that hyperspectral remote sensing can be used to create more accurate products for forest inventory (major forest species), forest health, foliar biochemistry, biomass, and aboveground carbon. Operationally there is a requirement for a mix of airborne and satellite approaches. This paper surveys some methods and results in hyperspectral sensing of forests and discusses the implications for space initiatives with hyperspectral sensing

  1. Hyperspectral cytometry.

    Science.gov (United States)

    Grégori, Gérald; Rajwa, Bartek; Patsekin, Valery; Jones, James; Furuki, Motohiro; Yamamoto, Masanobu; Paul Robinson, J

    2014-01-01

    Hyperspectral cytometry is an emerging technology for single-cell analysis that combines ultrafast optical spectroscopy and flow cytometry. Spectral cytometry systems utilize diffraction gratings or prism-based monochromators to disperse fluorescence signals from multiple labels (organic dyes, nanoparticles, or fluorescent proteins) present in each analyzed bioparticle onto linear detector arrays such as multianode photomultipliers or charge-coupled device sensors. The resultant data, consisting of a series of characterizing every analyzed cell, are not compensated by employing the traditional cytometry approach, but rather are spectrally unmixed utilizing algorithms such as constrained Poisson regression or non-negative matrix factorization. Although implementations of spectral cytometry were envisioned as early as the 1980s, only recently has the development of highly sensitive photomultiplier tube arrays led to design and construction of functional prototypes and subsequently to introduction of commercially available systems. This chapter summarizes the historical efforts and work in the field of spectral cytometry performed at Purdue University Cytometry Laboratories and describes the technology developed by Sony Corporation that resulted in release of the first commercial spectral cytometry system-the Sony SP6800. A brief introduction to spectral data analysis is also provided, with emphasis on the differences between traditional polychromatic and spectral cytometry approaches.

  2. 基于高光谱图像处理的大豆品种识别%Identification of soybean seed varieties based on hyperspectral image

    Institute of Scientific and Technical Information of China (English)

    谭克竹; 柴玉华; 宋伟先; 曹晓达

    2014-01-01

    Different soybean seed varieties have different components (oil, protein, fat etc.) content. Identification of soybean seed varieties is a critical factor that improves the quality of produced soybean. In this study, hyperspectral image technique was applied in order to classify soybean seeds based on their varieties. The spectral reflectance data was collected using the optical sensor system with spectral region of 1000-2500nm. Principal component analysis (PCA) was performed to reduce the dimensionality of the data and remove the redundancy. Scores of four PCs were used as input features in the classification algorithm. Four texture feature parameters (angular second moment, energy, entropy and correlation) were extracted from each feature image selected by PCA. For the extraction of specific features, four significant feature parameters were computed from the 16 characteristic variables. Artificial neural network (ANN) classifier was employed for classification using top selected features. The obtained average training accuracy rate was 97.50%and the average testing accuracy rate was 93.88%. Thus, the results confirmed that hyperspectral image technique in-conjunction with BP neural network could be useful for soybean seed varieties classification.%大豆组分(油,蛋白质,脂肪等)在不同的大豆品种间差异很大。对于提高大豆品质来说,大豆品种识别是一个关键因素。该文利用高光谱图像技术对不同的大豆品种进行识别。利用高光谱成像系统获取大豆样本1000~2500 nm范围的光谱反射数据;应用主成分分析法(PCA,principal component analysis)对获取到的光谱数据进行数据降维并去除冗余数据;在分类算法中将得分高的主成分值作为输入特征,通过PCA方法从每个特征图像中提取4个特征变量(能量、熵、惯性矩和相关性);对于具体特征提取,从16个特征变量中提取8个重要特征参数;根据选择的

  3. An algorithm for hyperspectral remote sensing of aerosols: 2. Information content analysis for aerosol parameters and principal components of surface spectra

    Science.gov (United States)

    Hou, Weizhen; Wang, Jun; Xu, Xiaoguang; Reid, Jeffrey S.

    2017-05-01

    This paper describes the second part of a series of investigation to develop algorithms for simultaneous retrieval of aerosol parameters and surface reflectance from the future hyperspectral and geostationary satellite sensors such as Tropospheric Emissions: Monitoring of POllution (TEMPO). The information content in these hyperspectral measurements is analyzed for 6 principal components (PCs) of surface spectra and a total of 14 aerosol parameters that describe the columnar aerosol volume Vtotal, fine-mode aerosol volume fraction, and the size distribution and wavelength-dependent index of refraction in both coarse and fine mode aerosols. Forward simulations of atmospheric radiative transfer are conducted for 5 surface types (green vegetation, bare soil, rangeland, concrete and mixed surface case) and a wide range of aerosol mixtures. It is shown that the PCs of surface spectra in the atmospheric window channel could be derived from the top-of-the-atmosphere reflectance in the conditions of low aerosol optical depth (AOD ≤ 0.2 at 550 nm), with a relative error of 1%. With degree freedom for signal analysis and the sequential forward selection method, the common bands for different aerosol mixture types and surface types can be selected for aerosol retrieval. The first 20% of our selected bands accounts for more than 90% of information content for aerosols, and only 4 PCs are needed to reconstruct surface reflectance. However, the information content in these common bands from each TEMPO individual observation is insufficient for the simultaneous retrieval of surface's PC weight coefficients and multiple aerosol parameters (other than Vtotal). In contrast, with multiple observations for the same location from TEMPO in multiple consecutive days, 1-3 additional aerosol parameters could be retrieved. Consequently, a self-adjustable aerosol retrieval algorithm to account for surface types, AOD conditions, and multiple-consecutive observations is recommended to derive

  4. Raman hyperspectral imaging in conjunction with independent component analysis as a forensic tool for explosive analysis: The case of an ATM explosion.

    Science.gov (United States)

    Almeida, Mariana Ramos; Logrado, Lucio Paulo Lima; Zacca, Jorge Jardim; Correa, Deleon Nascimento; Poppi, Ronei Jesus

    2017-11-01

    In this work, Raman hyperspectral imaging, in conjunction with independent component analysis, was employed as an analytical methodology to detect an ammonium nitrate fuel oil (ANFO) explosive in banknotes after an ATM explosion experiment. The proposed methodology allows for the identification of the ANFO explosive without sample preparation or destroying the sample, at quantities as small as 70μgcm -2 . The explosive was identified following ICA data decomposition by the characteristic nitrate band at 1044cm -1 . The use of Raman hyperspectral imaging and independent component analysis shows great potential for identifying forensic samples by providing chemical and spatial information. Copyright © 2017 Elsevier B.V. All rights reserved.

  5. D Reconstruction from Uav-Based Hyperspectral Images

    Science.gov (United States)

    Liu, L.; Xu, L.; Peng, J.

    2018-04-01

    Reconstructing the 3D profile from a set of UAV-based images can obtain hyperspectral information, as well as the 3D coordinate of any point on the profile. Our images are captured from the Cubert UHD185 (UHD) hyperspectral camera, which is a new type of high-speed onboard imaging spectrometer. And it can get both hyperspectral image and panchromatic image simultaneously. The panchromatic image have a higher spatial resolution than hyperspectral image, but each hyperspectral image provides considerable information on the spatial spectral distribution of the object. Thus there is an opportunity to derive a high quality 3D point cloud from panchromatic image and considerable spectral information from hyperspectral image. The purpose of this paper is to introduce our processing chain that derives a database which can provide hyperspectral information and 3D position of each point. First, We adopt a free and open-source software, Visual SFM which is based on structure from motion (SFM) algorithm, to recover 3D point cloud from panchromatic image. And then get spectral information of each point from hyperspectral image by a self-developed program written in MATLAB. The production can be used to support further research and applications.

  6. Postfire soil burn severity mapping with hyperspectral image unmixing

    Science.gov (United States)

    Peter R. Robichaud; Sarah A. Lewis; Denise Y. M. Laes; Andrew T. Hudak; Raymond F. Kokaly; Joseph A. Zamudio

    2007-01-01

    Burn severity is mapped after wildfires to evaluate immediate and long-term fire effects on the landscape. Remotely sensed hyperspectral imagery has the potential to provide important information about fine-scale ground cover components that are indicative of burn severity after large wildland fires. Airborne hyperspectral imagery and ground data were collected after...

  7. Substance Identification Information from EPA's Substance Registry

    Data.gov (United States)

    U.S. Environmental Protection Agency — The Substance Registry Services (SRS) is the authoritative resource for basic information about substances of interest to the U.S. EPA and its state and tribal...

  8. Geometrical identification of quantum and information theories

    International Nuclear Information System (INIS)

    Caianiello, E.R.

    1983-01-01

    The interrelation of quantum and information theories is investigation on the base of the conception of cross-entropy. It is assumed that ''complex information geometry'' may serve as a tool for ''technological transfer'' from one research field to the other which is not connected directly with the first one. It is pointed out that the ''infinitesimal distance'' ds 2 and ''infinitesimal cross-entropy'' dHsub(c) coincide

  9. 15 CFR 718.2 - Identification of confidential business information.

    Science.gov (United States)

    2010-01-01

    ... business information. 718.2 Section 718.2 Commerce and Foreign Trade Regulations Relating to Commerce and Foreign Trade (Continued) BUREAU OF INDUSTRY AND SECURITY, DEPARTMENT OF COMMERCE CHEMICAL WEAPONS CONVENTION REGULATIONS CONFIDENTIAL BUSINESS INFORMATION 718.2 Identification of confidential business...

  10. Study on the forest vegetation restoration monitoring using HJ-1A hyperspectral data

    International Nuclear Information System (INIS)

    Chuan, Zhang; Fawang, Ye; Hongcheng, Liu; Haixia, He

    2014-01-01

    In this paper, Xunke County was studied using HJ-1A hyperspectral data for monitoring vegetation restoration after forest fires. The pre-processing procedure including data format conversion, image mosaicing and atmospheric correction. Support vector machine classification was used to perform surface feature identification based on the extracted spectral end-members. On that basis, the image area was divided into seven categories and statistical analysis of classification types was performed. The results showed that HJ-1A hyperspectral data had great potential in fine classification of surface features and the accuracy of classification was 91.8%. The mild and severe fire-affected area extraction provided useful reference for disaster recovery monitoring. Furthermore, the distinction between coniferous forest and broadleaved forest can offer useful information for forest fire prevention and early warning to some extent

  11. Hyperspectral image segmentation using a cooperative nonparametric approach

    Science.gov (United States)

    Taher, Akar; Chehdi, Kacem; Cariou, Claude

    2013-10-01

    In this paper a new unsupervised nonparametric cooperative and adaptive hyperspectral image segmentation approach is presented. The hyperspectral images are partitioned band by band in parallel and intermediate classification results are evaluated and fused, to get the final segmentation result. Two unsupervised nonparametric segmentation methods are used in parallel cooperation, namely the Fuzzy C-means (FCM) method, and the Linde-Buzo-Gray (LBG) algorithm, to segment each band of the image. The originality of the approach relies firstly on its local adaptation to the type of regions in an image (textured, non-textured), and secondly on the introduction of several levels of evaluation and validation of intermediate segmentation results before obtaining the final partitioning of the image. For the management of similar or conflicting results issued from the two classification methods, we gradually introduced various assessment steps that exploit the information of each spectral band and its adjacent bands, and finally the information of all the spectral bands. In our approach, the detected textured and non-textured regions are treated separately from feature extraction step, up to the final classification results. This approach was first evaluated on a large number of monocomponent images constructed from the Brodatz album. Then it was evaluated on two real applications using a respectively multispectral image for Cedar trees detection in the region of Baabdat (Lebanon) and a hyperspectral image for identification of invasive and non invasive vegetation in the region of Cieza (Spain). A correct classification rate (CCR) for the first application is over 97% and for the second application the average correct classification rate (ACCR) is over 99%.

  12. Hyperspectral microscope imaging methods to classify gram-positive and gram-negative foodborne pathogenic bacteria

    Science.gov (United States)

    An acousto-optic tunable filter-based hyperspectral microscope imaging method has potential for identification of foodborne pathogenic bacteria from microcolony rapidly with a single cell level. We have successfully developed the method to acquire quality hyperspectral microscopic images from variou...

  13. Customized lifting multiwavelet packet information entropy for equipment condition identification

    International Nuclear Information System (INIS)

    Chen, Jinglong; Zi, Yanyang; He, Zhengjia; Chen, Xuefeng; Zuo, Ming J; Yuan, Jing

    2013-01-01

    Condition identification of mechanical equipment from vibration measurement data is significant to avoid economic loss caused by unscheduled breakdowns and catastrophic accidents. However, this task still faces challenges due to the complexity of equipment and the harsh environment. This paper provides a possibility for equipment condition identification by proposing a method called customized lifting multiwavelet packet information entropy. Benefiting from the properties of multi-resolution analysis and multiple wavelet basis functions, the multiwavelet method has advantages in characterizing non-stationary vibration signals. In order to realize the accurate detection and identification of the condition features, a customized lifting multiwavelet packet is constructed via a multiwavelet lifting scheme. Then the vibration signal from the mechanical equipment is processed by the customized lifting multiwavelet packet transform. The relative energy in each frequency band of the multiwavelet packet transform coefficients that equals a percentage of the whole signal energy is taken as the probability. The normalized information entropy is obtained based on the relative energy to describe the condition of a mechanical system. The proposed method is applied to the condition identification of a rolling mill and a demountable disk–drum aero-engine. The results support the feasibility of the proposed method in equipment condition identification. (paper)

  14. Dental caries imaging using hyperspectral stimulated Raman scattering microscopy

    Science.gov (United States)

    Wang, Zi; Zheng, Wei; Jian, Lin; Huang, Zhiwei

    2016-03-01

    We report the development of a polarization-resolved hyperspectral stimulated Raman scattering (SRS) imaging technique based on a picosecond (ps) laser-pumped optical parametric oscillator system for label-free imaging of dental caries. In our imaging system, hyperspectral SRS images (512×512 pixels) in both fingerprint region (800-1800 cm-1) and high-wavenumber region (2800-3600 cm-1) are acquired in minutes by scanning the wavelength of OPO output, which is a thousand times faster than conventional confocal micro Raman imaging. SRS spectra variations from normal enamel to caries obtained from the hyperspectral SRS images show the loss of phosphate and carbonate in the carious region. While polarization-resolved SRS images at 959 cm-1 demonstrate that the caries has higher depolarization ratio. Our results demonstrate that the polarization resolved-hyperspectral SRS imaging technique developed allows for rapid identification of the biochemical and structural changes of dental caries.

  15. Evaluating common de-identification heuristics for personal health information.

    Science.gov (United States)

    El Emam, Khaled; Jabbouri, Sam; Sams, Scott; Drouet, Youenn; Power, Michael

    2006-11-21

    With the growing adoption of electronic medical records, there are increasing demands for the use of this electronic clinical data in observational research. A frequent ethics board requirement for such secondary use of personal health information in observational research is that the data be de-identified. De-identification heuristics are provided in the Health Insurance Portability and Accountability Act Privacy Rule, funding agency and professional association privacy guidelines, and common practice. The aim of the study was to evaluate whether the re-identification risks due to record linkage are sufficiently low when following common de-identification heuristics and whether the risk is stable across sample sizes and data sets. Two methods were followed to construct identification data sets. Re-identification attacks were simulated on these. For each data set we varied the sample size down to 30 individuals, and for each sample size evaluated the risk of re-identification for all combinations of quasi-identifiers. The combinations of quasi-identifiers that were low risk more than 50% of the time were considered stable. The identification data sets we were able to construct were the list of all physicians and the list of all lawyers registered in Ontario, using 1% sampling fractions. The quasi-identifiers of region, gender, and year of birth were found to be low risk more than 50% of the time across both data sets. The combination of gender and region was also found to be low risk more than 50% of the time. We were not able to create an identification data set for the whole population. Existing Canadian federal and provincial privacy laws help explain why it is difficult to create an identification data set for the whole population. That such examples of high re-identification risk exist for mainstream professions makes a strong case for not disclosing the high-risk variables and their combinations identified here. For professional subpopulations with published

  16. 21 CFR 880.6300 - Implantable radiofrequency transponder system for patient identification and health information.

    Science.gov (United States)

    2010-04-01

    ... patient identification and health information. 880.6300 Section 880.6300 Food and Drugs FOOD AND DRUG... radiofrequency transponder system for patient identification and health information. (a) Identification. An implantable radiofrequency transponder system for patient identification and health information is a device...

  17. Hyperspectral imaging for non-contact analysis of forensic traces

    NARCIS (Netherlands)

    Edelman, G. J.; Gaston, E.; van Leeuwen, T. G.; Cullen, P. J.; Aalders, M. C. G.

    2012-01-01

    Hyperspectral imaging (HSI) integrates conventional imaging and spectroscopy, to obtain both spatial and spectral information from a specimen. This technique enables investigators to analyze the chemical composition of traces and simultaneously visualize their spatial distribution. HSI offers

  18. Sparse-Based Modeling of Hyperspectral Data

    DEFF Research Database (Denmark)

    Calvini, Rosalba; Ulrici, Alessandro; Amigo Rubio, Jose Manuel

    2016-01-01

    One of the main issues of hyperspectral imaging data is to unravel the relevant, yet overlapped, huge amount of information contained in the spatial and spectral dimensions. When dealing with the application of multivariate models in such high-dimensional data, sparsity can improve...

  19. Using hyperspectral imaging technology to identify diseased tomato leaves

    Science.gov (United States)

    Li, Cuiling; Wang, Xiu; Zhao, Xueguan; Meng, Zhijun; Zou, Wei

    2016-11-01

    In the process of tomato plants growth, due to the effect of plants genetic factors, poor environment factors, or disoperation of parasites, there will generate a series of unusual symptoms on tomato plants from physiology, organization structure and external form, as a result, they cannot grow normally, and further to influence the tomato yield and economic benefits. Hyperspectral image usually has high spectral resolution, not only contains spectral information, but also contains the image information, so this study adopted hyperspectral imaging technology to identify diseased tomato leaves, and developed a simple hyperspectral imaging system, including a halogen lamp light source unit, a hyperspectral image acquisition unit and a data processing unit. Spectrometer detection wavelength ranged from 400nm to 1000nm. After hyperspectral images of tomato leaves being captured, it was needed to calibrate hyperspectral images. This research used spectrum angle matching method and spectral red edge parameters discriminant method respectively to identify diseased tomato leaves. Using spectral red edge parameters discriminant method produced higher recognition accuracy, the accuracy was higher than 90%. Research results have shown that using hyperspectral imaging technology to identify diseased tomato leaves is feasible, and provides the discriminant basis for subsequent disease control of tomato plants.

  20. 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.

  1. Human Identification at a Distance Using Body Shape Information

    International Nuclear Information System (INIS)

    Rashid, N K A M; Yahya, M F; Shafie, A A

    2013-01-01

    Shape of human body is unique from one person to another. This paper presents an intelligent system approach for human identification at a distance using human body shape information. The body features used are the head, shoulder, and trunk. Image processing techniques for detection of these body features were developed in this work. Then, the features are recognized using fuzzy logic approach and used as inputs to a recognition system based on a multilayer neural network. The developed system is only applicable for recognizing a person from its frontal view and specifically constrained to male gender to simplify the algorithm. In this research, the accuracy for human identification using the proposed method is 77.5%. Thus, it is proved that human can be identified at a distance using body shape information

  2. Comparison of algorithms for blood stain detection applied to forensic hyperspectral imagery

    Science.gov (United States)

    Yang, Jie; Messinger, David W.; Mathew, Jobin J.; Dube, Roger R.

    2016-05-01

    Blood stains are among the most important types of evidence for forensic investigation. They contain valuable DNA information, and the pattern of the stains can suggest specifics about the nature of the violence that transpired at the scene. Early detection of blood stains is particularly important since the blood reacts physically and chemically with air and materials over time. Accurate identification of blood remnants, including regions that might have been intentionally cleaned, is an important aspect of forensic investigation. Hyperspectral imaging might be a potential method to detect blood stains because it is non-contact and provides substantial spectral information that can be used to identify regions in a scene with trace amounts of blood. The potential complexity of scenes in which such vast violence occurs can be high when the range of scene material types and conditions containing blood stains at a crime scene are considered. Some stains are hard to detect by the unaided eye, especially if a conscious effort to clean the scene has occurred (we refer to these as "latent" blood stains). In this paper we present the initial results of a study of the use of hyperspectral imaging algorithms for blood detection in complex scenes. We describe a hyperspectral imaging system which generates images covering 400 nm - 700 nm visible range with a spectral resolution of 10 nm. Three image sets of 31 wavelength bands were generated using this camera for a simulated indoor crime scene in which blood stains were placed on a T-shirt and walls. To detect blood stains in the scene, Principal Component Analysis (PCA), Subspace Reed Xiaoli Detection (SRXD), and Topological Anomaly Detection (TAD) algorithms were used. Comparison of the three hyperspectral image analysis techniques shows that TAD is most suitable for detecting blood stains and discovering latent blood stains.

  3. 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.

  4. An explorative chemometric approach applied to hyperspectral images for the study of illuminated manuscripts

    Science.gov (United States)

    Catelli, Emilio; Randeberg, Lise Lyngsnes; Alsberg, Bjørn Kåre; Gebremariam, Kidane Fanta; Bracci, Silvano

    2017-04-01

    Hyperspectral imaging (HSI) is a fast non-invasive imaging technology recently applied in the field of art conservation. With the help of chemometrics, important information about the spectral properties and spatial distribution of pigments can be extracted from HSI data. With the intent of expanding the applications of chemometrics to the interpretation of hyperspectral images of historical documents, and, at the same time, to study the colorants and their spatial distribution on ancient illuminated manuscripts, an explorative chemometric approach is here presented. The method makes use of chemometric tools for spectral de-noising (minimum noise fraction (MNF)) and image analysis (multivariate image analysis (MIA) and iterative key set factor analysis (IKSFA)/spectral angle mapper (SAM)) which have given an efficient separation, classification and mapping of colorants from visible-near-infrared (VNIR) hyperspectral images of an ancient illuminated fragment. The identification of colorants was achieved by extracting and interpreting the VNIR spectra as well as by using a portable X-ray fluorescence (XRF) spectrometer.

  5. PET and PVC Separation with Hyperspectral Imagery

    Science.gov (United States)

    Moroni, Monica; Mei, Alessandro; Leonardi, Alessandra; Lupo, Emanuela; La Marca, Floriana

    2015-01-01

    Traditional plants for plastic separation in homogeneous products employ material physical properties (for instance density). Due to the small intervals of variability of different polymer properties, the output quality may not be adequate. Sensing technologies based on hyperspectral imaging have been introduced in order to classify materials and to increase the quality of recycled products, which have to comply with specific standards determined by industrial applications. This paper presents the results of the characterization of two different plastic polymers—polyethylene terephthalate (PET) and polyvinyl chloride (PVC)—in different phases of their life cycle (primary raw materials, urban and urban-assimilated waste and secondary raw materials) to show the contribution of hyperspectral sensors in the field of material recycling. This is accomplished via near-infrared (900–1700 nm) reflectance spectra extracted from hyperspectral images acquired with a two-linear-spectrometer apparatus. Results have shown that a rapid and reliable identification of PET and PVC can be achieved by using a simple two near-infrared wavelength operator coupled to an analysis of reflectance spectra. This resulted in 100% classification accuracy. A sensor based on this identification method appears suitable and inexpensive to build and provides the necessary speed and performance required by the recycling industry. PMID:25609050

  6. PET and PVC separation with hyperspectral imagery.

    Science.gov (United States)

    Moroni, Monica; Mei, Alessandro; Leonardi, Alessandra; Lupo, Emanuela; Marca, Floriana La

    2015-01-20

    Traditional plants for plastic separation in homogeneous products employ material physical properties (for instance density). Due to the small intervals of variability of different polymer properties, the output quality may not be adequate. Sensing technologies based on hyperspectral imaging have been introduced in order to classify materials and to increase the quality of recycled products, which have to comply with specific standards determined by industrial applications. This paper presents the results of the characterization of two different plastic polymers--polyethylene terephthalate (PET) and polyvinyl chloride (PVC)--in different phases of their life cycle (primary raw materials, urban and urban-assimilated waste and secondary raw materials) to show the contribution of hyperspectral sensors in the field of material recycling. This is accomplished via near-infrared (900-1700 nm) reflectance spectra extracted from hyperspectral images acquired with a two-linear-spectrometer apparatus. Results have shown that a rapid and reliable identification of PET and PVC can be achieved by using a simple two near-infrared wavelength operator coupled to an analysis of reflectance spectra. This resulted in 100% classification accuracy. A sensor based on this identification method appears suitable and inexpensive to build and provides the necessary speed and performance required by the recycling industry.

  7. PET and PVC Separation with Hyperspectral Imagery

    Directory of Open Access Journals (Sweden)

    Monica Moroni

    2015-01-01

    Full Text Available Traditional plants for plastic separation in homogeneous products employ material physical properties (for instance density. Due to the small intervals of variability of different polymer properties, the output quality may not be adequate. Sensing technologies based on hyperspectral imaging have been introduced in order to classify materials and to increase the quality of recycled products, which have to comply with specific standards determined by industrial applications. This paper presents the results of the characterization of two different plastic polymers—polyethylene terephthalate (PET and polyvinyl chloride (PVC—in different phases of their life cycle (primary raw materials, urban and urban-assimilated waste and secondary raw materials to show the contribution of hyperspectral sensors in the field of material recycling. This is accomplished via near-infrared (900–1700 nm reflectance spectra extracted from hyperspectral images acquired with a two-linear-spectrometer apparatus. Results have shown that a rapid and reliable identification of PET and PVC can be achieved by using a simple two near-infrared wavelength operator coupled to an analysis of reflectance spectra. This resulted in 100% classification accuracy. A sensor based on this identification method appears suitable and inexpensive to build and provides the necessary speed and performance required by the recycling industry.

  8. Identification of the operating crew's information needs for accident management

    International Nuclear Information System (INIS)

    Nelson, W.R.; Hanson, D.J.; Ward, L.W.; Solberg, D.E.

    1988-01-01

    While it would be very difficult to predetermine all of the actions required to mitigate the consequences of every potential severe accident for a nuclear power plant, development of additional guidance and training could improve the likelihood that the operating crew would implement effective sever-accident management measures. The US Nuclear Regulatory Commission (NRC) is conducting an Accident Management Research Program that emphasizes the application of severe-accident research results to enhance the capability of the plant operating crew to effectively manage severe accidents. One element of this program includes identification of the information needed by the operating crew in severe-accident situations. This paper discusses a method developed for identifying these information needs and its application. The methodology has been applied to a generic reactor design representing a PWR with a large dry containment. The information needs were identified by systematically determining what information is needed to assess the health of the critical functions, identify the presence of challenges, select strategies, and assess the effectiveness of these strategies. This method allows the systematic identification of information needs for a broad range of severe-accident scenarios and can be validated by exercising the functional models for any specific event sequence

  9. The electronic identification, signature and security of information systems

    Directory of Open Access Journals (Sweden)

    Horovèák Pavel

    2002-12-01

    Full Text Available The contribution deals with the actual methods and technologies of information and communication systems security. It introduces the overview of electronic identification elements such as static password, dynamic password and single sign-on. Into this category belong also biometric and dynamic characteristics of verified person. Widespread is authentication based on identification elements ownership, such as various cards and authentication calculators. In the next part is specified a definition and characterization of electronic signature, its basic functions and certificate categories. Practical utilization of electronic signature consists of electronic signature acquirement, signature of outgoing email message, receiving of electronic signature and verification of electronic signature. The use of electronic signature is continuously growing and in connection with legislation development it exercises in all resorts.

  10. Comparison of hyperspectral transformation accuracies of multispectral Landsat TM, ETM+, OLI and EO-1 ALI images for detecting minerals in a geothermal prospect area

    Science.gov (United States)

    Hoang, Nguyen Tien; Koike, Katsuaki

    2018-03-01

    Hyperspectral remote sensing generally provides more detailed spectral information and greater accuracy than multispectral remote sensing for identification of surface materials. However, there have been no hyperspectral imagers that cover the entire Earth surface. This lack points to a need for producing pseudo-hyperspectral imagery by hyperspectral transformation from multispectral images. We have recently developed such a method, a Pseudo-Hyperspectral Image Transformation Algorithm (PHITA), which transforms Landsat 7 ETM+ images into pseudo-EO-1 Hyperion images using multiple linear regression models of ETM+ and Hyperion band reflectance data. This study extends the PHITA to transform TM, OLI, and EO-1 ALI sensor images into pseudo-Hyperion images. By choosing a part of the Fish Lake Valley geothermal prospect area in the western United States for study, the pseudo-Hyperion images produced from the TM, ETM+, OLI, and ALI images by PHITA were confirmed to be applicable to mineral mapping. Using a reference map as the truth, three main minerals (muscovite and chlorite mixture, opal, and calcite) were identified with high overall accuracies from the pseudo-images (> 95% and > 42% for excluding and including unclassified pixels, respectively). The highest accuracy was obtained from the ALI image, followed by ETM+, TM, and OLI images in descending order. The TM, OLI, and ALI images can be alternatives to ETM+ imagery for the hyperspectral transformation that aids the production of pseudo-Hyperion images for areas without high-quality ETM+ images because of scan line corrector failure, and for long-term global monitoring of land surfaces.

  11. Novel hyperspectral prediction method and apparatus

    Science.gov (United States)

    Kemeny, Gabor J.; Crothers, Natalie A.; Groth, Gard A.; Speck, Kathy A.; Marbach, Ralf

    2009-05-01

    Both the power and the challenge of hyperspectral technologies is the very large amount of data produced by spectral cameras. While off-line methodologies allow the collection of gigabytes of data, extended data analysis sessions are required to convert the data into useful information. In contrast, real-time monitoring, such as on-line process control, requires that compression of spectral data and analysis occur at a sustained full camera data rate. Efficient, high-speed practical methods for calibration and prediction are therefore sought to optimize the value of hyperspectral imaging. A novel method of matched filtering known as science based multivariate calibration (SBC) was developed for hyperspectral calibration. Classical (MLR) and inverse (PLS, PCR) methods are combined by spectroscopically measuring the spectral "signal" and by statistically estimating the spectral "noise." The accuracy of the inverse model is thus combined with the easy interpretability of the classical model. The SBC method is optimized for hyperspectral data in the Hyper-CalTM software used for the present work. The prediction algorithms can then be downloaded into a dedicated FPGA based High-Speed Prediction EngineTM module. Spectral pretreatments and calibration coefficients are stored on interchangeable SD memory cards, and predicted compositions are produced on a USB interface at real-time camera output rates. Applications include minerals, pharmaceuticals, food processing and remote sensing.

  12. Towards prediction of soil erodibility using hyperspectral information: a case study in a semi-arid region of Iran

    DEFF Research Database (Denmark)

    Ostovari, Yaser; Ghorbani-Dashtaki, Shoja; Bahrami, Hossein-Ali

    2018-01-01

    and develop Spectrotransfer Function (STF) using spectral reflectance information and Pedotransfer Function (PTF) to predict the K-factor, respectively. The derived STF was compared with developed PTF using measurable soil properties by Ostovari et al. (2016) and the Universal Soil Loss Equation (USLE......Soil Visible–Near-Infrared (Vis-NIR) spectroscopy has become an applicable and interesting technique to evaluate a number of soil properties because it is a fast, cost-effective, and non-invasive measurement technique. The main objective of the study to predict soil erodibility (K-factor), soil...... organic matter (SOM), and calcium carbonate equivalent (CaCO3) in calcareous soils of semi-arid regions located in south of Iran using spectral reflectance information in the Vis-NIR range. The K-factor was measured in 40 erosion plots under natural rainfall and the spectral reflectance of soil samples...

  13. Fukunaga-Koontz transform based dimensionality reduction for hyperspectral imagery

    Science.gov (United States)

    Ochilov, S.; Alam, M. S.; Bal, A.

    2006-05-01

    Fukunaga-Koontz Transform based technique offers some attractive properties for desired class oriented dimensionality reduction in hyperspectral imagery. In FKT, feature selection is performed by transforming into a new space where feature classes have complimentary eigenvectors. Dimensionality reduction technique based on these complimentary eigenvector analysis can be described under two classes, desired class and background clutter, such that each basis function best represent one class while carrying the least amount of information from the second class. By selecting a few eigenvectors which are most relevant to desired class, one can reduce the dimension of hyperspectral cube. Since the FKT based technique reduces data size, it provides significant advantages for near real time detection applications in hyperspectral imagery. Furthermore, the eigenvector selection approach significantly reduces computation burden via the dimensionality reduction processes. The performance of the proposed dimensionality reduction algorithm has been tested using real-world hyperspectral dataset.

  14. 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.

  15. Hyperspectral fundus imager

    Science.gov (United States)

    Truitt, Paul W.; Soliz, Peter; Meigs, Andrew D.; Otten, Leonard John, III

    2000-11-01

    A Fourier Transform hyperspectral imager was integrated onto a standard clinical fundus camera, a Zeiss FF3, for the purposes of spectrally characterizing normal anatomical and pathological features in the human ocular fundus. To develop this instrument an existing FDA approved retinal camera was selected to avoid the difficulties of obtaining new FDA approval. Because of this, several unusual design constraints were imposed on the optical configuration. Techniques to calibrate the sensor and to define where the hyperspectral pushbroom stripe was located on the retina were developed, including the manufacturing of an artificial eye with calibration features suitable for a spectral imager. In this implementation the Fourier transform hyperspectral imager can collect over a hundred 86 cm-1 spectrally resolved bands with 12 micro meter/pixel spatial resolution within the 1050 nm to 450 nm band. This equates to 2 nm to 8 nm spectral resolution depending on the wavelength. For retinal observations the band of interest tends to lie between 475 nm and 790 nm. The instrument has been in use over the last year successfully collecting hyperspectral images of the optic disc, retinal vessels, choroidal vessels, retinal backgrounds, and macula diabetic macular edema, and lesions of age-related macular degeneration.

  16. Letter-case information and the identification of brand names.

    Science.gov (United States)

    Perea, Manuel; Jiménez, María; Talero, Fernanda; López-Cañada, Soraya

    2015-02-01

    A central tenet of most current models of visual-word recognition is that lexical units are activated on the basis of case-invariant abstract letter representations. Here, we examined this assumption by using a unique type of words: brand names. The rationale of the experiments is that brand names are archetypically printed either in lowercase (e.g., adidas) or uppercase (e.g., IKEA). This allows us to present the brand names in their standard or non-standard case configuration (e.g., adidas, IKEA vs. ADIDAS, ikea, respectively). We conducted two experiments with a brand-decision task ('is it a brand name?'): a single-presentation experiment and a masked priming experiment. Results in the single-presentation experiment revealed faster identification times of brand names in their standard case configuration than in their non-standard case configuration (i.e., adidas faster than ADIDAS; IKEA faster than ikea). In the masked priming experiment, we found faster identification times of brand names when they were preceded by an identity prime that matched its standard case configuration than when it did not (i.e., faster response times to adidas-adidas than to ADIDAS-adidas). Taken together, the present findings strongly suggest that letter-case information forms part of a brand name's graphemic information, thus posing some limits to current models of visual-word recognition. © 2014 The British Psychological Society.

  17. Hyperspectral Image Analysis of Food Quality

    DEFF Research Database (Denmark)

    Arngren, Morten

    inspection.Near-infrared spectroscopy can address these issues by offering a fast and objectiveanalysis of the food quality. A natural extension to these single spectrumNIR systems is to include image information such that each pixel holds a NIRspectrum. This augmented image information offers several......Assessing the quality of food is a vital step in any food processing line to ensurethe best food quality and maximum profit for the farmer and food manufacturer.Traditional quality evaluation methods are often destructive and labourintensive procedures relying on wet chemistry or subjective human...... extensions to the analysis offood quality. This dissertation is concerned with hyperspectral image analysisused to assess the quality of single grain kernels. The focus is to highlight thebenefits and challenges of using hyperspectral imaging for food quality presentedin two research directions. Initially...

  18. Simulation of Hyperspectral Images

    Science.gov (United States)

    Richsmeier, Steven C.; Singer-Berk, Alexander; Bernstein, Lawrence S.

    2004-01-01

    A software package generates simulated hyperspectral imagery for use in validating algorithms that generate estimates of Earth-surface spectral reflectance from hyperspectral images acquired by airborne and spaceborne instruments. This software is based on a direct simulation Monte Carlo approach for modeling three-dimensional atmospheric radiative transport, as well as reflections from surfaces characterized by spatially inhomogeneous bidirectional reflectance distribution functions. In this approach, "ground truth" is accurately known through input specification of surface and atmospheric properties, and it is practical to consider wide variations of these properties. The software can treat both land and ocean surfaces, as well as the effects of finite clouds with surface shadowing. The spectral/spatial data cubes computed by use of this software can serve both as a substitute for, and a supplement to, field validation data.

  19. Identification of Chemical Toxicity Using Ontology Information of Chemicals

    Directory of Open Access Journals (Sweden)

    Zhanpeng Jiang

    2015-01-01

    Full Text Available With the advance of the combinatorial chemistry, a large number of synthetic compounds have surged. However, we have limited knowledge about them. On the other hand, the speed of designing new drugs is very slow. One of the key causes is the unacceptable toxicities of chemicals. If one can correctly identify the toxicity of chemicals, the unsuitable chemicals can be discarded in early stage, thereby accelerating the study of new drugs and reducing the R&D costs. In this study, a new prediction method was built for identification of chemical toxicities, which was based on ontology information of chemicals. By comparing to a previous method, our method is quite effective. We hope that the proposed method may give new insights to study chemical toxicity and other attributes of chemicals.

  20. Planetary Hyperspectral Imager (PHI)

    Science.gov (United States)

    Silvergate, Peter

    1996-01-01

    A hyperspectral imaging spectrometer was breadboarded. Key innovations were use of a sapphire prism and single InSb focal plane to cover the entire spectral range, and a novel slit optic and relay optics to reduce thermal background. Operation over a spectral range of 450 - 4950 nm (approximately 3.5 spectral octaves) was demonstrated. Thermal background reduction by a factor of 8 - 10 was also demonstrated.

  1. 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

  2. Chapter 08: Comments on, and additional information for, wood identification

    Science.gov (United States)

    Alex C. Wiedenhoeft

    2011-01-01

    This manual has described the theory of identification (Chapter 1), the botanical basis of wood structure (Chapter 2), the use of a hand lens (Chapter 3), how to use cutting tools to prepare wood for observation with a lens (Chapter 4), and the characters used in hand lens wood identification (Chapter 5) before leading you through an identification key (Chapter 6) and...

  3. High Throughput System for Plant Height and Hyperspectral Measurement

    Science.gov (United States)

    Zhao, H.; Xu, L.; Jiang, H.; Shi, S.; Chen, D.

    2018-04-01

    Hyperspectral and three-dimensional measurement can obtain the intrinsic physicochemical properties and external geometrical characteristics of objects, respectively. Currently, a variety of sensors are integrated into a system to collect spectral and morphological information in agriculture. However, previous experiments were usually performed with several commercial devices on a single platform. Inadequate registration and synchronization among instruments often resulted in mismatch between spectral and 3D information of the same target. And narrow field of view (FOV) extends the working hours in farms. Therefore, we propose a high throughput prototype that combines stereo vision and grating dispersion to simultaneously acquire hyperspectral and 3D information.

  4. HIGH THROUGHPUT SYSTEM FOR PLANT HEIGHT AND HYPERSPECTRAL MEASUREMENT

    Directory of Open Access Journals (Sweden)

    H. Zhao

    2018-04-01

    Full Text Available Hyperspectral and three-dimensional measurement can obtain the intrinsic physicochemical properties and external geometrical characteristics of objects, respectively. Currently, a variety of sensors are integrated into a system to collect spectral and morphological information in agriculture. However, previous experiments were usually performed with several commercial devices on a single platform. Inadequate registration and synchronization among instruments often resulted in mismatch between spectral and 3D information of the same target. And narrow field of view (FOV extends the working hours in farms. Therefore, we propose a high throughput prototype that combines stereo vision and grating dispersion to simultaneously acquire hyperspectral and 3D information.

  5. Parallel scan hyperspectral fluorescence imaging system and biomedical application for microarrays

    International Nuclear Information System (INIS)

    Liu Zhiyi; Ma Suihua; Liu Le; Guo Jihua; He Yonghong; Ji Yanhong

    2011-01-01

    Microarray research offers great potential for analysis of gene expression profile and leads to greatly improved experimental throughput. A number of instruments have been reported for microarray detection, such as chemiluminescence, surface plasmon resonance, and fluorescence markers. Fluorescence imaging is popular for the readout of microarrays. In this paper we develop a quasi-confocal, multichannel parallel scan hyperspectral fluorescence imaging system for microarray research. Hyperspectral imaging records the entire emission spectrum for every voxel within the imaged area in contrast to recording only fluorescence intensities of filter-based scanners. Coupled with data analysis, the recorded spectral information allows for quantitative identification of the contributions of multiple, spectrally overlapping fluorescent dyes and elimination of unwanted artifacts. The mechanism of quasi-confocal imaging provides a high signal-to-noise ratio, and parallel scan makes this approach a high throughput technique for microarray analysis. This system is improved with a specifically designed spectrometer which can offer a spectral resolution of 0.2 nm, and operates with spatial resolutions ranging from 2 to 30 μm . Finally, the application of the system is demonstrated by reading out microarrays for identification of bacteria.

  6. 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.

  7. Building of the system for managing and analyzing the hyperspectral data of drilling core

    International Nuclear Information System (INIS)

    Huang Yanju; Zhang Jielin; Wang Junhu

    2010-01-01

    Drilling core logging is very important for geological exploration, hyperspectral detection provides a totally new method for drilling core logging. To use and analyze the drilling core data more easily, and especially store them permanently, a system is built for analyzing and managing the hyperspectral data. The system provides a convenient way to sort the core data, and extract the spectral characteristics, which is the basis for the following mineral identification. (authors)

  8. Mapping Changes in a Recovering Mine Site with Hyperspectral Airborne HyMap Imagery (Sotiel, SW Spain

    Directory of Open Access Journals (Sweden)

    Jorge Buzzi

    2014-04-01

    Full Text Available Hyperspectral high spatial resolution HyMap data are used to map mine waste from massive sulfide ore deposits, mostly abandoned, on the Iberian Pyrite Belt (southwest Spain. Mine dams, mill tailings and mine dumps in variable states of pyrite oxidation are recognizable. The interpretation of hyperspectral remote sensing requires specific algorithms able to manage high dimensional data compared to multispectral data. The routine of image processing methods used to extract information from hyperspectral data to map geological features is explained, as well as the sequence of algorithms used to produce maps of the mine sites. The mineralogical identification capability of algorithms to produce maps based on archive spectral libraries is discussed. Trends of mineral growth differ spectrally over time according to the geological setting and the recovery state of the mine site. Subtle mineralogical changes are enhanced using the spectral response as indicators of pyrite oxidation intensity of the mine waste piles and pyrite mud tailings. The changes in the surface of the mill tailings deserve a detailed description, as the surfaces are inaccessible to direct observation. Such mineralogical changes respond faithfully to industrial activities or the influence of climate when undisturbed by human influence.

  9. Objective identification of informative wavelength regions in galaxy spectra

    Energy Technology Data Exchange (ETDEWEB)

    Yip, Ching-Wa; Szalay, Alexander S.; Budavári, Tamás; Wyse, Rosemary F. G. [Department of Physics and Astronomy, The Johns Hopkins University, 3701 San Martin Drive, Baltimore, MD 21218 (United States); Mahoney, Michael W. [Department of Mathematics, Stanford University, Stanford, CA 94305 (United States); Csabai, István; Dobos, Laszlo, E-mail: cwyip@pha.jhu.edu, E-mail: szalay@jhu.edu, E-mail: mmahoney@cs.stanford.edu [Department of Physics of Complex Systems, Eötvös Loránd University, H-1117 Budapest (Hungary)

    2014-05-01

    Understanding the diversity in spectra is the key to determining the physical parameters of galaxies. The optical spectra of galaxies are highly convoluted with continuum and lines that are potentially sensitive to different physical parameters. Defining the wavelength regions of interest is therefore an important question. In this work, we identify informative wavelength regions in a single-burst stellar population model using the CUR Matrix Decomposition. Simulating the Lick/IDS spectrograph configuration, we recover the widely used D {sub n}(4000), Hβ, and Hδ {sub A} to be most informative. Simulating the Sloan Digital Sky Survey spectrograph configuration with a wavelength range 3450-8350 Å and a model-limited spectral resolution of 3 Å, the most informative regions are: first region—the 4000 Å break and the Hδ line; second region—the Fe-like indices; third region—the Hβ line; and fourth region—the G band and the Hγ line. A principal component analysis on the first region shows that the first eigenspectrum tells primarily the stellar age, the second eigenspectrum is related to the age-metallicity degeneracy, and the third eigenspectrum shows an anti-correlation between the strengths of the Balmer and the Ca K and H absorptions. The regions can be used to determine the stellar age and metallicity in early-type galaxies that have solar abundance ratios, no dust, and a single-burst star formation history. The region identification method can be applied to any set of spectra of the user's interest, so that we eliminate the need for a common, fixed-resolution index system. We discuss future directions in extending the current analysis to late-type galaxies. ASCII formatted tables of the regional eigenspectra are available.

  10. Ore minerals textural characterization by hyperspectral imaging

    Science.gov (United States)

    Bonifazi, Giuseppe; Picone, Nicoletta; Serranti, Silvia

    2013-02-01

    The utilization of hyperspectral detection devices, for natural resources mapping/exploitation through remote sensing techniques, dates back to the early 1970s. From the first devices utilizing a one-dimensional profile spectrometer, HyperSpectral Imaging (HSI) devices have been developed. Thus, from specific-customized devices, originally developed by Governmental Agencies (e.g. NASA, specialized research labs, etc.), a lot of HSI based equipment are today available at commercial level. Parallel to this huge increase of hyperspectral systems development/manufacturing, addressed to airborne application, a strong increase also occurred in developing HSI based devices for "ground" utilization that is sensing units able to play inside a laboratory, a processing plant and/or in an open field. Thanks to this diffusion more and more applications have been developed and tested in this last years also in the materials sectors. Such an approach, when successful, is quite challenging being usually reliable, robust and characterised by lower costs if compared with those usually associated to commonly applied analytical off- and/or on-line analytical approaches. In this paper such an approach is presented with reference to ore minerals characterization. According to the different phases and stages of ore minerals and products characterization, and starting from the analyses of the detected hyperspectral firms, it is possible to derive useful information about mineral flow stream properties and their physical-chemical attributes. This last aspect can be utilized to define innovative process mineralogy strategies and to implement on-line procedures at processing level. The present study discusses the effects related to the adoption of different hardware configurations, the utilization of different logics to perform the analysis and the selection of different algorithms according to the different characterization, inspection and quality control actions to apply.

  11. High-resolution hyperspectral ground mapping for robotic vision

    Science.gov (United States)

    Neuhaus, Frank; Fuchs, Christian; Paulus, Dietrich

    2018-04-01

    Recently released hyperspectral cameras use large, mosaiced filter patterns to capture different ranges of the light's spectrum in each of the camera's pixels. Spectral information is sparse, as it is not fully available in each location. We propose an online method that avoids explicit demosaicing of camera images by fusing raw, unprocessed, hyperspectral camera frames inside an ego-centric ground surface map. It is represented as a multilayer heightmap data structure, whose geometry is estimated by combining a visual odometry system with either dense 3D reconstruction or 3D laser data. We use a publicly available dataset to show that our approach is capable of constructing an accurate hyperspectral representation of the surface surrounding the vehicle. We show that in many cases our approach increases spatial resolution over a demosaicing approach, while providing the same amount of spectral information.

  12. Dimensionality Reduction for Hyperspectral Data Based on Class-Aware Tensor Neighborhood Graph and Patch Alignment.

    Science.gov (United States)

    Gao, Yang; Wang, Xuesong; Cheng, Yuhu; Wang, Z Jane

    2015-08-01

    To take full advantage of hyperspectral information, to avoid data redundancy and to address the curse of dimensionality concern, dimensionality reduction (DR) becomes particularly important to analyze hyperspectral data. Exploring the tensor characteristic of hyperspectral data, a DR algorithm based on class-aware tensor neighborhood graph and patch alignment is proposed here. First, hyperspectral data are represented in the tensor form through a window field to keep the spatial information of each pixel. Second, using a tensor distance criterion, a class-aware tensor neighborhood graph containing discriminating information is obtained. In the third step, employing the patch alignment framework extended to the tensor space, we can obtain global optimal spectral-spatial information. Finally, the solution of the tensor subspace is calculated using an iterative method and low-dimensional projection matrixes for hyperspectral data are obtained accordingly. The proposed method effectively explores the spectral and spatial information in hyperspectral data simultaneously. Experimental results on 3 real hyperspectral datasets show that, compared with some popular vector- and tensor-based DR algorithms, the proposed method can yield better performance with less tensor training samples required.

  13. 33 CFR 181.27 - Information displayed near hull identification number.

    Science.gov (United States)

    2010-07-01

    ... 33 Navigation and Navigable Waters 2 2010-07-01 2010-07-01 false Information displayed near hull... displayed near hull identification number. With the exception of the characters “US-”, which constitute the... the 12-character hull identification number (HIN), that information must be separated from the HIN by...

  14. Secure and Efficient Transmission of Hyperspectral Images for Geosciences Applications

    Science.gov (United States)

    Carpentieri, Bruno; Pizzolante, Raffaele

    2017-12-01

    Hyperspectral images are acquired through air-borne or space-borne special cameras (sensors) that collect information coming from the electromagnetic spectrum of the observed terrains. Hyperspectral remote sensing and hyperspectral images are used for a wide range of purposes: originally, they were developed for mining applications and for geology because of the capability of this kind of images to correctly identify various types of underground minerals by analysing the reflected spectrums, but their usage has spread in other application fields, such as ecology, military and surveillance, historical research and even archaeology. The large amount of data obtained by the hyperspectral sensors, the fact that these images are acquired at a high cost by air-borne sensors and that they are generally transmitted to a base, makes it necessary to provide an efficient and secure transmission protocol. In this paper, we propose a novel framework that allows secure and efficient transmission of hyperspectral images, by combining a reversible invisible watermarking scheme, used in conjunction with digital signature techniques, and a state-of-art predictive-based lossless compression algorithm.

  15. Hyperspectral imaging and its applications

    Science.gov (United States)

    Serranti, S.; Bonifazi, G.

    2016-04-01

    Hyperspectral imaging (HSI) is an emerging technique that combines the imaging properties of a digital camera with the spectroscopic properties of a spectrometer able to detect the spectral attributes of each pixel in an image. For these characteristics, HSI allows to qualitatively and quantitatively evaluate the effects of the interactions of light with organic and/or inorganic materials. The results of this interaction are usually displayed as a spectral signature characterized by a sequence of energy values, in a pre-defined wavelength interval, for each of the investigated/collected wavelength. Following this approach, it is thus possible to collect, in a fast and reliable way, spectral information that are strictly linked to chemical-physical characteristics of the investigated materials and/or products. Considering that in an hyperspectral image the spectrum of each pixel can be analyzed, HSI can be considered as one of the best nondestructive technology allowing to perform the most accurate and detailed information extraction. HSI can be applied in different wavelength fields, the most common are the visible (VIS: 400-700 nm), the near infrared (NIR: 1000-1700 nm) and the short wave infrared (SWIR: 1000-2500 nm). It can be applied for inspections from micro- to macro-scale, up to remote sensing. HSI produces a large amount of information due to the great number of continuous collected spectral bands. Such an approach, when successful, is quite challenging being usually reliable, robust and characterized by lower costs, if compared with those usually associated to commonly applied analytical off-line and/or on-line analytical approaches. More and more applications have been thus developed and tested, in these last years, especially in food inspection, with a large range of investigated products, such as fruits and vegetables, meat, fish, eggs and cereals, but also in medicine and pharmaceutical sector, in cultural heritage, in material characterization and in

  16. Sparse Representations of Hyperspectral Images

    KAUST Repository

    Swanson, Robin J.

    2015-01-01

    Hyperspectral image data has long been an important tool for many areas of sci- ence. The addition of spectral data yields significant improvements in areas such as object and image classification, chemical and mineral composition detection, and astronomy. Traditional capture methods for hyperspectral data often require each wavelength to be captured individually, or by sacrificing spatial resolution. Recently there have been significant improvements in snapshot hyperspectral captures using, in particular, compressed sensing methods. As we move to a compressed sensing image formation model the need for strong image priors to shape our reconstruction, as well as sparse basis become more important. Here we compare several several methods for representing hyperspectral images including learned three dimensional dictionaries, sparse convolutional coding, and decomposable nonlocal tensor dictionaries. Addi- tionally, we further explore their parameter space to identify which parameters provide the most faithful and sparse representations.

  17. Sparse Representations of Hyperspectral Images

    KAUST Repository

    Swanson, Robin J.

    2015-11-23

    Hyperspectral image data has long been an important tool for many areas of sci- ence. The addition of spectral data yields significant improvements in areas such as object and image classification, chemical and mineral composition detection, and astronomy. Traditional capture methods for hyperspectral data often require each wavelength to be captured individually, or by sacrificing spatial resolution. Recently there have been significant improvements in snapshot hyperspectral captures using, in particular, compressed sensing methods. As we move to a compressed sensing image formation model the need for strong image priors to shape our reconstruction, as well as sparse basis become more important. Here we compare several several methods for representing hyperspectral images including learned three dimensional dictionaries, sparse convolutional coding, and decomposable nonlocal tensor dictionaries. Addi- tionally, we further explore their parameter space to identify which parameters provide the most faithful and sparse representations.

  18. Hyperspectral image analysis. A tutorial

    DEFF Research Database (Denmark)

    Amigo Rubio, Jose Manuel; Babamoradi, Hamid; Elcoroaristizabal Martin, Saioa

    2015-01-01

    This tutorial aims at providing guidelines and practical tools to assist with the analysis of hyperspectral images. Topics like hyperspectral image acquisition, image pre-processing, multivariate exploratory analysis, hyperspectral image resolution, classification and final digital image processi...... to differentiate between several types of plastics by using Near infrared hyperspectral imaging and Partial Least Squares - Discriminant Analysis. Thus, the reader is guided through every single step and oriented in order to adapt those strategies to the user's case....... will be exposed, and some guidelines given and discussed. Due to the broad character of current applications and the vast number of multivariate methods available, this paper has focused on an industrial chemical framework to explain, in a step-wise manner, how to develop a classification methodology...

  19. Image Segmentation of Hyperspectral Imagery

    National Research Council Canada - National Science Library

    Wellman, Mark

    2003-01-01

    .... Army tactical applications. An important tactical application of infrared (IR) hyperspectral imagery is the detection of low-contrast targets, including those targets that may employ camouflage, concealment, and deception (CCD) techniques 1, 2...

  20. Underwater Hyperspectral Imaging (UHI) for Assessing the Coverage of Drill Cuttings on Benthic Habitats

    Science.gov (United States)

    Erdal, I.; Sandvik Aas, L. M.; Cochrane, S.; Ekehaug, S.; Hansen, I. M.

    2016-02-01

    Larger-scale mapping of seabed areas requires improved methods in order to obtain effective and sound marine management. The state of the art for visual surveys today involves video transects, which is a proven, yet time consuming and subjective method. Underwater hyperspectral imaging (UHI) utilizes high color sensitive information in the visible light reflected from objects on the seafloor to automatically identify seabed organisms and other objects of interest (OOI). A spectral library containing optical fingerprints of a range of OOI's are used in the classification. The UHI is a push-broom hyperspectral camera utilizing a state of the art CMOS sensor ensuring high sensitivity and low noise levels. Dedicated lamps illuminate the imaging area of the seafloor. Specialized software is used both for processing raw data and for geo-localization and OOI identification. The processed hyperspectral image are used as a reference when extracting new spectral data for OOI's to the spectral library. By using the spectral library in classification algorithms, large sea floor areas can automatically be classified. Recent advantages in UHI classification includes mapping of areas affected by drill cuttings. Tools for automated classification of seabed that have a different bottom composition than adjacent baseline areas are under development. Tests have been applied to a transect in gradient from the drilling hole to baseline seabed. Some areas along the transect were identified as different compared to baseline seabed. The finding was supported by results from traditional seabed mapping methods. We propose that this can be a useful tool for tomorrows environmental mapping and monitoring of drill sites.

  1. 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)

  2. Hyperspectral image analysis. A tutorial

    International Nuclear Information System (INIS)

    Amigo, José Manuel; Babamoradi, Hamid; Elcoroaristizabal, Saioa

    2015-01-01

    This tutorial aims at providing guidelines and practical tools to assist with the analysis of hyperspectral images. Topics like hyperspectral image acquisition, image pre-processing, multivariate exploratory analysis, hyperspectral image resolution, classification and final digital image processing will be exposed, and some guidelines given and discussed. Due to the broad character of current applications and the vast number of multivariate methods available, this paper has focused on an industrial chemical framework to explain, in a step-wise manner, how to develop a classification methodology to differentiate between several types of plastics by using Near infrared hyperspectral imaging and Partial Least Squares – Discriminant Analysis. Thus, the reader is guided through every single step and oriented in order to adapt those strategies to the user's case. - Highlights: • Comprehensive tutorial of Hyperspectral Image analysis. • Hierarchical discrimination of six classes of plastics containing flame retardant. • Step by step guidelines to perform class-modeling on hyperspectral images. • Fusion of multivariate data analysis and digital image processing methods. • Promising methodology for real-time detection of plastics containing flame retardant.

  3. Hyperspectral remote sensing techniques for early detection of plant diseases

    Science.gov (United States)

    Krezhova, Dora; Maneva, Svetla; Zdravev, Tomas

    Hyperspectral remote sensing is an emerging, multidisciplinary field with diverse applications in Earth observation. Nowadays spectral remote sensing techniques allow presymptomatic monitoring of changes in the physiological state of plants with high spectral resolution. Hyperspectral leaf reflectance and chlorophyll fluorescence proved to be highly suitable for identification of growth anomalies of cultural plants that result from the environmental changes and different stress factors. Hyperspectral technologies can find place in many scientific areas, as well as for monitoring of plants status and functioning to help in making timely management decisions. This research aimed to detect a presence of viral infection in young pepper plants (Capsicum annuum L.) caused by Cucumber Mosaic Virus (CMV) by using hyperspectral reflectance and fluorescence data and to assess the effect of some growth regulators on the development of the disease. In Bulgaria CMV is one of the widest spread pathogens, causing the biggest economical losses in crop vegetable production. Leaf spectral reflectance and fluorescence data were collected by a portable fibre-optics spectrometer in the spectral ranges 450÷850 nm and 600-900 nm. Greenhouse experiment with pepper plants of two cultivars, Sivria (sensitive to CMV) and Ostrion (resistant to CMV) were used. The plants were divided into six groups. The first group consisted of healthy (control) plants. At growth stage 4-6 expanded leaf, the second group was inoculated with CMV. The other four groups were treated with growth regulators: Spermine, MEIA (beta-monomethyl ester of itaconic acid), BTH (benzo(1,2,3)thiadiazole-7-carbothioic acid-S-methyl ester) and Phytoxin. On the next day, the pepper plants of these four groups were inoculated with CMV. The viral concentrations in the plants were determined by the serological method DAS-ELISA. Statistical, first derivative and cluster analysis were applied and several vegetation indices were

  4. Objective Color Classification of Ecstasy Tablets by Hyperspectral Imaging

    NARCIS (Netherlands)

    Edelman, Gerda; Lopatka, Martin; Aalders, Maurice

    2013-01-01

    The general procedure followed in the examination of ecstasy tablets for profiling purposes includes a color description, which depends highly on the observers' perception. This study aims to provide objective quantitative color information using visible hyperspectral imaging. Both self-manufactured

  5. Information-theoretical analysis of private content identification

    NARCIS (Netherlands)

    Voloshynovskiy, S.; Koval, O.; Beekhof, F.; Farhadzadeh, F.; Holotyak, T.

    2010-01-01

    In recent years, content identification based on digital fingerprinting attracts a lot of attention in different emerging applications. At the same time, the theoretical analysis of digital fingerprinting systems for finite length case remains an open issue. Additionally, privacy leaks caused by

  6. Hyperspectral analysis of clay minerals

    Science.gov (United States)

    Janaki Rama Suresh, G.; Sreenivas, K.; Sivasamy, R.

    2014-11-01

    A study was carried out by collecting soil samples from parts of Gwalior and Shivpuri district, Madhya Pradesh in order to assess the dominant clay mineral of these soils using hyperspectral data, as 0.4 to 2.5 μm spectral range provides abundant and unique information about many important earth-surface minerals. Understanding the spectral response along with the soil chemical properties can provide important clues for retrieval of mineralogical soil properties. The soil samples were collected based on stratified random sampling approach and dominant clay minerals were identified through XRD analysis. The absorption feature parameters like depth, width, area and asymmetry of the absorption peaks were derived from spectral profile of soil samples through DISPEC tool. The derived absorption feature parameters were used as inputs for modelling the dominant soil clay mineral present in the unknown samples using Random forest approach which resulted in kappa accuracy of 0.795. Besides, an attempt was made to classify the Hyperion data using Spectral Angle Mapper (SAM) algorithm with an overall accuracy of 68.43 %. Results showed that kaolinite was the dominant mineral present in the soils followed by montmorillonite in the study area.

  7. Hyperspectral imaging applied to complex particulate solids systems

    Science.gov (United States)

    Bonifazi, Giuseppe; Serranti, Silvia

    2008-04-01

    HyperSpectral Imaging (HSI) is based on the utilization of an integrated hardware and software (HW&SW) platform embedding conventional imaging and spectroscopy to attain both spatial and spectral information from an object. Although HSI was originally developed for remote sensing, it has recently emerged as a powerful process analytical tool, for non-destructive analysis, in many research and industrial sectors. The possibility to apply on-line HSI based techniques in order to identify and quantify specific particulate solid systems characteristics is presented and critically evaluated. The originally developed HSI based logics can be profitably applied in order to develop fast, reliable and lowcost strategies for: i) quality control of particulate products that must comply with specific chemical, physical and biological constraints, ii) performance evaluation of manufacturing strategies related to processing chains and/or realtime tuning of operative variables and iii) classification-sorting actions addressed to recognize and separate different particulate solid products. Case studies, related to recent advances in the application of HSI to different industrial sectors, as agriculture, food, pharmaceuticals, solid waste handling and recycling, etc. and addressed to specific goals as contaminant detection, defect identification, constituent analysis and quality evaluation are described, according to authors' originally developed application.

  8. Hyperspectral band selection and classification of Hyperion image of Bhitarkanika mangrove ecosystem, eastern India

    Science.gov (United States)

    Ashokkumar, L.; Shanmugam, S.

    2014-10-01

    Tropical mangrove forests along the coast evolve dynamically due to constant changes in the natural ecosystem and ecological cycle. Remote sensing has paved the way for periodic monitoring and conservation of such floristic resources, compared to labour intensive in-situ observations. With the laboratory quality image spectra obtained from hyperspectral image data, species level discrimination in habitats and ecosystems is attainable. One of the essential steps before classification of hyperspectral image data is band selection. It is important to eliminate the redundant bands to mitigate the problems of Hughes effect that are likely to affect further image analysis and classification accuracy. This paper presents a methodology for the selection of appropriate hyperspectral bands from the EO-1 Hyperion image for the identification and mapping of mangrove species and coastal landcover types in the Bhitarkanika coastal forest region, eastern India. Band selection procedure follows class based elimination procedure and the separability of the classes are tested in the band selection process. Individual bands are de-correlated and redundant bands are removed from the bandwise correlation matrix. The percent contribution of class variance in each band is analysed from the factors of PCA component ranking. Spectral bands are selected from the wavelength groups and statistically tested. Further, the band selection procedure is compared with similar techniques (Band Index and Mutual information) for validation. The number of bands in the Hyperion image was reduced from 196 to 88 by the Factor-based ranking approach. Classification was performed by Support Vector Machine approach. It is observed that the proposed Factor-based ranking approach performed well in discriminating the mangrove species and other landcover units compared to the other statistical approaches. The predominant mangrove species Heritiera fomes, Excoecaria agallocha and Cynometra ramiflora are spectral

  9. Informational dissimilarity and organizational citizenship behavior : The role of intrateam interdependence and team identification

    NARCIS (Netherlands)

    Van der Vegt, GS; Van de Vliert, E; Oosterhof, A

    2003-01-01

    A questionnaire study of 129 members of 20 multidisciplinary project teams examined the relationship between informational dissimilarity and both team identification and organizational citizenship behavior (OCB) for individuals working under different interdependence configurations. Results revealed

  10. Hyperspectral Alteration Information from Drill Cores and Deep Uranium Exploration in the Baiyanghe Uranium Deposit in the Xuemisitan Area, Xinjiang, China

    Directory of Open Access Journals (Sweden)

    Qing-Jun Xu

    2017-05-01

    Full Text Available The Baiyanghe uranium deposit is a currently important medium-sized deposit in the Xuemisitan area, Xinjiang. The hydrothermal alteration in this deposit is closely related to the uranium mineralization of the deposit. In this study, hyperspectral data are collected from drill cores in the Baiyanghe uranium deposit using a FieldSpec4 visible-shortwave infrared spectrometer to study the hydrothermal alteration. The results reveal that the altered mineral assemblages have obvious zonation characteristics: (1 the upper section comprises long-wavelength illite and minor hematite and montmorillonite; (2 the middle section contains three types of illite (long-, medium- and short-wavelength illite and hematite; and (3 the lower section includes short-wavelength illite, chlorite and carbonate. Additionally, the variety in the characteristic absorption-peak wavelength of illite at 2200 nm gradually shifts to shorter wavelength and ranges between 2195 nm and 2220 nm with increasing depth, while the SWIR-IC (short-wavelength infrared illite crystallinity, a dimensionless quantity of the drill holes gradually increases from 0.2 to 2.1. These patterns reflect the hydrothermal fluid activity in the deposit, which features relatively high-temperature, high-pressure hydrothermal fluid in the deeper section and low-temperature, low-pressure hydrothermal fluid in the shallower section. Additionally, the uranium mineralization is located near the fracture zone, which represents the center of hydrothermal fluid activity or mineralization. This area has abundant alteration minerals, and the minerals illite (short- and medium-wavelength, hematite and fluorite can be used as uranium-prospecting indicators for uranium exploration in the deeper sections of the Baiyanghe uranium deposit.

  11. Hyperspectral remote sensing detection of petroleum hydrocarbons in mixtures with mineral substrates: Implications for onshore exploration and monitoring

    Science.gov (United States)

    Scafutto, Rebecca Del'Papa Moreira; de Souza Filho, Carlos Roberto; de Oliveira, Wilson José

    2017-06-01

    Remote detection and mapping of hydrocarbons (PHCs) in situ in continental areas is still an operational challenge due to the small scale of the occurrences and the mix of spectral signatures of PHCs and mineral substrates in imagery pixels. Despite the increasing development of new technologies, the use of hyperspectral remote sensing data as a complementary tool for both oil exploration and environmental monitoring is not standard in the oil industry, despite its potential. The high spectral resolution of hyperspectral images allows the direct identification of PHCs on the surface and provides valuable information regarding the location and spread of oil spills that can assist in containment and cleanup operations. Combining the spectral information with statistical techniques also offers the potential to improve exploration programs focused on the discovery of new exploration fields through the qualitative and quantitative characterization of oil occurrences in onshore areas. In this scenario, the aim of this work was to develop methods that can assist the detection of continental areas affected by natural oil seeps or leaks (crude oils and fuels). A field experiment was designed by impregnating several mineral substrates with crude oils and fuels in varying concentrations. Simultaneous measurements of soil-PHC combinations were taken using both a hand-held spectrometer and an airborne hyperspectral imager. Classification algorithms were used to directly map the PHCs on the surface. Spectral information was submitted to a PLS (partial least square regression) to create a prediction model for the estimation of the concentrations of PHCs in soils. The developed model was able to detect three impregnation levels (low, intermediate, high), predicting values close to the concentrations used in the experiment. Given the quality of the results in controlled experiments, the methods developed in this research show the potential to support the oil industry in the

  12. The Problem of Informational Object Identification in Case of the Considerable Quantity of Identifying Features

    Directory of Open Access Journals (Sweden)

    S. D. Kulik

    2010-03-01

    Full Text Available The modification of the algorithm of identification of the informational object, used for identification of the hand-written texts performer in an automated workplace of the forensic expert, is presented. As modification, it is offered to use a method of association rules discovery for definition of statistically dependent sets of feature of hand-written capital letters of the Russian language. The algorithm is approved on set of 691 samples of hand-written documents for which about 2000 identifying feature are defined. The modification of the identification algorithm allows to lower level of errors and to raise quality of accepted decisions for information security.

  13. 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

  14. 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.

  15. 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.

  16. Spatial-Spectral Approaches to Edge Detection in Hyperspectral Remote Sensing

    Science.gov (United States)

    Cox, Cary M.

    This dissertation advances geoinformation science at the intersection of hyperspectral remote sensing and edge detection methods. A relatively new phenomenology among its remote sensing peers, hyperspectral imagery (HSI) comprises only about 7% of all remote sensing research - there are five times as many radar-focused peer reviewed journal articles than hyperspectral-focused peer reviewed journal articles. Similarly, edge detection studies comprise only about 8% of image processing research, most of which is dedicated to image processing techniques most closely associated with end results, such as image classification and feature extraction. Given the centrality of edge detection to mapping, that most important of geographic functions, improving the collective understanding of hyperspectral imagery edge detection methods constitutes a research objective aligned to the heart of geoinformation sciences. Consequently, this dissertation endeavors to narrow the HSI edge detection research gap by advancing three HSI edge detection methods designed to leverage HSI's unique chemical identification capabilities in pursuit of generating accurate, high-quality edge planes. The Di Zenzo-based gradient edge detection algorithm, an innovative version of the Resmini HySPADE edge detection algorithm and a level set-based edge detection algorithm are tested against 15 traditional and non-traditional HSI datasets spanning a range of HSI data configurations, spectral resolutions, spatial resolutions, bandpasses and applications. This study empirically measures algorithm performance against Dr. John Canny's six criteria for a good edge operator: false positives, false negatives, localization, single-point response, robustness to noise and unbroken edges. The end state is a suite of spatial-spectral edge detection algorithms that produce satisfactory edge results against a range of hyperspectral data types applicable to a diverse set of earth remote sensing applications. This work

  17. INFLUENCE OF THE VIEWING GEOMETRY WITHIN HYPERSPECTRAL IMAGES RETRIEVED FROM UAV SNAPSHOT CAMERAS

    OpenAIRE

    Aasen, Helge

    2016-01-01

    Hyperspectral data has great potential for vegetation parameter retrieval. However, due to angular effects resulting from different sun-surface-sensor geometries, objects might appear differently depending on the position of an object within the field of view of a sensor. Recently, lightweight snapshot cameras have been introduced, which capture hyperspectral information in two spatial and one spectral dimension and can be mounted on unmanned aerial vehicles. This study investigates th...

  18. Nitrogen concentration estimation with hyperspectral LiDAR

    Directory of Open Access Journals (Sweden)

    O. Nevalainen

    2013-10-01

    Full Text Available Agricultural lands have strong impact on global carbon dynamics and nitrogen availability. Monitoring changes in agricultural lands require more efficient and accurate methods. The first prototype of a full waveform hyperspectral Light Detection and Ranging (LiDAR instrument has been developed at the Finnish Geodetic Institute (FGI. The instrument efficiently combines the benefits of passive and active remote sensing sensors. It is able to produce 3D point clouds with spectral information included for every point which offers great potential in the field of remote sensing of environment. This study investigates the performance of the hyperspectral LiDAR instrument in nitrogen estimation. The investigation was conducted by finding vegetation indices sensitive to nitrogen concentration using hyperspectral LiDAR data and validating their performance in nitrogen estimation. The nitrogen estimation was performed by calculating 28 published vegetation indices to ten oat samples grown in different fertilization conditions. Reference data was acquired by laboratory nitrogen concentration analysis. The performance of the indices in nitrogen estimation was determined by linear regression and leave-one-out cross-validation. The results indicate that the hyperspectral LiDAR instrument holds a good capability to estimate plant biochemical parameters such as nitrogen concentration. The instrument holds much potential in various environmental applications and provides a significant improvement to the remote sensing of environment.

  19. Hyperspectral Imaging Sensors and the Marine Coastal Zone

    Science.gov (United States)

    Richardson, Laurie L.

    2000-01-01

    Hyperspectral imaging sensors greatly expand the potential of remote sensing to assess, map, and monitor marine coastal zones. Each pixel in a hyperspectral image contains an entire spectrum of information. As a result, hyperspectral image data can be processed in two very different ways: by image classification techniques, to produce mapped outputs of features in the image on a regional scale; and by use of spectral analysis of the spectral data embedded within each pixel of the image. The latter is particularly useful in marine coastal zones because of the spectral complexity of suspended as well as benthic features found in these environments. Spectral-based analysis of hyperspectral (AVIRIS) imagery was carried out to investigate a marine coastal zone of South Florida, USA. Florida Bay is a phytoplankton-rich estuary characterized by taxonomically distinct phytoplankton assemblages and extensive seagrass beds. End-member spectra were extracted from AVIRIS image data corresponding to ground-truth sample stations and well-known field sites. Spectral libraries were constructed from the AVIRIS end-member spectra and used to classify images using the Spectral Angle Mapper (SAM) algorithm, a spectral-based approach that compares the spectrum, in each pixel of an image with each spectrum in a spectral library. Using this approach different phytoplankton assemblages containing diatoms, cyanobacteria, and green microalgae, as well as benthic community (seagrasses), were mapped.

  20. Enabling Searches on Wavelengths in a Hyperspectral Indices Database

    Science.gov (United States)

    Piñuela, F.; Cerra, D.; Müller, R.

    2017-10-01

    Spectral indices derived from hyperspectral reflectance measurements are powerful tools to estimate physical parameters in a non-destructive and precise way for several fields of applications, among others vegetation health analysis, coastal and deep water constituents, geology, and atmosphere composition. In the last years, several micro-hyperspectral sensors have appeared, with both full-frame and push-broom acquisition technologies, while in the near future several hyperspectral spaceborne missions are planned to be launched. This is fostering the use of hyperspectral data in basic and applied research causing a large number of spectral indices to be defined and used in various applications. Ad hoc search engines are therefore needed to retrieve the most appropriate indices for a given application. In traditional systems, query input parameters are limited to alphanumeric strings, while characteristics such as spectral range/ bandwidth are not used in any existing search engine. Such information would be relevant, as it enables an inverse type of search: given the spectral capabilities of a given sensor or a specific spectral band, find all indices which can be derived from it. This paper describes a tool which enables a search as described above, by using the central wavelength or spectral range used by a given index as a search parameter. This offers the ability to manage numeric wavelength ranges in order to select indices which work at best in a given set of wavelengths or wavelength ranges.

  1. Analysis of hyperspectral fluorescence images for poultry skin tumor inspection

    Science.gov (United States)

    Kong, Seong G.; Chen, Yud-Ren; Kim, Intaek; Kim, Moon S.

    2004-02-01

    We present a hyperspectral fluorescence imaging system with a fuzzy inference scheme for detecting skin tumors on poultry carcasses. Hyperspectral images reveal spatial and spectral information useful for finding pathological lesions or contaminants on agricultural products. Skin tumors are not obvious because the visual signature appears as a shape distortion rather than a discoloration. Fluorescence imaging allows the visualization of poultry skin tumors more easily than reflectance. The hyperspectral image samples obtained for this poultry tumor inspection contain 65 spectral bands of fluorescence in the visible region of the spectrum at wavelengths ranging from 425 to 711 nm. The large amount of hyperspectral image data is compressed by use of a discrete wavelet transform in the spatial domain. Principal-component analysis provides an effective compressed representation of the spectral signal of each pixel in the spectral domain. A small number of significant features are extracted from two major spectral peaks of relative fluorescence intensity that have been identified as meaningful spectral bands for detecting tumors. A fuzzy inference scheme that uses a small number of fuzzy rules and Gaussian membership functions successfully detects skin tumors on poultry carcasses. Spatial-filtering techniques are used to significantly reduce false positives.

  2. Recent Advances in Techniques for Hyperspectral Image Processing

    Science.gov (United States)

    Plaza, Antonio; Benediktsson, Jon Atli; Boardman, Joseph W.; Brazile, Jason; Bruzzone, Lorenzo; Camps-Valls, Gustavo; Chanussot, Jocelyn; Fauvel, Mathieu; Gamba, Paolo; Gualtieri, Anthony; hide

    2009-01-01

    Imaging spectroscopy, also known as hyperspectral imaging, has been transformed in less than 30 years from being a sparse research tool into a commodity product available to a broad user community. Currently, there is a need for standardized data processing techniques able to take into account the special properties of hyperspectral data. In this paper, we provide a seminal view on recent advances in techniques for hyperspectral image processing. Our main focus is on the design of techniques able to deal with the highdimensional nature of the data, and to integrate the spatial and spectral information. Performance of the discussed techniques is evaluated in different analysis scenarios. To satisfy time-critical constraints in specific applications, we also develop efficient parallel implementations of some of the discussed algorithms. Combined, these parts provide an excellent snapshot of the state-of-the-art in those areas, and offer a thoughtful perspective on future potentials and emerging challenges in the design of robust hyperspectral imaging algorithms

  3. [Prediction of Encapsulation Temperatures of Copolymer Films in Photovoltaic Cells Using Hyperspectral Imaging Techniques and Chemometrics].

    Science.gov (United States)

    Lin, Ping; Chen, Yong-ming; Yao, Zhi-lei

    2015-11-01

    A novel method of combination of the chemometrics and the hyperspectral imaging techniques was presented to detect the temperatures of Ethylene-Vinyl Acetate copolymer (EVA) films in photovoltaic cells during the thermal encapsulation process. Four varieties of the EVA films which had been heated at the temperatures of 128, 132, 142 and 148 °C during the photovoltaic cells production process were used for investigation in this paper. These copolymer encapsulation films were firstly scanned by the hyperspectral imaging equipment (Spectral Imaging Ltd. Oulu, Finland). The scanning band range of hyperspectral equipemnt was set between 904.58 and 1700.01 nm. The hyperspectral dataset of copolymer films was randomly divided into two parts for the training and test purpose. Each type of the training set and test set contained 90 and 10 instances, respectively. The obtained hyperspectral images of EVA films were dealt with by using the ENVI (Exelis Visual Information Solutions, USA) software. The size of region of interest (ROI) of each obtained hyperspectral image of EVA film was set as 150 x 150 pixels. The average of reflectance hyper spectra of all the pixels in the ROI was used as the characteristic curve to represent the instance. There kinds of chemometrics methods including partial least squares regression (PLSR), multi-class support vector machine (SVM) and large margin nearest neighbor (LMNN) were used to correlate the characteristic hyper spectra with the encapsulation temperatures of of copolymer films. The plot of weighted regression coefficients illustrated that both bands of short- and long-wave near infrared hyperspectral data contributed to enhancing the prediction accuracy of the forecast model. Because the attained reflectance hyperspectral data of EVA materials displayed the strong nonlinearity, the prediction performance of linear modeling method of PLSR declined and the prediction precision only reached to 95%. The kernel-based forecast models were

  4. Identification of risk factors of computer information technologies in education

    Directory of Open Access Journals (Sweden)

    Hrebniak M.P.

    2014-03-01

    Full Text Available The basic direction of development of secondary school and vocational training is computer training of schoolchildren and students, including distance forms of education and widespread usage of world information systems. The purpose of the work is to determine risk factors for schoolchildren and students, when using modern information and computer technologies. Results of researches allowed to establish dynamics of formation of skills using computer information technologies in education and characteristics of mental ability among schoolchildren and students during training in high school. Common risk factors, while operating CIT, are: intensification and formalization of intellectual activity, adverse ergonomic parameters, unfavorable working posture, excess of hygiene standards by chemical and physical characteristics. The priority preventive directions in applying computer information technology in education are: improvement of optimal visual parameters of activity, rationalization of ergonomic parameters, minimizing of adverse effects of chemical and physical conditions, rationalization of work and rest activity.

  5. CLOSE RANGE HYPERSPECTRAL IMAGING INTEGRATED WITH TERRESTRIAL LIDAR SCANNING APPLIED TO ROCK CHARACTERISATION AT CENTIMETRE SCALE

    Directory of Open Access Journals (Sweden)

    T. H. Kurz

    2012-07-01

    Full Text Available Compact and lightweight hyperspectral imagers allow the application of close range hyperspectral imaging with a ground based scanning setup for geological fieldwork. Using such a scanning setup, steep cliff sections and quarry walls can be scanned with a more appropriate viewing direction and a higher image resolution than from airborne and spaceborne platforms. Integration of the hyperspectral imagery with terrestrial lidar scanning provides the hyperspectral information in a georeferenced framework and enables measurement at centimetre scale. In this paper, three geological case studies are used to demonstrate the potential of this method for rock characterisation. Two case studies are applied to carbonate quarries where mapping of different limestone and dolomite types was required, as well as measurements of faults and layer thicknesses from inaccessible parts of the quarries. The third case study demonstrates the method using artificial lighting, applied in a subsurface scanning scenario where solar radiation cannot be utilised.

  6. Boosting healthy heart employer-sponsored health dissemination efforts: identification and information-sharing intentions.

    Science.gov (United States)

    Stephens, Keri K; Pastorek, Angie; Crook, Brittani; Mackert, Michael; Donovan, Erin E; Shalev, Heidi

    2015-01-01

    Health information dissemination options have expanded to include workplaces and employer-sponsored efforts. This study focuses on a core relational concept found in workplaces, organizational identification-the feeling of belongingness-and the impact of partnering with employers and health clinics in health information dissemination. We use social-identity theory and multiple identification to test our predictions from a sample of working adults representing more than 100 different employers. We found that when people strongly identify with their employer, they have increased health behavioral intentions and they intend to talk about the health information with coworkers. The significant models explain more than 50% and 30% of the variance in these two outcomes. The experimental results examining single and multiple organizational sources revealed no differences on any outcomes. These findings offer a contribution to health information dissemination research by articulating how identification with an employer functions to affect behavioral intentions.

  7. Detection of wheat powdery mildew by differentiating background factors using hyperspectral imaging

    Science.gov (United States)

    Accurate assessment of crop disease severities is the key for precision application of pesticides to prevent disease infestation. In-situ hyperspectral imaging technology can provide high-resolution imagery with spectra for rapid identification of crop disease and determining disease infestation pat...

  8. Minimum volume simplicial enclosure for spectral unmixing of remotely sensed hyperspectral data

    NARCIS (Netherlands)

    Hendrix, E.M.T.; García, I.; Plaza, J.; Plaza, A.

    2010-01-01

    Spectral unmixing is an important task for remotely sensed hyperspectral data exploitation. Linear spectral unmixing relies on two main steps: 1) identification of pure spectral constituents (endmembers), and 2) end member abundance estimation in mixed pixels. One of the main problems concerning the

  9. Trust Your Boss or Listen to the Union? Information, Social Identification, Trust, and Strike Participation

    NARCIS (Netherlands)

    Born, Marieke J.; Akkerman, Agnes; Torenvlied, René

    2013-01-01

    We investigate two questions regarding the effect of information on participation in labor strikes: First, how are social identification and trust used as filters for information? Second, we investigate how cross-pressures affect willingness to participate. Using a dataset of 468 union members, we

  10. Identification of Functional Information Subgraphs in Complex Networks

    International Nuclear Information System (INIS)

    Bettencourt, Luis M. A.; Gintautas, Vadas; Ham, Michael I.

    2008-01-01

    We present a general information theoretic approach for identifying functional subgraphs in complex networks. We show that the uncertainty in a variable can be written as a sum of information quantities, where each term is generated by successively conditioning mutual informations on new measured variables in a way analogous to a discrete differential calculus. The analogy to a Taylor series suggests efficient optimization algorithms for determining the state of a target variable in terms of functional groups of other nodes. We apply this methodology to electrophysiological recordings of cortical neuronal networks grown in vitro. Each cell's firing is generally explained by the activity of a few neurons. We identify these neuronal subgraphs in terms of their redundant or synergetic character and reconstruct neuronal circuits that account for the state of target cells

  11. Information Integration in Risky Choice: Identification and Stability

    OpenAIRE

    Stewart, Neil

    2011-01-01

    How is information integrated across the\\ud attributes of an option when making risky\\ud choices? In most descriptive models of\\ud decision under risk, information about\\ud risk, and reward is combined multiplicatively\\ud (e.g., expected value; expected utility\\ud theory, Bernouli, 1738/1954; subjective\\ud expected utility theory, Savage, 1954;\\ud Edwards, 1955; prospect theory, Kahneman\\ud and Tversky, 1979; rank-dependent utility,\\ud Quiggin, 1993; decision field theory,\\ud Busemeyer and To...

  12. A Practical Approach for Parameter Identification with Limited Information

    DEFF Research Database (Denmark)

    Zeni, Lorenzo; Yang, Guangya; Tarnowski, Germán Claudio

    2014-01-01

    A practical parameter estimation procedure for a real excitation system is reported in this paper. The core algorithm is based on genetic algorithm (GA) which estimates the parameters of a real AC brushless excitation system with limited information about the system. Practical considerations are ...... parameters. The whole methodology is described and the estimation strategy is presented in this paper....

  13. Context-sensitive Information security Risk identification and evaluation techniques

    NARCIS (Netherlands)

    Ionita, Dan

    2014-01-01

    The objective of my research is to improve and support the process of Information security Risk Assessment by designing a scalable Risk argumentation framework for socio-digital-technical Risk. Due to the various types of IT systems, diversity of architectures and dynamic nature of Risk, there is no

  14. Identification of appropriate tools of information and communication ...

    African Journals Online (AJOL)

    Access to sufficient and desirable food is one of the principles of any developing and healthy society. One of the important means for attainment of food security is information and communication technologies (ICT). The purpose of the research was to identify appropriate tools of ICT in improving food security of Iran's rural ...

  15. The French proposal for a high spatial resolution Hyperspectral mission

    Science.gov (United States)

    Carrère, Véronique; Briottet, Xavier; Jacquemoud, Stéphane; Marion, Rodolphe; Bourguignon, Anne; Chami, Malik; Chanussot, Jocelyn; Chevrel, Stéphane; Deliot, Philippe; Dumont, Marie; Foucher, Pierre-Yves; Gomez, Cécile; Roman-Minghelli, Audrey; Sheeren, David; Weber, Christiane; Lefèvre, Marie-José; Mandea, Mioara

    2014-05-01

    More than 25 years of airborne imaging spectroscopy and spaceborne sensors such as Hyperion or HICO have clearly demonstrated the ability of such a remote sensing technique to produce value added information regarding surface composition and physical properties for a large variety of applications. Scheduled missions such as EnMAP and PRISMA prove the increased interest of the scientific community for such a type of remote sensing data. In France, a group of Science and Defence users of imaging spectrometry data (Groupe de Synthèse Hyperspectral, GSH) established an up-to-date review of possible applications, define instrument specifications required for accurate, quantitative retrieval of diagnostic parameters, and identify fields of application where imaging spectrometry is a major contribution. From these conclusions, CNES (French Space Agency) decided a phase 0 study for an hyperspectral mission concept, named at this time HYPXIM (HYPerspectral-X IMagery), the main fields of applications are vegetation biodiversity, coastal and inland waters, geosciences, urban environment, atmospheric sciences, cryosphere and Defence. Results pointed out applications where high spatial resolution was necessary and would not be covered by the other foreseen hyperspectral missions. The phase A started at the beginning of 2013 based on the following HYPXIM characteristics: a hyperspectral camera covering the [0.4 - 2.5 µm] spectral range with a 8 m ground sampling distance (GSD) and a PAN camera with a 1.85 m GSD, onboard a mini-satellite platform. This phase A is currently stopped due to budget constraints. Nevertheless, the Science team is currently focusing on the preparation for the next CNES prospective meeting (March, 2014), an important step for the future of the mission. This paper will provide an update of the status of this mission and of new results obtained by the Science team.

  16. Rapid hyperspectral image classification to enable autonomous search systems

    Directory of Open Access Journals (Sweden)

    Raj Bridgelal

    2016-11-01

    Full Text Available The emergence of lightweight full-frame hyperspectral cameras is destined to enable autonomous search vehicles in the air, on the ground and in water. Self-contained and long-endurance systems will yield important new applications, for example, in emergency response and the timely identification of environmental hazards. One missing capability is rapid classification of hyperspectral scenes so that search vehicles can immediately take actions to verify potential targets. Onsite verifications minimise false positives and preclude the expense of repeat missions. Verifications will require enhanced image quality, which is achievable by either moving closer to the potential target or by adjusting the optical system. Such a solution, however, is currently impractical for small mobile platforms with finite energy sources. Rapid classifications with current methods demand large computing capacity that will quickly deplete the on-board battery or fuel. To develop the missing capability, the authors propose a low-complexity hyperspectral image classifier that approaches the performance of prevalent classifiers. This research determines that the new method will require at least 19-fold less computing capacity than the prevalent classifier. To assess relative performances, the authors developed a benchmark that compares a statistic of library endmember separability in their respective feature spaces.

  17. Information identification, evaluation and utilisation for decision-making by managers in South West Nigeria

    Directory of Open Access Journals (Sweden)

    Omotola Osunrinde

    2017-11-01

    Full Text Available Background: Managers’ organisational decisions and subsequent actions flow from their understanding of the business environment in which they operate. This study sought to understand how managers in various organisations identify, evaluate and use information for effective current and future decision-making. Objectives: The study focused on the types of information needed by managers for decision-making, the methods used to identify and acquire the information and the sources of information consulted, their satisfaction with the information used and their decision-making behaviours. Methods: The study employed descriptive study design. Simple random sampling was used. A pre-tested self-administered questionnaire was used to gather information from 219 managers, randomly selected from the registers of the Ibadan, Abeokuta and Lagos chapters of Nigerian Institute of Management. Results: Results indicated that the types of information considered very important for decision-making included industry information followed by government policies and economic development/forecasts. Conclusion: Investigation revealed the extent of information identification, information evaluation and information utilisation individually predict the perceived effectiveness of decision-making by the managers. Nevertheless, information evaluation was found to have greater predictive relationship with perceived effectiveness of decision-making than information use and information identification.

  18. Multiband and Lossless Compression of Hyperspectral Images

    Directory of Open Access Journals (Sweden)

    Raffaele Pizzolante

    2016-02-01

    Full Text Available Hyperspectral images are widely used in several real-life applications. In this paper, we investigate on the compression of hyperspectral images by considering different aspects, including the optimization of the computational complexity in order to allow implementations on limited hardware (i.e., hyperspectral sensors, etc.. We present an approach that relies on a three-dimensional predictive structure. Our predictive structure, 3D-MBLP, uses one or more previous bands as references to exploit the redundancies among the third dimension. The achieved results are comparable, and often better, with respect to the other state-of-art lossless compression techniques for hyperspectral images.

  19. A Student Information Management System Based on Fingerprint Identification and Data Security Transmission

    Directory of Open Access Journals (Sweden)

    Pengtao Yang

    2017-01-01

    Full Text Available A new type of student information management system is designed to implement student information identification and management based on fingerprint identification. In order to ensure the security of data transmission, this paper proposes a data encryption method based on an improved AES algorithm. A new S-box is cleverly designed, which can significantly reduce the encryption time by improving ByteSub, ShiftRow, and MixColumn in the round transformation of the traditional AES algorithm with the process of look-up table. Experimental results show that the proposed algorithm can significantly improve the encryption time compared with the traditional AES algorithm.

  20. [Measures to prevent patient identification errors in blood collection/physiological function testing utilizing a laboratory information system].

    Science.gov (United States)

    Shimazu, Chisato; Hoshino, Satoshi; Furukawa, Taiji

    2013-08-01

    We constructed an integrated personal identification workflow chart using both bar code reading and an all in-one laboratory information system. The information system not only handles test data but also the information needed for patient guidance in the laboratory department. The reception terminals at the entrance, displays for patient guidance and patient identification tools at blood-sampling booths are all controlled by the information system. The number of patient identification errors was greatly reduced by the system. However, identification errors have not been abolished in the ultrasound department. After re-evaluation of the patient identification process in this department, we recognized that the major reason for the errors came from excessive identification workflow. Ordinarily, an ultrasound test requires patient identification 3 times, because 3 different systems are required during the entire test process, i.e. ultrasound modality system, laboratory information system and a system for producing reports. We are trying to connect the 3 different systems to develop a one-time identification workflow, but it is not a simple task and has not been completed yet. Utilization of the laboratory information system is effective, but is not yet perfect for patient identification. The most fundamental procedure for patient identification is to ask a person's name even today. Everyday checks in the ordinary workflow and everyone's participation in safety-management activity are important for the prevention of patient identification errors.

  1. The study of active tectonic based on hyperspectral remote sensing

    Science.gov (United States)

    Cui, J.; Zhang, S.; Zhang, J.; Shen, X.; Ding, R.; Xu, S.

    2017-12-01

    As of the latest technical methods, hyperspectral remote sensing technology has been widely used in each brach of the geosciences. However, it is still a blank for using the hyperspectral remote sensing to study the active structrure. Hyperspectral remote sensing, with high spectral resolution, continuous spectrum, continuous spatial data, low cost, etc, has great potentialities in the areas of stratum division and fault identification. Blind fault identification in plains and invisible fault discrimination in loess strata are the two hot problems in the current active fault research. Thus, the study of active fault based on the hyperspectral technology has great theoretical significance and practical value. Magnetic susceptibility (MS) records could reflect the rhythm alteration of the formation. Previous study shown that MS has correlation with spectral feature. In this study, the Emaokou section, located to the northwest of the town of Huairen, in Shanxi Province, has been chosen for invisible fault study. We collected data from the Emaokou section, including spectral data, hyperspectral image, MS data. MS models based on spectral features were established and applied to the UHD185 image for MS mapping. The results shown that MS map corresponded well to the loess sequences. It can recognize the stratum which can not identity by naked eyes. Invisible fault has been found in this section, which is useful for paleoearthquake analysis. The faults act as the conduit for migration of terrestrial gases, the fault zones, especially the structurally weak zones such as inrtersections or bends of fault, may has different material composition. We take Xiadian fault for study. Several samples cross-fault were collected and these samples were measured by ASD Field Spec 3 spectrometer. Spectral classification method has been used for spectral analysis, we found that the spectrum of the fault zone have four special spectral region(550-580nm, 600-700nm, 700-800nm and 800-900nm

  2. Bobcat 2013: a hyperspectral data collection supporting the development and evaluation of spatial-spectral algorithms

    Science.gov (United States)

    Kaufman, Jason; Celenk, Mehmet; White, A. K.; Stocker, Alan D.

    2014-06-01

    The amount of hyperspectral imagery (HSI) data currently available is relatively small compared to other imaging modalities, and what is suitable for developing, testing, and evaluating spatial-spectral algorithms is virtually nonexistent. In this work, a significant amount of coincident airborne hyperspectral and high spatial resolution panchromatic imagery that supports the advancement of spatial-spectral feature extraction algorithms was collected to address this need. The imagery was collected in April 2013 for Ohio University by the Civil Air Patrol, with their Airborne Real-time Cueing Hyperspectral Enhanced Reconnaissance (ARCHER) sensor. The target materials, shapes, and movements throughout the collection area were chosen such that evaluation of change detection algorithms, atmospheric compensation techniques, image fusion methods, and material detection and identification algorithms is possible. This paper describes the collection plan, data acquisition, and initial analysis of the collected imagery.

  3. Classification of High-Mountain Vegetation Communities within a Diverse Giant Mountains Ecosystem Using Airborne APEX Hyperspectral Imagery

    Directory of Open Access Journals (Sweden)

    Adriana Marcinkowska-Ochtyra

    2018-04-01

    Full Text Available Mapping plant communities is a difficult and time consuming endeavor. Methods relying on field surveys deliver high quality data but are usually limited to relatively small areas. In this paper we apply airborne hyperspectral data to vegetation mapping in remote and hard to reach areas. We classified 22 vegetation communities in the Giant Mountains on 3.12-m Airborne Prism Experiment (APEX hyperspectral images, registered in 288 spectral bands (10 September 2012. As the classification algorithm, Support Vector Machines (SVM was used. APEX data were corrected geometrically and atmospherically, and three dimensionality reduction methods were performed to select the best dataset. As reference we used a non-forest vegetation map containing vegetation communities of Polish Karkonosze National Park from 2002, orthophotomap and field surveys data from 2013 to 2014. We obtained the post-classification maps of 22 vegetation communities, lakes and areas without any vegetation. Iterative accuracy assessment repeated 100 times was used to obtain the most objective results for individual communities. The median value of overall accuracy (OA was 84%. Fourteen out of twenty-four classes were classified of more than 80% of producer accuracy (PA and sixteen out of twenty-four of user accuracy (UA. APEX data and SVM with the use of iterative accuracy assessment are useful for the mountain communities classification. This can support both Polish and Czech national parks management by giving the information about diversity of communities in the whole transboundary area, helping with identification especially in changing environment caused by humans.

  4. Panchromatic cooperative hyperspectral adaptive wide band deletion repair method

    Science.gov (United States)

    Jiang, Bitao; Shi, Chunyu

    2018-02-01

    In the hyperspectral data, the phenomenon of stripe deletion often occurs, which seriously affects the efficiency and accuracy of data analysis and application. Narrow band deletion can be directly repaired by interpolation, and this method is not ideal for wide band deletion repair. In this paper, an adaptive spectral wide band missing restoration method based on panchromatic information is proposed, and the effectiveness of the algorithm is verified by experiments.

  5. Hyperspectral remote sensing of vegetation

    Science.gov (United States)

    Thenkabail, Prasad S.; Lyon, John G.; Huete, Alfredo

    2011-01-01

    Hyperspectral narrow-band (or imaging spectroscopy) spectral data are fast emerging as practical solutions in modeling and mapping vegetation. Recent research has demonstrated the advances in and merit of hyperspectral data in a range of applications including quantifying agricultural crops, modeling forest canopy biochemical properties, detecting crop stress and disease, mapping leaf chlorophyll content as it influences crop production, identifying plants affected by contaminants such as arsenic, demonstrating sensitivity to plant nitrogen content, classifying vegetation species and type, characterizing wetlands, and mapping invasive species. The need for significant improvements in quantifying, modeling, and mapping plant chemical, physical, and water properties is more critical than ever before to reduce uncertainties in our understanding of the Earth and to better sustain it. There is also a need for a synthesis of the vast knowledge spread throughout the literature from more than 40 years of research.

  6. Hyperspectral discrimination of camouflaged target

    Science.gov (United States)

    Bárta, Vojtěch; Racek, František

    2017-10-01

    The article deals with detection of camouflaged objects during winter season. Winter camouflage is a marginal affair in most countries due to short time period of the snow cover. In the geographical condition of Central Europe the winter period with snow occurs less than 1/12 of year. The LWIR or SWIR spectral areas are used for detection of camouflaged objects. For those spectral regions the difference in chemical composition and temperature express in spectral features. Exploitation of the LWIR and SWIR devices is demanding due to their large dimension and expensiveness. Therefore, the article deals with estimation of utilization of VIS region for detecting of camouflaged object on snow background. The multispectral image output for the various spectral filters is simulated. Hyperspectral indices are determined to detect the camouflaged objects in the winter. The multispectral image simulation is based on the hyperspectral datacube obtained in real conditions.

  7. Hyperspectral Image Classification Using Discriminative Dictionary Learning

    International Nuclear Information System (INIS)

    Zongze, Y; Hao, S; Kefeng, J; Huanxin, Z

    2014-01-01

    The hyperspectral image (HSI) processing community has witnessed a surge of papers focusing on the utilization of sparse prior for effective HSI classification. In sparse representation based HSI classification, there are two phases: sparse coding with an over-complete dictionary and classification. In this paper, we first apply a novel fisher discriminative dictionary learning method, which capture the relative difference in different classes. The competitive selection strategy ensures that atoms in the resulting over-complete dictionary are the most discriminative. Secondly, motivated by the assumption that spatially adjacent samples are statistically related and even belong to the same materials (same class), we propose a majority voting scheme incorporating contextual information to predict the category label. Experiment results show that the proposed method can effectively strengthen relative discrimination of the constructed dictionary, and incorporating with the majority voting scheme achieve generally an improved prediction performance

  8. Hyperspectral laser-induced autofluorescence imaging of dental caries

    Science.gov (United States)

    Bürmen, Miran; Fidler, Aleš; Pernuš, Franjo; Likar, Boštjan

    2012-01-01

    Dental caries is a disease characterized by demineralization of enamel crystals leading to the penetration of bacteria into the dentine and pulp. Early detection of enamel demineralization resulting in increased enamel porosity, commonly known as white spots, is a difficult diagnostic task. Laser induced autofluorescence was shown to be a useful method for early detection of demineralization. The existing studies involved either a single point spectroscopic measurements or imaging at a single spectral band. In the case of spectroscopic measurements, very little or no spatial information is acquired and the measured autofluorescence signal strongly depends on the position and orientation of the probe. On the other hand, single-band spectral imaging can be substantially affected by local spectral artefacts. Such effects can significantly interfere with automated methods for detection of early caries lesions. In contrast, hyperspectral imaging effectively combines the spatial information of imaging methods with the spectral information of spectroscopic methods providing excellent basis for development of robust and reliable algorithms for automated classification and analysis of hard dental tissues. In this paper, we employ 405 nm laser excitation of natural caries lesions. The fluorescence signal is acquired by a state-of-the-art hyperspectral imaging system consisting of a high-resolution acousto-optic tunable filter (AOTF) and a highly sensitive Scientific CMOS camera in the spectral range from 550 nm to 800 nm. The results are compared to the contrast obtained by near-infrared hyperspectral imaging technique employed in the existing studies on early detection of dental caries.

  9. Memory for stereotype (in)consistent information : The role of in-group identification

    NARCIS (Netherlands)

    Doosje, Bertjan; Spears, Russell; de Redelijkheid, Hans; van Onna, Joost

    Effects of identification with one's group on memory of stereotype consistent and inconsistent information about one's group were examined in two studies. In the first study, we focused on supporters of a low status soccer team, and observed that diehard fans were more likely to remember

  10. Interest point detection for hyperspectral imagery

    Science.gov (United States)

    Dorado-Muñoz, Leidy P.; Vélez-Reyes, Miguel; Roysam, Badrinath; Mukherjee, Amit

    2009-05-01

    This paper presents an algorithm for automated extraction of interest points (IPs)in multispectral and hyperspectral images. Interest points are features of the image that capture information from its neighbours and they are distinctive and stable under transformations such as translation and rotation. Interest-point operators for monochromatic images were proposed more than a decade ago and have since been studied extensively. IPs have been applied to diverse problems in computer vision, including image matching, recognition, registration, 3D reconstruction, change detection, and content-based image retrieval. Interest points are helpful in data reduction, and reduce the computational burden of various algorithms (like registration, object detection, 3D reconstruction etc) by replacing an exhaustive search over the entire image domain by a probe into a concise set of highly informative points. An interest operator seeks out points in an image that are structurally distinct, invariant to imaging conditions, stable under geometric transformation, and interpretable which are good candidates for interest points. Our approach extends ideas from Lowe's keypoint operator that uses local extrema of Difference of Gaussian (DoG) operator at multiple scales to detect interest point in gray level images. The proposed approach extends Lowe's method by direct conversion of scalar operations such as scale-space generation, and extreme point detection into operations that take the vector nature of the image into consideration. Experimental results with RGB and hyperspectral images which demonstrate the potential of the method for this application and the potential improvements of a fully vectorial approach over band-by-band approaches described in the literature.

  11. Identifying Non-Volatile Data Storage Areas: Unique Notebook Identification Information as Digital Evidence

    Directory of Open Access Journals (Sweden)

    Nikica Budimir

    2007-03-01

    Full Text Available The research reported in this paper introduces new techniques to aid in the identification of recovered notebook computers so they may be returned to the rightful owner. We identify non-volatile data storage areas as a means of facilitating the safe storing of computer identification information. A forensic proof of concept tool has been designed to test the feasibility of several storage locations identified within this work to hold the data needed to uniquely identify a computer. The tool was used to perform the creation and extraction of created information in order to allow the analysis of the non-volatile storage locations as valid storage areas capable of holding and preserving the data created within them.  While the format of the information used to identify the machine itself is important, this research only discusses the insertion, storage and ability to retain such information.

  12. Identification and summary characterization of materials potentially requiring vitrification: Background information

    International Nuclear Information System (INIS)

    Croff, A.G.

    1996-01-01

    This document contains background information for the Workshop in general and the presentation entitled 'Identification and Summary Characterization of Materials Potentially Requiring Vitrification' that was given during the first morning of the workshop. summary characteristics of 9 categories of US materials having some potential to be vitrified are given. This is followed by a 1-2 page elaborations for each of these 9 categories. References to more detailed information are included

  13. Hyperspectral Soil Mapper (HYSOMA) software interface: Review and future plans

    Science.gov (United States)

    Chabrillat, Sabine; Guillaso, Stephane; Eisele, Andreas; Rogass, Christian

    2014-05-01

    With the upcoming launch of the next generation of hyperspectral satellites that will routinely deliver high spectral resolution images for the entire globe (e.g. EnMAP, HISUI, HyspIRI, HypXIM, PRISMA), an increasing demand for the availability/accessibility of hyperspectral soil products is coming from the geoscience community. Indeed, many robust methods for the prediction of soil properties based on imaging spectroscopy already exist and have been successfully used for a wide range of soil mapping airborne applications. Nevertheless, these methods require expert know-how and fine-tuning, which makes them used sparingly. More developments are needed toward easy-to-access soil toolboxes as a major step toward the operational use of hyperspectral soil products for Earth's surface processes monitoring and modelling, to allow non-experienced users to obtain new information based on non-expensive software packages where repeatability of the results is an important prerequisite. In this frame, based on the EU-FP7 EUFAR (European Facility for Airborne Research) project and EnMAP satellite science program, higher performing soil algorithms were developed at the GFZ German Research Center for Geosciences as demonstrators for end-to-end processing chains with harmonized quality measures. The algorithms were built-in into the HYSOMA (Hyperspectral SOil MApper) software interface, providing an experimental platform for soil mapping applications of hyperspectral imagery that gives the choice of multiple algorithms for each soil parameter. The software interface focuses on fully automatic generation of semi-quantitative soil maps such as soil moisture, soil organic matter, iron oxide, clay content, and carbonate content. Additionally, a field calibration option calculates fully quantitative soil maps provided ground truth soil data are available. Implemented soil algorithms have been tested and validated using extensive in-situ ground truth data sets. The source of the HYSOMA

  14. Eyewitness identification: Bayesian information gain, base-rate effect equivalency curves, and reasonable suspicion.

    Science.gov (United States)

    Wells, Gary L; Yang, Yueran; Smalarz, Laura

    2015-04-01

    We provide a novel Bayesian treatment of the eyewitness identification problem as it relates to various system variables, such as instruction effects, lineup presentation format, lineup-filler similarity, lineup administrator influence, and show-ups versus lineups. We describe why eyewitness identification is a natural Bayesian problem and how numerous important observations require careful consideration of base rates. Moreover, we argue that the base rate in eyewitness identification should be construed as a system variable (under the control of the justice system). We then use prior-by-posterior curves and information-gain curves to examine data obtained from a large number of published experiments. Next, we show how information-gain curves are moderated by system variables and by witness confidence and we note how information-gain curves reveal that lineups are consistently more proficient at incriminating the guilty than they are at exonerating the innocent. We then introduce a new type of analysis that we developed called base rate effect-equivalency (BREE) curves. BREE curves display how much change in the base rate is required to match the impact of any given system variable. The results indicate that even relatively modest changes to the base rate can have more impact on the reliability of eyewitness identification evidence than do the traditional system variables that have received so much attention in the literature. We note how this Bayesian analysis of eyewitness identification has implications for the question of whether there ought to be a reasonable-suspicion criterion for placing a person into the jeopardy of an identification procedure. (c) 2015 APA, all rights reserved).

  15. A hyperspectral image analysis workbench for environmental science applications

    Energy Technology Data Exchange (ETDEWEB)

    Christiansen, J.H.; Zawada, D.G.; Simunich, K.L.; Slater, J.C.

    1992-10-01

    A significant challenge to the information sciences is to provide more powerful and accessible means to exploit the enormous wealth of data available from high-resolution imaging spectrometry, or ``hyperspectral`` imagery, for analysis, for mapping purposes, and for input to environmental modeling applications. As an initial response to this challenge, Argonne`s Advanced Computer Applications Center has developed a workstation-based prototype software workbench which employs Al techniques and other advanced approaches to deduce surface characteristics and extract features from the hyperspectral images. Among its current capabilities, the prototype system can classify pixels by abstract surface type. The classification process employs neural network analysis of inputs which include pixel spectra and a variety of processed image metrics, including image ``texture spectra`` derived from fractal signatures computed for subimage tiles at each wavelength.

  16. A hyperspectral image analysis workbench for environmental science applications

    Energy Technology Data Exchange (ETDEWEB)

    Christiansen, J.H.; Zawada, D.G.; Simunich, K.L.; Slater, J.C.

    1992-01-01

    A significant challenge to the information sciences is to provide more powerful and accessible means to exploit the enormous wealth of data available from high-resolution imaging spectrometry, or hyperspectral'' imagery, for analysis, for mapping purposes, and for input to environmental modeling applications. As an initial response to this challenge, Argonne's Advanced Computer Applications Center has developed a workstation-based prototype software workbench which employs Al techniques and other advanced approaches to deduce surface characteristics and extract features from the hyperspectral images. Among its current capabilities, the prototype system can classify pixels by abstract surface type. The classification process employs neural network analysis of inputs which include pixel spectra and a variety of processed image metrics, including image texture spectra'' derived from fractal signatures computed for subimage tiles at each wavelength.

  17. The effect of texture on face identification and configural information processing

    Directory of Open Access Journals (Sweden)

    Tzschaschel Eva Alica

    2014-01-01

    Full Text Available Shape and texture are an integral part of face identity. In the present study, the importance of face texture for face identification and detection of configural manipulation (i.e., spatial relation among facial features was examined by comparing grayscale face photographs (i.e., real faces and line drawings of the same faces. Whereas real faces provide information about texture and shape of faces, line drawings are lacking texture cues. A change-detection task and a forced-choice identification task were used with both stimuli categories. Within the change detection task, participants had to decide whether the size of the eyes of two sequentially presented faces had changed or not. After having made this decision, three faces were shown to the subjects and they had to identify the previously shown face among them. Furthermore, context (full vs. cropped faces and orientation (upright vs. inverted were manipulated. The results obtained in the change detection task suggest that configural information was used in processing real faces, while part-based and featural information was used in processing line-drawings. Additionally, real faces were identified more accurately than line drawings, and identification was less context but more orientation sensitive than identification of line drawings. Taken together, the results of the present study provide new evidence stressing the importance of face texture for identity encoding and configural face processing.

  18. 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.

  19. Reconstruction of hyperspectral image using matting model for classification

    Science.gov (United States)

    Xie, Weiying; Li, Yunsong; Ge, Chiru

    2016-05-01

    Although hyperspectral images (HSIs) captured by satellites provide much information in spectral regions, some bands are redundant or have large amounts of noise, which are not suitable for image analysis. To address this problem, we introduce a method for reconstructing the HSI with noise reduction and contrast enhancement using a matting model for the first time. The matting model refers to each spectral band of an HSI that can be decomposed into three components, i.e., alpha channel, spectral foreground, and spectral background. First, one spectral band of an HSI with more refined information than most other bands is selected, and is referred to as an alpha channel of the HSI to estimate the hyperspectral foreground and hyperspectral background. Finally, a combination operation is applied to reconstruct the HSI. In addition, the support vector machine (SVM) classifier and three sparsity-based classifiers, i.e., orthogonal matching pursuit (OMP), simultaneous OMP, and OMP based on first-order neighborhood system weighted classifiers, are utilized on the reconstructed HSI and the original HSI to verify the effectiveness of the proposed method. Specifically, using the reconstructed HSI, the average accuracy of the SVM classifier can be improved by as much as 19%.

  20. 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.

  1. 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.

  2. A FPGA implementation for linearly unmixing a hyperspectral image using OpenCL

    Science.gov (United States)

    Guerra, Raúl; López, Sebastián.; Sarmiento, Roberto

    2017-10-01

    Hyperspectral imaging systems provide images in which single pixels have information from across the electromagnetic spectrum of the scene under analysis. These systems divide the spectrum into many contiguos channels, which may be even out of the visible part of the spectra. The main advantage of the hyperspectral imaging technology is that certain objects leave unique fingerprints in the electromagnetic spectrum, known as spectral signatures, which allow to distinguish between different materials that may look like the same in a traditional RGB image. Accordingly, the most important hyperspectral imaging applications are related with distinguishing or identifying materials in a particular scene. In hyperspectral imaging applications under real-time constraints, the huge amount of information provided by the hyperspectral sensors has to be rapidly processed and analysed. For such purpose, parallel hardware devices, such as Field Programmable Gate Arrays (FPGAs) are typically used. However, developing hardware applications typically requires expertise in the specific targeted device, as well as in the tools and methodologies which can be used to perform the implementation of the desired algorithms in the specific device. In this scenario, the Open Computing Language (OpenCL) emerges as a very interesting solution in which a single high-level synthesis design language can be used to efficiently develop applications in multiple and different hardware devices. In this work, the Fast Algorithm for Linearly Unmixing Hyperspectral Images (FUN) has been implemented into a Bitware Stratix V Altera FPGA using OpenCL. The obtained results demonstrate the suitability of OpenCL as a viable design methodology for quickly creating efficient FPGAs designs for real-time hyperspectral imaging applications.

  3. A New Algorithm for the On-Board Compression of Hyperspectral Images

    Directory of Open Access Journals (Sweden)

    Raúl Guerra

    2018-03-01

    Full Text Available Hyperspectral sensors are able to provide information that is useful for many different applications. However, the huge amounts of data collected by these sensors are not exempt of drawbacks, especially in remote sensing environments where the hyperspectral images are collected on-board satellites and need to be transferred to the earth’s surface. In this situation, an efficient compression of the hyperspectral images is mandatory in order to save bandwidth and storage space. Lossless compression algorithms have been traditionally preferred, in order to preserve all the information present in the hyperspectral cube for scientific purposes, despite their limited compression ratio. Nevertheless, the increment in the data-rate of the new-generation sensors is making more critical the necessity of obtaining higher compression ratios, making it necessary to use lossy compression techniques. A new transform-based lossy compression algorithm, namely Lossy Compression Algorithm for Hyperspectral Image Systems (HyperLCA, is proposed in this manuscript. This compressor has been developed for achieving high compression ratios with a good compression performance at a reasonable computational burden. An extensive amount of experiments have been performed in order to evaluate the goodness of the proposed HyperLCA compressor using different calibrated and uncalibrated hyperspectral images from the AVIRIS and Hyperion sensors. The results provided by the proposed HyperLCA compressor have been evaluated and compared against those produced by the most relevant state-of-the-art compression solutions. The theoretical and experimental evidence indicates that the proposed algorithm represents an excellent option for lossy compressing hyperspectral images, especially for applications where the available computational resources are limited, such as on-board scenarios.

  4. Language identification of information blocks based on lexico-grammatic markers

    Directory of Open Access Journals (Sweden)

    Sergey N. Kalegin

    2017-12-01

    Full Text Available This article is a continuation of the author's series of publications on the subjects of language identification of texts. In the article is being considered the creation of a technological basis for language identification systems of unstructured information blocks based on lexico-grammatical markers, in which are used the forms of verbs, verbal formations or functionally analogous constructions, are described method and algorithm for its software implementation. These developments will significantly reduce the resource intensity and improve the quality of such systems, which will give a significant economic effect and the possibility of creating fundamentally new technologies for determining the linguistic affiliation of information in a multilingual environment. Consequently, the study is of interest for computer linguists and developers of automatic word processing systems, such as: global monitoring systems, multilingual knowledge bases, automatic translation systems, information retrieval systems, document summarizing systems, literature catalogers, etc.

  5. Identification of energy information needs and existing information sources for Pennsylvania

    Energy Technology Data Exchange (ETDEWEB)

    Wisch, A.; Kunzier, J.; Limaye, D.; Orlando, J.

    1976-01-01

    Through use of a comprehensive interviewing schedule designed to elicit information needs from state policymakers, this study has shown a statewide need for a workable energy information network. As a counterpoint to this needs survey, it was also demonstrated that many of the components of such an information base already are available at the state and Federal levels. In order to assure that Pennsylvania's decision makers have access to this required information in a current and useful format at a minimal cost, this study has suggested a three-pronged action program: (1) In order to construct a workable energy information network for use by the Commonwealth, a liaison should be established with the Governor's Energy Council and the various national and regional energy information sources as cited in this report. (2) An information directory on State, Federal and private sources should be maintained and distributed on a continuing basis. An assessment of each source should be included with information on ease of access and relevance of the source to Pennsylvania. (3) After an information need is unable to be met through use of (1) the state energy information network and/or (2) the state energy information directory, effort should be initiated to satisfy that specific requirement.

  6. Producing patient-avatar identification in animation video information on spinal anesthesia by different narrative strategies.

    Science.gov (United States)

    Høybye, Mette Terp; Vesterby, Martin; Jørgensen, Lene Bastrup

    2016-06-01

    Visual approaches to health information reduce complexity and may bridge challenges in health literacy. But the mechanisms and meanings of using animated video in communication with patients undergoing surgery are not well described. By comparing two versions of a two-dimensional animated video on spinal anesthesia, this study tested the patient-avatar identification within two different narrative models. To explore the perspectives of total hip arthroplasty, we employed qualitative methods of interviews and ethnographic observation. The animated presentation of the spinal anesthesia procedure was immediately recognized by all participants as reflecting their experience of the procedure independent of the narrative form. The avatar gender did not affect this identification. We found no preference for either narrative form. This study supports the potential of animation video in health informatics as a didactic model for qualifying patient behavior. Animation video creates a high degree of identification that may work to reduce pre-surgical anxiety. © The Author(s) 2014.

  7. 40 CFR 91.113 - Requirement of certification-emission control information label and engine identification number.

    Science.gov (United States)

    2010-07-01

    ... control information label and engine identification number. 91.113 Section 91.113 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) CONTROL OF EMISSIONS FROM... certification—emission control information label and engine identification number. (a) The engine manufacturer...

  8. A Web-based patient information system--identification of patients' information needs.

    Science.gov (United States)

    Hassling, Linda; Babic, Ankica; Lönn, Urban; Casimir-Ahn, Henrik

    2003-06-01

    Research described here was carried out to explore possibilities of creating a web-based patient information system within the areas of thoracic surgery. Data were collected to distinguish and assess the actual information needs of patients (1) prior to surgical treatment, (2) before discharge, and (3) 8 months after the hospitalization using a follow-up questionnaire. Interviews were performed with patients undergoing heart surgery. The study included material of 19 consecutive patients undergoing coronary artery bypass surgery (12) and valve replacement (7), age 35-74, 13 males and 6 females with nonacademic background. Patient satisfaction with given information was high. Analysis of the interviews held at the hospital resulted in seven different categories describing and giving a picture of the patients' information needs and apprehension of received care. The results found in this study can be used as guidance for developers in their design and development process of a health information system.

  9. [Analysis of related factors of slope plant hyperspectral remote sensing].

    Science.gov (United States)

    Sun, Wei-Qi; Zhao, Yun-Sheng; Tu, Lin-Ling

    2014-09-01

    In the present paper, the slope gradient, aspect, detection zenith angle and plant types were analyzed. In order to strengthen the theoretical discussion, the research was under laboratory condition, and modeled uniform slope for slope plant. Through experiments we found that these factors indeed have influence on plant hyperspectral remote sensing. When choosing slope gradient as the variate, the blade reflection first increases and then decreases as the slope gradient changes from 0° to 36°; When keeping other factors constant, and only detection zenith angle increasing from 0° to 60°, the spectral characteristic of slope plants do not change significantly in visible light band, but decreases gradually in near infrared band; With only slope aspect changing, when the dome meets the light direction, the blade reflectance gets maximum, and when the dome meets the backlit direction, the blade reflectance gets minimum, furthermore, setting the line of vertical intersection of incidence plane and the dome as an axis, the reflectance on the axis's both sides shows symmetric distribution; In addition, spectral curves of different plant types have a lot differences between each other, which means that the plant types also affect hyperspectral remote sensing results of slope plants. This research breaks through the limitations of the traditional vertical remote sensing data collection and uses the multi-angle and hyperspectral information to analyze spectral characteristics of slope plants. So this research has theoretical significance to the development of quantitative remote sensing, and has application value to the plant remote sensing monitoring.

  10. Hyperspectral and thermal methodologies applied to landslide monitoring

    Science.gov (United States)

    Vellico, Michela; Sterzai, Paolo; Pietrapertosa, Carla; Mora, Paolo; Berti, Matteo; Corsini, Alessandro; Ronchetti, Francesco; Giannini, Luciano; Vaselli, Orlando

    2010-05-01

    Landslide monitoring is a very actual topic. Landslides are a widespread phenomenon over the European territory and these phenomena have been responsible of huge economic losses. The aim of the WISELAND research project (Integrated Airborne and Wireless Sensor Network systems for Landslide Monitoring), funded by the Italian Government, is to test new monitoring techniques capable to rapidly and successfully characterize large landslides in fine soils. Two active earthflows in the Northern Italian Appenines have been chosen as test sites and investigated: Silla (Bologna Province) and Valoria (Modena Province). The project implies the use of remote sensing methodologies, with particular focus on the joint use of airborne Lidar, hyperspectral and thermal systems. These innovative techniques give promising results, since they allow to detect the principal landslide components and to evaluate the spatial distribution of parameters relevant to landslide dynamics such as surface water content and roughness. In this paper we put the attention on the response of the terrain related to the use of a hyperspectral system and its integration with the complementary information obtained using a thermal sensor. The potentiality of a hyperspectral dataset acquired in the VNIR (Visible Near Infrared field) and of the spectral response of the terrain could be high since they give important information both on the soil and on the vegetation status. Several significant indexes can be calculated, such as NDVI, obtained considering a band in the Red field and a band in the Infrared field; it gives information on the vegetation health and indirectly on the water content of soils. This is a key point that bridges hyperspectral and thermal datasets. Thermal infrared data are closely related to soil moisture, one of the most important parameter affecting surface stability in soil slopes. Effective stresses and shear strength in unsaturated soils are directly related to water content, and

  11. Unfamiliar voice identification: Effect of post-event information on accuracy and voice ratings

    Directory of Open Access Journals (Sweden)

    Harriet Mary Jessica Smith

    2014-04-01

    Full Text Available This study addressed the effect of misleading post-event information (PEI on voice ratings, identification accuracy, and confidence, as well as the link between verbal recall and accuracy. Participants listened to a dialogue between male and female targets, then read misleading information about voice pitch. Participants engaged in verbal recall, rated voices on a feature checklist, and made a lineup decision. Accuracy rates were low, especially on target-absent lineups. Confidence and accuracy were unrelated, but the number of facts recalled about the voice predicted later lineup accuracy. There was a main effect of misinformation on ratings of target voice pitch, but there was no effect on identification accuracy or confidence ratings. As voice lineup evidence from earwitnesses is used in courts, the findings have potential applied relevance.

  12. Mineral mapping in the western Kunlun Mountains using Tiangong-1 hyperspectral imagery

    Science.gov (United States)

    Ge, W.; Cheng, Q.; Jing, L.; Chen, Y.; Guo, X.; Ding, H.; Liu, Q.

    2016-04-01

    The unmanned Chinese space module Tiangong-1 was launched in September 2011 with a hyperspectral sensor on board. The sensor combines high spatial and spectral resolution suitable for mineral mapping. In this study, Tiangong-1 hyperspectral data were employed for mineral mapping in the western Kunlun Mountains, an important metallogenic belt in China. A Spectral Hourglass Wizard method was applied to detect common minerals from the Tiangong- 1 shortwave infrared data with reference to a set of spectral libraries. Spectral information on minerals, such as zoisite, mica, quartz, sodalite, dolomite, and actinolite, was extracted from the data. The resulting mineral interpretation maps were highly correlated with the reference geological maps and information from ASTER satellite imagery, suggesting that the hyperspectral data are suitable for mineral mapping.

  13. 基于天宫一号高光谱数据的荒漠化地区稀疏植被参量估测%Estimation for Sparse Vegetation Information in Desertification Region Based on Tiangong-1 Hyperspectral Image

    Institute of Scientific and Technical Information of China (English)

    吴俊君; 高志海; 李增元; 王红岩; 庞勇; 孙斌; 李长龙; 李绪志; 张九星

    2014-01-01

    为了精准地估测荒漠化地区的稀疏植被信息,选取内蒙古苏尼特右旗为研究区,以天宫一号高光谱数据为数据源,结合野外实地调查数据,通过归一化植被指数(normalized difference vegetation index , NDVI)和土壤调节植被指数(soil adjusted vegetation index ,SAVI)对研究区内的植被覆盖度和生物量进行反演,并对比两种植被指数的优劣。首先,分析了每种波段组合下的植被指数与覆盖度、生物量的相关性,确定了最大相关的波段组合。覆盖度和生物量与NDVI的最大相关系数可达0.7左右,而与SAVI的最大相关系数可达0.8左右。NDVI的最佳波段组合的红光波段中心波长为630nm,近红外波段的中心波长为910nm,而SAVI的组合为620和920nm。其次,分别构建了两种植被指数与覆盖度、生物量之间的线性回归模型,所建模型的R2均能达到0.5以上。SAVI所建模型R2要比NDVI略高,其中植被覆盖度的反演模型R2高达0.59。经留一交叉验证,SAVI所建模型的均方根误差RMSE也比基于NDVI的模型小。结果表明:天宫一号高光谱数据丰富的光谱信息能有效地反映地表植被的真实情况,并且SAVI比NDVI更能较为精准地估测荒漠化地区的稀疏植被信息。%In order to estimate the sparse vegetation information accurately in desertification region ,taking southeast of Sunite Right Banner ,Inner Mongolia ,as the test site and Tiangong-1 hyperspectral image as the main data ,sparse vegetation coverage and biomass were retrieved based on normalized difference vegetation in-dex(NDVI) and soil adjusted vegetation index (SAVI) ,combined with the field investigation data .Then the advantages and disadvantages between them were compared .Firstly ,the correlation between vegetation inde-xes and vegetation coverage under different bands combination was analyzed ,as well as the biomass .Secondly , the best bands combination

  14. GEOMETRIC AND REFLECTANCE SIGNATURE CHARACTERIZATION OF COMPLEX CANOPIES USING HYPERSPECTRAL STEREOSCOPIC IMAGES FROM UAV AND TERRESTRIAL PLATFORMS

    Directory of Open Access Journals (Sweden)

    E. Honkavaara

    2016-06-01

    Full Text Available Light-weight hyperspectral frame cameras represent novel developments in remote sensing technology. With frame camera technology, when capturing images with stereoscopic overlaps, it is possible to derive 3D hyperspectral reflectance information and 3D geometric data of targets of interest, which enables detailed geometric and radiometric characterization of the object. These technologies are expected to provide efficient tools in various environmental remote sensing applications, such as canopy classification, canopy stress analysis, precision agriculture, and urban material classification. Furthermore, these data sets enable advanced quantitative, physical based retrieval of biophysical and biochemical parameters by model inversion technologies. Objective of this investigation was to study the aspects of capturing hyperspectral reflectance data from unmanned airborne vehicle (UAV and terrestrial platform with novel hyperspectral frame cameras in complex, forested environment.

  15. UAV hyperspectral and lidar data and their fusion for arid and semi-arid land vegetation monitoring

    Science.gov (United States)

    We demonstrate a unique fusion of unmanned aerial vehicle (UAV) lidar and hyperspectral imagery for individual plant species identification and 3D characterization of the earth surface at sub-meter scales in southeastern Arizona, USA. We hypothesized that the fusion of the two different data sources...

  16. Ring-shaped Calorimetry Information for a Neural eGamma Identification with ATLAS Detector

    CERN Document Server

    Da Fonseca Pinto, Joao Victor; The ATLAS collaboration; Oliveira Damazio, Denis; Seixas, Jose

    2016-01-01

    \\title{Ring-shaped Calorimetry Information for a Neural e/$\\gamma$ Identification with ATLAS Detector} After the successful operation of the Large Hadron Collider resulting with the discovery of the Higgs boson, a new data-taking period (Run 2) has started. For the first time, collisions are produced with energies of 13 TeV in the centre of mass. It is foreseen the luminosity increase, reaching values as high as $10^{34}cm^{-2}s^{-1}$ yet in 2015. These changes in experimental conditions bring a proper environment for possible new physics key-findings. ATLAS is the largest LHC detector and was designed for general-purpose physics studies. Many potential physics channels have electrons or photons in their final states. For efficient studies on these channels precise measurement and identification of such particles is necessary. The identification task consists of disentangling those particles (signal) from collimated hadronic jets (background). Reported work concerns the identification process based on the cal...

  17. Enhanced statistical damage identification using frequency-shift information with tunable piezoelectric transducer circuitry

    International Nuclear Information System (INIS)

    Zhao, J; Tang, J; Wang, K W

    2008-01-01

    The frequency-shift-based damage detection method entertains advantages such as global detection capability and easy implementation, but also suffers from drawbacks that include low detection accuracy and sensitivity and the difficulty in identifying damage using a small number of measurable frequencies. Moreover, the damage detection/identification performance is inevitably affected by the uncertainty/variations in the baseline model. In this research, we investigate an enhanced statistical damage identification method using the tunable piezoelectric transducer circuitry. The tunable piezoelectric transducer circuitry can lead to much enriched information on frequency shift (before and after damage occurrence). The circuitry elements, meanwhile, can be directly and accurately measured and thus can be considered uncertainty-free. A statistical damage identification algorithm is formulated which can identify both the mean and variance of the elemental property change. Our analysis indicates that the integration of the tunable piezoelectric transducer circuitry can significantly enhance the robustness of the frequency-shift-based damage identification approach under uncertainty and noise

  18. Common hyperspectral image database design

    Science.gov (United States)

    Tian, Lixun; Liao, Ningfang; Chai, Ali

    2009-11-01

    This paper is to introduce Common hyperspectral image database with a demand-oriented Database design method (CHIDB), which comprehensively set ground-based spectra, standardized hyperspectral cube, spectral analysis together to meet some applications. The paper presents an integrated approach to retrieving spectral and spatial patterns from remotely sensed imagery using state-of-the-art data mining and advanced database technologies, some data mining ideas and functions were associated into CHIDB to make it more suitable to serve in agriculture, geological and environmental areas. A broad range of data from multiple regions of the electromagnetic spectrum is supported, including ultraviolet, visible, near-infrared, thermal infrared, and fluorescence. CHIDB is based on dotnet framework and designed by MVC architecture including five main functional modules: Data importer/exporter, Image/spectrum Viewer, Data Processor, Parameter Extractor, and On-line Analyzer. The original data were all stored in SQL server2008 for efficient search, query and update, and some advance Spectral image data Processing technology are used such as Parallel processing in C#; Finally an application case is presented in agricultural disease detecting area.

  19. Identification of the operating crew's information needs for accident management

    Energy Technology Data Exchange (ETDEWEB)

    Nelson, W.R.; Hanson, D.J.; Ward, L.W.; Solberg, D.E.

    1988-01-01

    While it would be very difficult to predetermine all of the actions required to mitigate the consequences of every potential severe accident for a nuclear power plant, development of additional guidance and training could improve the likelihood that the operating crew would implement effective sever-accident management measures. The US Nuclear Regulatory Commission (NRC) is conducting an Accident Management Research Program that emphasizes the application of severe-accident research results to enhance the capability of the plant operating crew to effectively manage severe accidents. One element of this program includes identification of the information needed by the operating crew in severe-accident situations. This paper discusses a method developed for identifying these information needs and its application. The methodology has been applied to a generic reactor design representing a PWR with a large dry containment. The information needs were identified by systematically determining what information is needed to assess the health of the critical functions, identify the presence of challenges, select strategies, and assess the effectiveness of these strategies. This method allows the systematic identification of information needs for a broad range of severe-accident scenarios and can be validated by exercising the functional models for any specific event sequence.

  20. 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

  1. A New Method of Reliability Evaluation Based on Wavelet Information Entropy for Equipment Condition Identification

    International Nuclear Information System (INIS)

    He, Z J; Zhang, X L; Chen, X F

    2012-01-01

    Aiming at reliability evaluation of condition identification of mechanical equipment, it is necessary to analyze condition monitoring information. A new method of reliability evaluation based on wavelet information entropy extracted from vibration signals of mechanical equipment is proposed. The method is quite different from traditional reliability evaluation models that are dependent on probability statistics analysis of large number sample data. The vibration signals of mechanical equipment were analyzed by means of second generation wavelet package (SGWP). We take relative energy in each frequency band of decomposed signal that equals a percentage of the whole signal energy as probability. Normalized information entropy (IE) is obtained based on the relative energy to describe uncertainty of a system instead of probability. The reliability degree is transformed by the normalized wavelet information entropy. A successful application has been achieved to evaluate the assembled quality reliability for a kind of dismountable disk-drum aero-engine. The reliability degree indicates the assembled quality satisfactorily.

  2. Spherical stochastic neighbor embedding of hyperspectral data

    CSIR Research Space (South Africa)

    Lunga, D

    2012-07-01

    Full Text Available and manifold learning in Euclidean spaces, very few attempts have focused on non-Euclidean spaces. Here, we propose a novel approach that embeds hyperspectral data, transformed into bilateral probability similarities, onto a nonlinear unit norm coordinate...

  3. Target Detection Using an AOTF Hyperspectral Imager

    Science.gov (United States)

    Cheng, L-J.; Mahoney, J.; Reyes, F.; Suiter, H.

    1994-01-01

    This paper reports results of a recent field experiment using a prototype system to evaluate the acousto-optic tunable filter polarimetric hyperspectral imaging technology for target detection applications.

  4. Identification and Characterization of Inter-Organizational Information Flows in the Portuguese National Health Service

    Science.gov (United States)

    Brito, António Carvalho; Cruz-Correia, Ricardo João

    2016-01-01

    Summary Objectives To understand and build a collective vision of all existing institutions in the Portuguese National Health Service as well as to perceive how and how far the interaction between those multiple institutions is supported by Information Systems (IS). Methods Upon identification of the institutions involved in the healthcare process, a set of interviews with experienced people from those institutions was conducted, which produced about five hours of tape. The research was focused exclusively on processes involving two different organizations and any internal processes were altogether excluded from it. Results The study allowed the identification of about 50 recurrent interaction processes, which were classified into four different varieties in accordance with the nature of the information flow: administrative, clinical, identificational and statistical. In addition, these processes were divided in accordance with the way how that integration is achieved, from completely automated to email or telephone-based. Conclusions Funds/Money related processes are technologically more rigid and standardized, whereas auditing and inspection ones are less supported by automatic systems. There emerged an interesting level of sharing and integration in clinical processes, although the integration is mostly made at the interface level. The authors identified 5 particularly relevant and dominant actors (2 classes of individuals and 3 institutions) with which there is a need for coordination and cooperation. The authors consider that, in future works, an effort should be made to provide the various institutions with guidelines/interfaces and prompt such institutions to elaborate upon these. PMID:27999840

  5. Hyperspectral remote sensing techniques applied to the noninvasive investigation of mural paintings: a feasibility study carried out on a wall painting by Beato Angelico in Florence

    Science.gov (United States)

    Cucci, Costanza; Picollo, Marcello; Chiarantini, Leandro; Sereni, Barbara

    2015-06-01

    Nowadays hyperspectral imaging is a well-established methodology for the non-invasive diagnostics of polychrome surfaces, and is increasingly utilized in museums and conservation laboratories for documentation purposes and in support of restoration procedures. However, so far the applications of hyperspectral imaging have been mainly limited to easel paintings or paper-based artifacts. Indeed, specifically designed hyperspectral imagers, are usually used for applications in museum context. These devices work at short-distances from the targets and cover limited size surfaces. Instead, almost still unexplored remain the applications of hyperspectral imaging to the investigations of frescoes and large size mural paintings. For this type of artworks a remote sensing approach, based on sensors capable of acquiring hyperspectral data from distances of the order of tens of meters, is needed. This paper illustrates an application of hyperspectral remote sensing to an important wall-painting by Beato Angelico, located in the San Marco Museum in Florence. Measurements were carried out using a re-adapted version of the Galileo Avionica Multisensor Hyperspectral System (SIM-GA), an avionic hyperspectral imager originally designed for applications from mobile platforms. This system operates in the 400-2500 nm range with over 700 channels, thus guaranteeing acquisition of high resolution hyperspectral data exploitable for materials identification and mapping. In the present application, the SIM-GA device was mounted on a static scanning platform for ground-based applications. The preliminary results obtained on the Angelico's wall-painting are discussed, with highlights on the main technical issues addressed to optimize the SIM-GA system for new applications on cultural assets.

  6. Microscopic hyperspectral imaging studies of normal and diabetic retina of rats

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    A microscopic hyperspectral imager was developed based on the microscopic technology and the spectral imaging technology. Some microscopic hyperspectral images of retina sections of the normal, the diabetic, and the treated rats were collected by the new imager. Single-band images and pseudo-color images of each group were obtained and the typical transmittance spectrums were ex-tracted. The results showed that the transmittance of outer nuclear layer cells of the diabetic group was generally higher than that of the normal. A small absorption peak appeared near the 180th band in the spectrum of the diabetic group and this peak weakened or disappeared in the spectrum of the treated group. Our findings indicate that the microscopic hyperspectral images include wealthy information of retina sections which is helpful for the ophthalmologist to reveal the pathogenesis of diabetic reti-nopathy and explore the therapeutic effect of drugs.

  7. A BIOSENSOR USING COUPLED PLASMON WAVEGUIDE RESONANCE COMBINED WITH HYPERSPECTRAL FLUORESCENCE ANALYSIS

    Directory of Open Access Journals (Sweden)

    CHAN DU

    2014-01-01

    Full Text Available We developed a biosensor that is capable for simultaneous surface plasmon resonance (SPR sensing and hyperspectral fluorescence analysis in this paper. A symmetrical metal-dielectric slab scheme is employed for the excitation of coupled plasmon waveguide resonance (CPWR in the present work. Resonance between surface plasmon mode and the guided waveguide mode generates narrower full width half-maximum of the reflective curves which leads to increased precision for the determination of refractive index over conventional SPR sensors. In addition, CPWR also offers longer surface propagation depths and higher surface electric field strengths that enable the excitation of fluorescence with hyperspectral technique to maintain an appreciable signal-to-noise ratio. The refractive index information obtained from SPR sensing and the chemical properties obtained through hyperspectral fluorescence analysis confirm each other to exclude false-positive or false-negative cases. The sensor provides a comprehensive understanding of the biological events on the sensor chips.

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

    Directory of Open Access Journals (Sweden)

    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.

  9. Low-Complexity Compression Algorithm for Hyperspectral Images Based on Distributed Source Coding

    Directory of Open Access Journals (Sweden)

    Yongjian Nian

    2013-01-01

    Full Text Available A low-complexity compression algorithm for hyperspectral images based on distributed source coding (DSC is proposed in this paper. The proposed distributed compression algorithm can realize both lossless and lossy compression, which is implemented by performing scalar quantization strategy on the original hyperspectral images followed by distributed lossless compression. Multilinear regression model is introduced for distributed lossless compression in order to improve the quality of side information. Optimal quantized step is determined according to the restriction of the correct DSC decoding, which makes the proposed algorithm achieve near lossless compression. Moreover, an effective rate distortion algorithm is introduced for the proposed algorithm to achieve low bit rate. Experimental results show that the compression performance of the proposed algorithm is competitive with that of the state-of-the-art compression algorithms for hyperspectral images.

  10. 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

  11. 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

  12. Orientational imaging of a single plasmonic nanoparticle using dark-field hyperspectral imaging

    Science.gov (United States)

    Mehta, Nishir; Mahigir, Amirreza; Veronis, Georgios; Gartia, Manas Ranjan

    2017-08-01

    Orientation of plasmonic nanostructures is an important feature in many nanoscale applications such as catalyst, biosensors DNA interactions, protein detections, hotspot of surface enhanced Raman spectroscopy (SERS), and fluorescence resonant energy transfer (FRET) experiments. However, due to diffraction limit, it is challenging to obtain the exact orientation of the nanostructure using standard optical microscope. Hyperspectral Imaging Microscopy is a state-of-the-art visualization technology that combines modern optics with hyperspectral imaging and computer system to provide the identification and quantitative spectral analysis of nano- and microscale structures. In this work, initially we use transmitted dark field imaging technique to locate single nanoparticle on a glass substrate. Then we employ hyperspectral imaging technique at the same spot to investigate orientation of single nanoparticle. No special tagging or staining of nanoparticle has been done, as more likely required in traditional microscopy techniques. Different orientations have been identified by carefully understanding and calibrating shift in spectral response from each different orientations of similar sized nanoparticles. Wavelengths recorded are between 300 nm to 900 nm. The orientations measured by hyperspectral microscopy was validated using finite difference time domain (FDTD) electrodynamics calculations and scanning electron microscopy (SEM) analysis. The combination of high resolution nanometer-scale imaging techniques and the modern numerical modeling capacities thus enables a meaningful advance in our knowledge of manipulating and fabricating shaped nanostructures. This work will advance our understanding of the behavior of small nanoparticle clusters useful for sensing, nanomedicine, and surface sciences.

  13. Distributed Parallel Endmember Extraction of Hyperspectral Data Based on Spark

    Directory of Open Access Journals (Sweden)

    Zebin Wu

    2016-01-01

    Full Text Available Due to the increasing dimensionality and volume of remotely sensed hyperspectral data, the development of acceleration techniques for massive hyperspectral image analysis approaches is a very important challenge. Cloud computing offers many possibilities of distributed processing of hyperspectral datasets. This paper proposes a novel distributed parallel endmember extraction method based on iterative error analysis that utilizes cloud computing principles to efficiently process massive hyperspectral data. The proposed method takes advantage of technologies including MapReduce programming model, Hadoop Distributed File System (HDFS, and Apache Spark to realize distributed parallel implementation for hyperspectral endmember extraction, which significantly accelerates the computation of hyperspectral processing and provides high throughput access to large hyperspectral data. The experimental results, which are obtained by extracting endmembers of hyperspectral datasets on a cloud computing platform built on a cluster, demonstrate the effectiveness and computational efficiency of the proposed method.

  14. Manifold learning based feature extraction for classification of hyperspectral data

    CSIR Research Space (South Africa)

    Lunga, D

    2014-01-01

    Full Text Available in analysis of hyperspectral imagery. High spectral resolution and the typically continuous bands of hyperspectral image (HSI) data enable discrimination between spectrally similar targets of interest, provide capability to estimate within pixel abundances...

  15. 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

  16. Multi- and hyperspectral scene modeling

    Science.gov (United States)

    Borel, Christoph C.; Tuttle, Ronald F.

    2011-06-01

    This paper shows how to use a public domain raytracer POV-Ray (Persistence Of Vision Raytracer) to render multiand hyper-spectral scenes. The scripting environment allows automatic changing of the reflectance and transmittance parameters. The radiosity rendering mode allows accurate simulation of multiple-reflections between surfaces and also allows semi-transparent surfaces such as plant leaves. We show that POV-Ray computes occlusion accurately using a test scene with two blocks under a uniform sky. A complex scene representing a plant canopy is generated using a few lines of script. With appropriate rendering settings, shadows cast by leaves are rendered in many bands. Comparing single and multiple reflection renderings, the effect of multiple reflections is clearly visible and accounts for 25% of the overall apparent canopy reflectance in the near infrared.

  17. Red Blood Cell Count Automation Using Microscopic Hyperspectral Imaging Technology.

    Science.gov (United States)

    Li, Qingli; Zhou, Mei; Liu, Hongying; Wang, Yiting; Guo, Fangmin

    2015-12-01

    Red blood cell counts have been proven to be one of the most frequently performed blood tests and are valuable for early diagnosis of some diseases. This paper describes an automated red blood cell counting method based on microscopic hyperspectral imaging technology. Unlike the light microscopy-based red blood count methods, a combined spatial and spectral algorithm is proposed to identify red blood cells by integrating active contour models and automated two-dimensional k-means with spectral angle mapper algorithm. Experimental results show that the proposed algorithm has better performance than spatial based algorithm because the new algorithm can jointly use the spatial and spectral information of blood cells.

  18. Pixelated camouflage patterns from the perspective of hyperspectral imaging

    Science.gov (United States)

    Racek, František; Jobánek, Adam; Baláž, Teodor; Krejčí, Jaroslav

    2016-10-01

    Pixelated camouflage patterns fulfill the role of both principles the matching and the disrupting that are exploited for blending the target into the background. It means that pixelated pattern should respect natural background in spectral and spatial characteristics embodied in micro and macro patterns. The HS imaging plays the similar, however the reverse role in the field of reconnaissance systems. The HS camera fundamentally records and extracts both the spectral and spatial information belonging to the recorded scenery. Therefore, the article deals with problems of hyperspectral (HS) imaging and subsequent processing of HS images of pixelated camouflage patterns which are among others characterized by their specific spatial frequency heterogeneity.

  19. An Unsupervised Deep Hyperspectral Anomaly Detector

    Directory of Open Access Journals (Sweden)

    Ning Ma

    2018-02-01

    Full Text Available Hyperspectral image (HSI based detection has attracted considerable attention recently in agriculture, environmental protection and military applications as different wavelengths of light can be advantageously used to discriminate different types of objects. Unfortunately, estimating the background distribution and the detection of interesting local objects is not straightforward, and anomaly detectors may give false alarms. In this paper, a Deep Belief Network (DBN based anomaly detector is proposed. The high-level features and reconstruction errors are learned through the network in a manner which is not affected by previous background distribution assumption. To reduce contamination by local anomalies, adaptive weights are constructed from reconstruction errors and statistical information. By using the code image which is generated during the inference of DBN and modified by adaptively updated weights, a local Euclidean distance between under test pixels and their neighboring pixels is used to determine the anomaly targets. Experimental results on synthetic and recorded HSI datasets show the performance of proposed method outperforms the classic global Reed-Xiaoli detector (RXD, local RX detector (LRXD and the-state-of-the-art Collaborative Representation detector (CRD.

  20. MWIR hyperspectral imaging with the MIDAS instrument

    Science.gov (United States)

    Honniball, Casey I.; Wright, Rob; Lucey, Paul G.

    2017-02-01

    Hyperspectral imaging (HSI) in the Mid-Wave InfraRed (MWIR, 3-5 microns) can provide information on a variety of science applications from determining the chemical composition of lava lakes on Jupiter's moon Io, to investigating the amount of carbon liberated into the Earth's atmosphere during a wildfire. The limited signal available in the MWIR presents technical challenges to achieving high signal-to-noise ratios, and therefore it is typically necessary to cryogenically cool MWIR instruments. With recent improvements in microbolometer technology and emerging interferometric techniques, we have shown that uncooled microbolometers coupled with a Sagnac interferometer can achieve high signal-to-noise ratios for long-wave infrared HSI. To explore if this technique can be applied to the MWIR, this project, with funding from NASA, has built the Miniaturized Infrared Detector of Atmospheric Species (MIDAS). Standard characterization tests are used to compare MIDAS against a cryogenically cooled photon detector to evaluate the MIDAS instruments' ability to quantify gas concentrations. Atmospheric radiative transfer codes are in development to explore the limitations of MIDAS and identify the range of science objectives that MIDAS will most likely excel at. We will simulate science applications with gas cells filled with varying gas concentrations and varying source temperatures to verify our results from lab characterization and our atmospheric modeling code.

  1. Detection of mechanical injury on pickling cucumbers using near-infrared hyperspectral imaging

    Science.gov (United States)

    Ariana, D.; Lu, R.; Guyer, D.

    2005-11-01

    Automated detection of defects on freshly harvested pickling cucumbers will help the pickle industry provide higher quality pickle products and reduce potential economic losses. Research was conducted on using a hyperspectral imaging system for detecting defects on pickling cucumbers caused by mechanical stress. A near-infrared hyperspectral imaging system was used to capture both spatial and spectral information from cucumbers in the spectral region of 900 - 1700 nm. The system consisted of an imaging spectrograph attached to an InGaAs camera with line-light fiber bundles as an illumination source. Cucumber samples were subjected to two forms of mechanical loading, dropping and rolling, to simulate stress caused by mechanical harvesting. Hyperspectral images were acquired from the cucumbers over time periods of 0, 1, 2, 3, and 6 days after mechanical stress. Hyperspectral image processing methods, including principal component analysis and wavelength selection, were developed to separate normal and mechanically injured cucumbers. Results showed that reflectance from normal or non-bruised cucumbers was consistently higher than that from bruised cucumbers. The spectral region between 950 and 1350 nm was found to be most effective for bruise detection. The hyperspectral imaging system detected all mechanically injured cucumbers immediately after they were bruised. The overall detection accuracy was 97% within two hours of bruising and it was lower as time progressed. Lower detection accuracies for the prolonged times after bruising were attributed to the self- healing of the bruised tissue after mechanical injury. This research demonstrated that hyperspectral imaging is useful for detecting mechanical injury on pickling cucumbers.

  2. The software and algorithms for hyperspectral data processing

    Science.gov (United States)

    Shyrayeva, Anhelina; Martinov, Anton; Ivanov, Victor; Katkovsky, Leonid

    2017-04-01

    Hyperspectral remote sensing technique is widely used for collecting and processing -information about the Earth's surface objects. Hyperspectral data are combined to form a three-dimensional (x, y, λ) data cube. Department of Aerospace Research of the Institute of Applied Physical Problems of the Belarusian State University presents a general model of the software for hyperspectral image data analysis and processing. The software runs in Windows XP/7/8/8.1/10 environment on any personal computer. This complex has been has been written in C++ language using QT framework and OpenGL for graphical data visualization. The software has flexible structure that consists of a set of independent plugins. Each plugin was compiled as Qt Plugin and represents Windows Dynamic library (dll). Plugins can be categorized in terms of data reading types, data visualization (3D, 2D, 1D) and data processing The software has various in-built functions for statistical and mathematical analysis, signal processing functions like direct smoothing function for moving average, Savitzky-Golay smoothing technique, RGB correction, histogram transformation, and atmospheric correction. The software provides two author's engineering techniques for the solution of atmospheric correction problem: iteration method of refinement of spectral albedo's parameters using Libradtran and analytical least square method. The main advantages of these methods are high rate of processing (several minutes for 1 GB data) and low relative error in albedo retrieval (less than 15%). Also, the software supports work with spectral libraries, region of interest (ROI) selection, spectral analysis such as cluster-type image classification and automatic hypercube spectrum comparison by similarity criterion with similar ones from spectral libraries, and vice versa. The software deals with different kinds of spectral information in order to identify and distinguish spectrally unique materials. Also, the following advantages

  3. Ring-shaped Calorimetry Information for a Neural EGamma Identification with ATLAS Detector

    CERN Document Server

    AUTHOR|(SzGeCERN)762100; The ATLAS collaboration

    2016-01-01

    This work concerns the identification process of electrons based only on calorimeter information. It is proposed the usage of ring-shaped description for a region of interest of the calorimeter which explores the shower shape propagation throughout the ATLAS calorimeters. This information is fed into a multivariate discriminator, currently an artificial neural network, responsible for hypothesis testing. The concept is evaluated for online selection (trigger), used for reducing storage rate into viable levels while preserving collision events containing desired signals. Preliminary results from Monte Carlo data point out that the background rejection can be reduced by as much as 50 % over the current method used in the High-Level Trigger, allowing for high-latency reconstruction algorithms such as tracking to run over at a later stage of the trigger.

  4. Ring-shaped Calorimetry Information for a Neural Egamma Identification with ATLAS Detector

    International Nuclear Information System (INIS)

    Victor da Fonseca Pinto, João

    2016-01-01

    This article presents the identification process of electrons based only on calorimeter information. It is based on the usage of ring-shaped description for a region of interest of the calorimeter which explores the shower shape propagation throughout the ATLAS calorimeters. This information is fed into a multivariate discriminator, currently an artificial neural network, responsible for hypothesis testing. The concept is evaluated for online selection (trigger), used for reducing storage rate into viable levels while preserving collision events containing desired signals. Preliminary results from Monte Carlo simulation data indicate that the background rejection can be reduced by as much as 50 % over the current method used in the High-Level Trigger, allowing for high-latency reconstruction algorithms such as tracking to run at a later stage of the trigger. (paper)

  5. A Time-Space Domain Information Fusion Method for Specific Emitter Identification Based on Dempster-Shafer Evidence Theory.

    Science.gov (United States)

    Jiang, Wen; Cao, Ying; Yang, Lin; He, Zichang

    2017-08-28

    Specific emitter identification plays an important role in contemporary military affairs. However, most of the existing specific emitter identification methods haven't taken into account the processing of uncertain information. Therefore, this paper proposes a time-space domain information fusion method based on Dempster-Shafer evidence theory, which has the ability to deal with uncertain information in the process of specific emitter identification. In this paper, radars will generate a group of evidence respectively based on the information they obtained, and our main task is to fuse the multiple groups of evidence to get a reasonable result. Within the framework of recursive centralized fusion model, the proposed method incorporates a correlation coefficient, which measures the relevance between evidence and a quantum mechanical approach, which is based on the parameters of radar itself. The simulation results of an illustrative example demonstrate that the proposed method can effectively deal with uncertain information and get a reasonable recognition result.

  6. Brand crisis management: the use of information for prevention, identification and management

    Directory of Open Access Journals (Sweden)

    Alexandre Borba Salvador

    2018-01-01

    Full Text Available Purpose – To understand, from the management perspective, the use of information on brand crisis management in moments involving prevention, identification and management. Design/methodology/approach – This article is the result of an exploratory, qualitative study. Primary data was collected through interviews with marketing executives and crisis management specialists. Findings – We concluded that managers use information in very different ways, and, taking possession of information and decisionmaking attitudes into account, four groups of crisis managers were found. Originality/value – In order to contribute to the expansion of academic knowledge in the field of marketing administration and brand crisis management, this study presented the crossing of three different fields (information systems, brand crisis management and organizational development to structure knowledge concerning brand crisis management. From the managerial perspective, certain crises could be avoided by monitoring internal and external information, and by identifying problems in their early stages. From the social perspective, reducing brand crisis cases positively affects society as a whole (organizations and individuals.

  7. Text de-identification for privacy protection: a study of its impact on clinical text information content.

    Science.gov (United States)

    Meystre, Stéphane M; Ferrández, Óscar; Friedlin, F Jeffrey; South, Brett R; Shen, Shuying; Samore, Matthew H

    2014-08-01

    As more and more electronic clinical information is becoming easier to access for secondary uses such as clinical research, approaches that enable faster and more collaborative research while protecting patient privacy and confidentiality are becoming more important. Clinical text de-identification offers such advantages but is typically a tedious manual process. Automated Natural Language Processing (NLP) methods can alleviate this process, but their impact on subsequent uses of the automatically de-identified clinical narratives has only barely been investigated. In the context of a larger project to develop and investigate automated text de-identification for Veterans Health Administration (VHA) clinical notes, we studied the impact of automated text de-identification on clinical information in a stepwise manner. Our approach started with a high-level assessment of clinical notes informativeness and formatting, and ended with a detailed study of the overlap of select clinical information types and Protected Health Information (PHI). To investigate the informativeness (i.e., document type information, select clinical data types, and interpretation or conclusion) of VHA clinical notes, we used five different existing text de-identification systems. The informativeness was only minimally altered by these systems while formatting was only modified by one system. To examine the impact of de-identification on clinical information extraction, we compared counts of SNOMED-CT concepts found by an open source information extraction application in the original (i.e., not de-identified) version of a corpus of VHA clinical notes, and in the same corpus after de-identification. Only about 1.2-3% less SNOMED-CT concepts were found in de-identified versions of our corpus, and many of these concepts were PHI that was erroneously identified as clinical information. To study this impact in more details and assess how generalizable our findings were, we examined the overlap between

  8. Hyperspectral remote sensing of plant pigments.

    Science.gov (United States)

    Blackburn, George Alan

    2007-01-01

    The dynamics of pigment concentrations are diagnostic of a range of plant physiological properties and processes. This paper appraises the developing technologies and analytical methods for quantifying pigments non-destructively and repeatedly across a range of spatial scales using hyperspectral remote sensing. Progress in deriving predictive relationships between various characteristics and transforms of hyperspectral reflectance data are evaluated and the roles of leaf and canopy radiative transfer models are reviewed. Requirements are identified for more extensive intercomparisons of different approaches and for further work on the strategies for interpreting canopy scale data. The paper examines the prospects for extending research to the wider range of pigments in addition to chlorophyll, testing emerging methods of hyperspectral analysis and exploring the fusion of hyperspectral and LIDAR remote sensing. In spite of these opportunities for further development and the refinement of techniques, current evidence of an expanding range of applications in the ecophysiological, environmental, agricultural, and forestry sciences highlights the growing value of hyperspectral remote sensing of plant pigments.

  9. Design and Test of Portable Hyperspectral Imaging Spectrometer

    Directory of Open Access Journals (Sweden)

    Chunbo Zou

    2017-01-01

    Full Text Available We design and implement a portable hyperspectral imaging spectrometer, which has high spectral resolution, high spatial resolution, small volume, and low weight. The flight test has been conducted, and the hyperspectral images are acquired successfully. To achieve high performance, small volume, and regular appearance, an improved Dyson structure is designed and used in the hyperspectral imaging spectrometer. The hyperspectral imaging spectrometer is suitable for the small platform such as CubeSat and UAV (unmanned aerial vehicle, and it is also convenient to use for hyperspectral imaging acquiring in the laboratory and the field.

  10. A new hyperspectral image compression paradigm based on fusion

    Science.gov (United States)

    Guerra, Raúl; Melián, José; López, Sebastián.; Sarmiento, Roberto

    2016-10-01

    The on-board compression of remote sensed hyperspectral images is an important task nowadays. One of the main difficulties is that the compression of these images must be performed in the satellite which carries the hyperspectral sensor. Hence, this process must be performed by space qualified hardware, having area, power and speed limitations. Moreover, it is important to achieve high compression ratios without compromising the quality of the decompress image. In this manuscript we proposed a new methodology for compressing hyperspectral images based on hyperspectral image fusion concepts. The proposed compression process has two independent steps. The first one is to spatially degrade the remote sensed hyperspectral image to obtain a low resolution hyperspectral image. The second step is to spectrally degrade the remote sensed hyperspectral image to obtain a high resolution multispectral image. These two degraded images are then send to the earth surface, where they must be fused using a fusion algorithm for hyperspectral and multispectral image, in order to recover the remote sensed hyperspectral image. The main advantage of the proposed methodology for compressing remote sensed hyperspectral images is that the compression process, which must be performed on-board, becomes very simple, being the fusion process used to reconstruct image the more complex one. An extra advantage is that the compression ratio can be fixed in advanced. Many simulations have been performed using different fusion algorithms and different methodologies for degrading the hyperspectral image. The results obtained in the simulations performed corroborate the benefits of the proposed methodology.

  11. A coarse-to-fine approach for medical hyperspectral image classification with sparse representation

    Science.gov (United States)

    Chang, Lan; Zhang, Mengmeng; Li, Wei

    2017-10-01

    A coarse-to-fine approach with sparse representation is proposed for medical hyperspectral image classification in this work. Segmentation technique with different scales is employed to exploit edges of the input image, where coarse super-pixel patches provide global classification information while fine ones further provide detail information. Different from common RGB image, hyperspectral image has multi bands to adjust the cluster center with more high precision. After segmentation, each super pixel is classified by recently-developed sparse representation-based classification (SRC), which assigns label for testing samples in one local patch by means of sparse linear combination of all the training samples. Furthermore, segmentation with multiple scales is employed because single scale is not suitable for complicate distribution of medical hyperspectral imagery. Finally, classification results for different sizes of super pixel are fused by some fusion strategy, offering at least two benefits: (1) the final result is obviously superior to that of segmentation with single scale, and (2) the fusion process significantly simplifies the choice of scales. Experimental results using real medical hyperspectral images demonstrate that the proposed method outperforms the state-of-the-art SRC.

  12. Innovative Hyperspectral Imaging-Based Techniques for Quality Evaluation of Fruits and Vegetables: A Review

    Directory of Open Access Journals (Sweden)

    Yuzhen Lu

    2017-02-01

    Full Text Available New, non-destructive sensing techniques for fast and more effective quality assessment of fruits and vegetables are needed to meet the ever-increasing consumer demand for better, more consistent and safer food products. Over the past 15 years, hyperspectral imaging has emerged as a new generation of sensing technology for non-destructive food quality and safety evaluation, because it integrates the major features of imaging and spectroscopy, thus enabling the acquisition of both spectral and spatial information from an object simultaneously. This paper first provides a brief overview of hyperspectral imaging configurations and common sensing modes used for food quality and safety evaluation. The paper is, however, focused on the three innovative hyperspectral imaging-based techniques or sensing platforms, i.e., spectral scattering, integrated reflectance and transmittance, and spatially-resolved spectroscopy, which have been developed in our laboratory for property and quality evaluation of fruits, vegetables and other food products. The basic principle and instrumentation of each technique are described, followed by the mathematical methods for processing and extracting critical information from the acquired data. Applications of these techniques for property and quality evaluation of fruits and vegetables are then presented. Finally, concluding remarks are given on future research needs to move forward these hyperspectral imaging techniques.

  13. Clusters versus GPUs for Parallel Target and Anomaly Detection in Hyperspectral Images

    Directory of Open Access Journals (Sweden)

    Antonio Plaza

    2010-01-01

    Full Text Available Remotely sensed hyperspectral sensors provide image data containing rich information in both the spatial and the spectral domain, and this information can be used to address detection tasks in many applications. In many surveillance applications, the size of the objects (targets searched for constitutes a very small fraction of the total search area and the spectral signatures associated to the targets are generally different from those of the background, hence the targets can be seen as anomalies. In hyperspectral imaging, many algorithms have been proposed for automatic target and anomaly detection. Given the dimensionality of hyperspectral scenes, these techniques can be time-consuming and difficult to apply in applications requiring real-time performance. In this paper, we develop several new parallel implementations of automatic target and anomaly detection algorithms. The proposed parallel algorithms are quantitatively evaluated using hyperspectral data collected by the NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS system over theWorld Trade Center (WTC in New York, five days after the terrorist attacks that collapsed the two main towers in theWTC complex.

  14. Clusters versus GPUs for Parallel Target and Anomaly Detection in Hyperspectral Images

    Directory of Open Access Journals (Sweden)

    Paz Abel

    2010-01-01

    Full Text Available Abstract Remotely sensed hyperspectral sensors provide image data containing rich information in both the spatial and the spectral domain, and this information can be used to address detection tasks in many applications. In many surveillance applications, the size of the objects (targets searched for constitutes a very small fraction of the total search area and the spectral signatures associated to the targets are generally different from those of the background, hence the targets can be seen as anomalies. In hyperspectral imaging, many algorithms have been proposed for automatic target and anomaly detection. Given the dimensionality of hyperspectral scenes, these techniques can be time-consuming and difficult to apply in applications requiring real-time performance. In this paper, we develop several new parallel implementations of automatic target and anomaly detection algorithms. The proposed parallel algorithms are quantitatively evaluated using hyperspectral data collected by the NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS system over theWorld Trade Center (WTC in New York, five days after the terrorist attacks that collapsed the two main towers in theWTC complex.

  15. Lithological mapping of Kanjamalai hill using hyperspectral remote sensing tools in Salem district, Tamil Nadu, India

    Science.gov (United States)

    Arulbalaji, Palanisamy; Balasubramanian, Gurugnanam

    2017-07-01

    This study uses advanced spaceborne thermal emission and reflection radiometer (ASTER) hyperspectral remote sensing techniques to discriminate rock types composing Kanjamalai hill located in the Salem district of Tamil Nadu, India. Kanjamalai hill is of particular interest because it contains economically viable iron ore deposits. ASTER hyperspectral data were subjected to principal component analysis (PCA), independent component analysis (ICA), and minimum noise fraction (MNF) to improve identification of lithologies remotely and to compare these digital data results with published geologic maps. Hyperspectral remote sensing analysis indicates that PCA (R∶G∶B=2∶1∶3), MNF (R∶G∶B=3∶2∶1), and ICA (R∶G∶B=1∶3∶2) provide the best band combination for effective discrimination of lithological rock types composing Kanjamalai hill. The remote sensing-derived lithological map compares favorably with a published geological map from Geological Survey of India and has been verified with ground truth field investigations. Therefore, ASTER data-based lithological mapping provides fast, cost-effective, and accurate geologic data useful for lithological discrimination and identification of ore deposits.

  16. The Influence of High-Frequency Envelope Information on Low-Frequency Vowel Identification in Noise.

    Directory of Open Access Journals (Sweden)

    Wiebke Schubotz

    Full Text Available Vowel identification in noise using consonant-vowel-consonant (CVC logatomes was used to investigate a possible interplay of speech information from different frequency regions. It was hypothesized that the periodicity conveyed by the temporal envelope of a high frequency stimulus can enhance the use of the information carried by auditory channels in the low-frequency region that share the same periodicity. It was further hypothesized that this acts as a strobe-like mechanism and would increase the signal-to-noise ratio for the voiced parts of the CVCs. In a first experiment, different high-frequency cues were provided to test this hypothesis, whereas a second experiment examined more closely the role of amplitude modulations and intact phase information within the high-frequency region (4-8 kHz. CVCs were either natural or vocoded speech (both limited to a low-pass cutoff-frequency of 2.5 kHz and were presented in stationary 3-kHz low-pass filtered masking noise. The experimental results did not support the hypothesized use of periodicity information for aiding low-frequency perception.

  17. The Influence of High-Frequency Envelope Information on Low-Frequency Vowel Identification in Noise.

    Science.gov (United States)

    Schubotz, Wiebke; Brand, Thomas; Kollmeier, Birger; Ewert, Stephan D

    2016-01-01

    Vowel identification in noise using consonant-vowel-consonant (CVC) logatomes was used to investigate a possible interplay of speech information from different frequency regions. It was hypothesized that the periodicity conveyed by the temporal envelope of a high frequency stimulus can enhance the use of the information carried by auditory channels in the low-frequency region that share the same periodicity. It was further hypothesized that this acts as a strobe-like mechanism and would increase the signal-to-noise ratio for the voiced parts of the CVCs. In a first experiment, different high-frequency cues were provided to test this hypothesis, whereas a second experiment examined more closely the role of amplitude modulations and intact phase information within the high-frequency region (4-8 kHz). CVCs were either natural or vocoded speech (both limited to a low-pass cutoff-frequency of 2.5 kHz) and were presented in stationary 3-kHz low-pass filtered masking noise. The experimental results did not support the hypothesized use of periodicity information for aiding low-frequency perception.

  18. The hyperspectral imaging trade-off

    DEFF Research Database (Denmark)

    Carstensen, Jens Michael

    , this will be the standard situation, and it enables the detection of small spectral features like peaks, valleys and shoulders for a wide range of chemistries. Everything else being equal this is what you would wish for, and hyperspectral imaging is often used in research and in remote sensing because of the needs and cost......Although it has no clear-cut definition, hyperspectral imaging in the UV-Visible-NIR wavelength region seems to mean spectral image sampling in bands from 10 nm width or narrower that enables spectral reconstruction over some wavelength interval. For non-imaging spectral applications...... structures in these projects. However, hyperspectral imaging is a sampling choice within spectral imaging that typically will impose some trade-offs, and these trade-offs will not be optimal for many applications. The purpose of this presentation is to point out and increase the awareness of these trade...

  19. Manifold structure preservative for hyperspectral target detection

    Science.gov (United States)

    Imani, Maryam

    2018-05-01

    A nonparametric method termed as manifold structure preservative (MSP) is proposed in this paper for hyperspectral target detection. MSP transforms the feature space of data to maximize the separation between target and background signals. Moreover, it minimizes the reconstruction error of targets and preserves the topological structure of data in the projected feature space. MSP does not need to consider any distribution for target and background data. So, it can achieve accurate results in real scenarios due to avoiding unreliable assumptions. The proposed MSP detector is compared to several popular detectors and the experiments on a synthetic data and two real hyperspectral images indicate the superior ability of it in target detection.

  20. Temporal Variability of Observed and Simulated Hyperspectral Earth Reflectance

    Science.gov (United States)

    Roberts, Yolanda; Pilewskie, Peter; Kindel, Bruce; Feldman, Daniel; Collins, William D.

    2012-01-01

    series analysis of the PC scores using techniques such as Singular Spectrum Analysis (SSA) and Multichannel SSA will provide information about the temporal variability of the dominant variables. Quantitative comparison techniques can evaluate how well the OSSE reproduces the temporal variability observed by SCIAMACHY spectral reflectance measurements during the first decade of the 21st century. PCA of OSSE-simulated reflectance can also be used to study how the dominant spectral variables change on centennial scales for forced and unforced climate change scenarios. To have confidence in OSSE predictions of the spectral variability of hyperspectral reflectance, it is first necessary for us to evaluate the degree to which the OSSE simulations are able to reproduce the Earth?s present-day spectral variability.

  1. Mapping of information and identification of construction waste at project life cycle

    Science.gov (United States)

    Wibowo, Mochamad Agung; Handayani, Naniek Utami; Nurdiana, Asri; Sholeh, Moh Nur; Pamungkas, Gita Silvia

    2018-03-01

    The development of construction project towards green construction is needed in order to improve the efficiency of construction projects. One that needs to be minimized is construction waste. Construction waste is waste generated from construction project activities, both solid waste and non solid waste. More specifically, the waste happens at every phase of the project life cycle. Project life cycle are the stage of idea, design, construction, and operation/maintenance. Each phase is managed by different stakeholders. Therefore it requires special handling from the involved stakeholders. The objective of the study is to map the information and identify the waste at each phase of the project life cycle. The purpose of mapping is to figure out the process of information and product flow and with its timeline. This mapping used Value Stream Mapping (VSM). Identification of waste was done by distributing questionnaire to respondents to know the waste according to owner, consultant planner, contractor, and supervisory consultant. The result of the study is the mapping of information flow and product flow at the phases of idea, design, construction, and operation/ maintenance.

  2. Comparison of Accuracy and Speed of Information Identification by Nonpathologists in Synoptic Reports With Different Formats.

    Science.gov (United States)

    Renshaw, Andrew A; Gould, Edwin W

    2017-03-01

    - The College of American Pathologists requires synoptic reports for specific types of pathology reports. - To compare the accuracy and speed of information retrieval in synoptic reports of different formats. - We assessed the performance of 28 nonpathologists from 4 different types of users (cancer registrars, MDs, medical non-MDs, and nonmedical) at identifying specific information in various formatted synoptic reports, using a computerized quiz that measured both accuracy and speed. - There was no significant difference in the accuracy of data identification for any user group or in any format. While there were significant differences in raw time between users, these were eliminated when normalized times were used. Compared with the standard format of a required data element (RDE) and response on 1 line, both a list of responses without an RDE (21%, P RDE header on one line, response indented on the second line) (12%, P RDE response pairs in a random order were significantly slower (16%, P < .001). - There are significant differences in ease of use by nonpathologists between different synoptic report formats. Such information may be useful in deciding between different format options.

  3. INFLUENCE OF THE VIEWING GEOMETRY WITHIN HYPERSPECTRAL IMAGES RETRIEVED FROM UAV SNAPSHOT CAMERAS

    Directory of Open Access Journals (Sweden)

    H. Aasen

    2016-06-01

    Full Text Available Hyperspectral data has great potential for vegetation parameter retrieval. However, due to angular effects resulting from different sun-surface-sensor geometries, objects might appear differently depending on the position of an object within the field of view of a sensor. Recently, lightweight snapshot cameras have been introduced, which capture hyperspectral information in two spatial and one spectral dimension and can be mounted on unmanned aerial vehicles. This study investigates the influence of the different viewing geometries within an image on the apparent hyperspectral reflection retrieved by these sensors. Additionally, it is evaluated how hyperspectral vegetation indices like the NDVI are effected by the angular effects within a single image and if the viewing geometry influences the apparent heterogeneity with an area of interest. The study is carried out for a barley canopy at booting stage. The results show significant influences of the position of the area of interest within the image. The red region of the spectrum is more influenced by the position than the near infrared. The ability of the NDVI to compensate these effects was limited to the capturing positions close to nadir. The apparent heterogeneity of the area of interest is the highest close to a nadir.

  4. An Intraoperative Visualization System Using Hyperspectral Imaging to Aid in Brain Tumor Delineation

    Directory of Open Access Journals (Sweden)

    Himar Fabelo

    2018-02-01

    Full Text Available Hyperspectral imaging (HSI allows for the acquisition of large numbers of spectral bands throughout the electromagnetic spectrum (within and beyond the visual range with respect to the surface of scenes captured by sensors. Using this information and a set of complex classification algorithms, it is possible to determine which material or substance is located in each pixel. The work presented in this paper aims to exploit the characteristics of HSI to develop a demonstrator capable of delineating tumor tissue from brain tissue during neurosurgical operations. Improved delineation of tumor boundaries is expected to improve the results of surgery. The developed demonstrator is composed of two hyperspectral cameras covering a spectral range of 400–1700 nm. Furthermore, a hardware accelerator connected to a control unit is used to speed up the hyperspectral brain cancer detection algorithm to achieve processing during the time of surgery. A labeled dataset comprised of more than 300,000 spectral signatures is used as the training dataset for the supervised stage of the classification algorithm. In this preliminary study, thematic maps obtained from a validation database of seven hyperspectral images of in vivo brain tissue captured and processed during neurosurgical operations demonstrate that the system is able to discriminate between normal and tumor tissue in the brain. The results can be provided during the surgical procedure (~1 min, making it a practical system for neurosurgeons to use in the near future to improve excision and potentially improve patient outcomes.

  5. Detecting Sulfuric and Nitric Acid Rain Stresses on Quercus glauca through Hyperspectral Responses.

    Science.gov (United States)

    Wang, Shanqian; Zhang, Xiuying; Ma, Yuandan; Li, Xinhui; Cheng, Min; Zhang, Xiaomin; Liu, Lei

    2018-03-09

    Acid rain, which has become one of the most severe global environmental issues, is detrimental to plant growth. However, effective methods for monitoring plant responses to acid rain stress are currently lacking. The hyperspectral technique provides a cost-effective and nondestructive way to diagnose acid rain stresses. Taking a widely distributed species ( Quercus glauca ) in Southern China as an example, this study aims to monitor the hyperspectral responses of Q. glauca to simulated sulfuric acid rain (SAR) and nitric acid rain (NAR). A total of 15 periods of leaf hyperspectral data under four pH levels of SAR and NAR were obtained during the experiment. The results showed that hyperspectral information could be used to distinguish plant responses under acid rain stress. An index (green peak area index, GPAI) was proposed to indicate acid rain stresses, based on the significantly variations in the region of 500-660 nm. Light acid rain (pH 4.5 SAR and NAR) promoted Q. glauca growth relative to the control groups (pH 5.6 SAR and NAR); moderate acid rain (pH 3.0 SAR) firstly promoted and then inhibited plant growth, while pH 3.0 NAR showed mild inhibitory effects during the experiment; and heavy acid rain (pH 2.0) significantly inhibited plant growth. Compared with NAR, SAR induced more serious damages to Q. glauca . These results could help monitor acid rain stress on plants on a regional scale using remote sensing techniques.

  6. Hyperspectral Imaging and K-Means Classification for Histologic Evaluation of Ductal Carcinoma In Situ

    Directory of Open Access Journals (Sweden)

    Yasser Khouj

    2018-02-01

    Full Text Available Hyperspectral imaging (HSI is a non-invasive optical imaging modality that shows the potential to aid pathologists in breast cancer diagnoses cases. In this study, breast cancer tissues from different patients were imaged by a hyperspectral system to detect spectral differences between normal and breast cancer tissues. Tissue samples mounted on slides were identified from 10 different patients. Samples from each patient included both normal and ductal carcinoma tissue, both stained with hematoxylin and eosin stain and unstained. Slides were imaged using a snapshot HSI system, and the spectral reflectance differences were evaluated. Analysis of the spectral reflectance values indicated that wavelengths near 550 nm showed the best differentiation between tissue types. This information was used to train image processing algorithms using supervised and unsupervised data. The K-means method was applied to the hyperspectral data cubes, and successfully detected spectral tissue differences with sensitivity of 85.45%, and specificity of 94.64% with true negative rate of 95.8%, and false positive rate of 4.2%. These results were verified by ground-truth marking of the tissue samples by a pathologist. In the hyperspectral image analysis, the image processing algorithm, K-means, shows the greatest potential for building a semi-automated system that could identify and sort between normal and ductal carcinoma in situ tissues.

  7. Use of hyperspectral imaging technology to develop a diagnostic support system for gastric cancer

    Science.gov (United States)

    Goto, Atsushi; Nishikawa, Jun; Kiyotoki, Shu; Nakamura, Munetaka; Nishimura, Junichi; Okamoto, Takeshi; Ogihara, Hiroyuki; Fujita, Yusuke; Hamamoto, Yoshihiko; Sakaida, Isao

    2015-01-01

    Hyperspectral imaging (HSI) is a new technology that obtains spectroscopic information and renders it in image form. This study examined the difference in the spectral reflectance (SR) of gastric tumors and normal mucosa recorded with a hyperspectral camera equipped with HSI technology and attempted to determine the specific wavelength that is useful for the diagnosis of gastric cancer. A total of 104 gastric tumors removed by endoscopic submucosal dissection from 96 patients at Yamaguchi University Hospital were recorded using a hyperspectral camera. We determined the optimal wavelength and the cut-off value for differentiating tumors from normal mucosa to establish a diagnostic algorithm. We also attempted to highlight tumors by image processing using the hyperspectral camera's analysis software. A wavelength of 770 nm and a cut-off value of 1/4 the corrected SR were selected as the respective optimal wavelength and cut-off values. The rates of sensitivity, specificity, and accuracy of the algorithm's diagnostic capability were 71%, 98%, and 85%, respectively. It was possible to enhance tumors by image processing at the 770-nm wavelength. HSI can be used to measure the SR in gastric tumors and to differentiate between tumorous and normal mucosa.

  8. Hyperspectral Imaging and K-Means Classification for Histologic Evaluation of Ductal Carcinoma In Situ.

    Science.gov (United States)

    Khouj, Yasser; Dawson, Jeremy; Coad, James; Vona-Davis, Linda

    2018-01-01

    Hyperspectral imaging (HSI) is a non-invasive optical imaging modality that shows the potential to aid pathologists in breast cancer diagnoses cases. In this study, breast cancer tissues from different patients were imaged by a hyperspectral system to detect spectral differences between normal and breast cancer tissues. Tissue samples mounted on slides were identified from 10 different patients. Samples from each patient included both normal and ductal carcinoma tissue, both stained with hematoxylin and eosin stain and unstained. Slides were imaged using a snapshot HSI system, and the spectral reflectance differences were evaluated. Analysis of the spectral reflectance values indicated that wavelengths near 550 nm showed the best differentiation between tissue types. This information was used to train image processing algorithms using supervised and unsupervised data. The K-means method was applied to the hyperspectral data cubes, and successfully detected spectral tissue differences with sensitivity of 85.45%, and specificity of 94.64% with true negative rate of 95.8%, and false positive rate of 4.2%. These results were verified by ground-truth marking of the tissue samples by a pathologist. In the hyperspectral image analysis, the image processing algorithm, K-means, shows the greatest potential for building a semi-automated system that could identify and sort between normal and ductal carcinoma in situ tissues.

  9. Hyperspectral and LiDAR remote sensing of fire fuels in Hawaii Volcanoes National Park.

    Science.gov (United States)

    Varga, Timothy A; Asner, Gregory P

    2008-04-01

    Alien invasive grasses threaten to transform Hawaiian ecosystems through the alteration of ecosystem dynamics, especially the creation or intensification of a fire cycle. Across sub-montane ecosystems of Hawaii Volcanoes National Park on Hawaii Island, we quantified fine fuels and fire spread potential of invasive grasses using a combination of airborne hyperspectral and light detection and ranging (LiDAR) measurements. Across a gradient from forest to savanna to shrubland, automated mixture analysis of hyperspectral data provided spatially explicit fractional cover estimates of photosynthetic vegetation, non-photosynthetic vegetation, and bare substrate and shade. Small-footprint LiDAR provided measurements of vegetation height along this gradient of ecosystems. Through the fusion of hyperspectral and LiDAR data, a new fire fuel index (FFI) was developed to model the three-dimensional volume of grass fuels. Regionally, savanna ecosystems had the highest volumes of fire fuels, averaging 20% across the ecosystem and frequently filling all of the three-dimensional space represented by each image pixel. The forest and shrubland ecosystems had lower FFI values, averaging 4.4% and 8.4%, respectively. The results indicate that the fusion of hyperspectral and LiDAR remote sensing can provide unique information on the three-dimensional properties of ecosystems, their flammability, and the potential for fire spread.

  10. Assimilation of SAPHIR radiance: impact on hyperspectral radiances in 4D-VAR

    Science.gov (United States)

    Indira Rani, S.; Doherty, Amy; Atkinson, Nigel; Bell, William; Newman, Stuart; Renshaw, Richard; George, John P.; Rajagopal, E. N.

    2016-04-01

    Assimilation of a new observation dataset in an NWP system may affect the quality of an existing observation data set against the model background (short forecast), which in-turn influence the use of an existing observation in the NWP system. Effect of the use of one data set on the use of another data set can be quantified as positive, negative or neutral. Impact of the addition of new dataset is defined as positive if the number of assimilated observations of an existing type of observation increases, and bias and standard deviation decreases compared to the control (without the new dataset) experiment. Recently a new dataset, Megha Tropiques SAPHIR radiances, which provides atmospheric humidity information, is added in the Unified Model 4D-VAR assimilation system. In this paper we discuss the impact of SAPHIR on the assimilation of hyper-spectral radiances like AIRS, IASI and CrIS. Though SAPHIR is a Microwave instrument, its impact can be clearly seen in the use of hyper-spectral radiances in the 4D-VAR data assimilation systems in addition to other Microwave and InfraRed observation. SAPHIR assimilation decreased the standard deviation of the spectral channels of wave number from 650 -1600 cm-1 in all the three hyperspectral radiances. Similar impact on the hyperspectral radiances can be seen due to the assimilation of other Microwave radiances like from AMSR2 and SSMIS Imager.

  11. Contrast based band selection for optimized weathered oil detection in hyperspectral images

    Science.gov (United States)

    Levaux, Florian; Bostater, Charles R., Jr.; Neyt, Xavier

    2012-09-01

    Hyperspectral imagery offers unique benefits for detection of land and water features due to the information contained in reflectance signatures such as the bi-directional reflectance distribution function or BRDF. The reflectance signature directly shows the relative absorption and backscattering features of targets. These features can be very useful in shoreline monitoring or surveillance applications, for example to detect weathered oil. In real-time detection applications, processing of hyperspectral data can be an important tool and Optimal band selection is thus important in real time applications in order to select the essential bands using the absorption and backscatter information. In the present paper, band selection is based upon the optimization of target detection using contrast algorithms. The common definition of the contrast (using only one band out of all possible combinations available within a hyperspectral image) is generalized in order to consider all the possible combinations of wavelength dependent contrasts using hyperspectral images. The inflection (defined here as an approximation of the second derivative) is also used in order to enhance the variations in the reflectance spectra as well as in the contrast spectrua in order to assist in optimal band selection. The results of the selection in term of target detection (false alarms and missed detection) are also compared with a previous method to perform feature detection, namely the matched filter. In this paper, imagery is acquired using a pushbroom hyperspectral sensor mounted at the bow of a small vessel. The sensor is mechanically rotated using an optical rotation stage. This opto-mechanical scanning system produces hyperspectral images with pixel sizes on the order of mm to cm scales, depending upon the distance between the sensor and the shoreline being monitored. The motion of the platform during the acquisition induces distortions in the collected HSI imagery. It is therefore

  12. 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.

  13. a Spatio-Spectral Camera for High Resolution Hyperspectral Imaging

    Science.gov (United States)

    Livens, S.; Pauly, K.; Baeck, P.; Blommaert, J.; Nuyts, D.; Zender, J.; Delauré, B.

    2017-08-01

    Imaging with a conventional frame camera from a moving remotely piloted aircraft system (RPAS) is by design very inefficient. Less than 1 % of the flying time is used for collecting light. This unused potential can be utilized by an innovative imaging concept, the spatio-spectral camera. The core of the camera is a frame sensor with a large number of hyperspectral filters arranged on the sensor in stepwise lines. It combines the advantages of frame cameras with those of pushbroom cameras. By acquiring images in rapid succession, such a camera can collect detailed hyperspectral information, while retaining the high spatial resolution offered by the sensor. We have developed two versions of a spatio-spectral camera and used them in a variety of conditions. In this paper, we present a summary of three missions with the in-house developed COSI prototype camera (600-900 nm) in the domains of precision agriculture (fungus infection monitoring in experimental wheat plots), horticulture (crop status monitoring to evaluate irrigation management in strawberry fields) and geology (meteorite detection on a grassland field). Additionally, we describe the characteristics of the 2nd generation, commercially available ButterflEYE camera offering extended spectral range (475-925 nm), and we discuss future work.

  14. Automating Hyperspectral Data for Rapid Response in Volcanic Emergencies

    Science.gov (United States)

    Davies, Ashley G.; Doubleday, Joshua R.; Chien, Steve A.

    2013-01-01

    In a volcanic emergency, time is of the essence. It is vital to quantify eruption parameters (thermal emission, effusion rate, location of activity) and distribute this information as quickly as possible to decision-makers in order to enable effective evaluation of eruption-related risk and hazard. The goal of this work was to automate and streamline processing of spacecraft hyperspectral data, automate product generation, and automate distribution of products. Visible and Short-Wave Infrared Images of volcanic eruption in Iceland in May 2010." class="caption" align="right">The software rapidly processes hyperspectral data, correcting for incident sunlight where necessary, and atmospheric transmission; detects thermally anomalous pixels; fits data with model black-body thermal emission spectra to determine radiant flux; calculates atmospheric convection thermal removal; and then calculates total heat loss. From these results, an estimation of effusion rate is made. Maps are generated of thermal emission and location (see figure). Products are posted online, and relevant parties notified. Effusion rate data are added to historical record and plotted to identify spikes in activity for persistently active eruptions. The entire process from start to end is autonomous. Future spacecraft, especially those in deep space, can react to detection of transient processes without the need to communicate with Earth, thus increasing science return. Terrestrially, this removes the need for human intervention.

  15. Accurate reconstruction of hyperspectral images from compressive sensing measurements

    Science.gov (United States)

    Greer, John B.; Flake, J. C.

    2013-05-01

    The emerging field of Compressive Sensing (CS) provides a new way to capture data by shifting the heaviest burden of data collection from the sensor to the computer on the user-end. This new means of sensing requires fewer measurements for a given amount of information than traditional sensors. We investigate the efficacy of CS for capturing HyperSpectral Imagery (HSI) remotely. We also introduce a new family of algorithms for constructing HSI from CS measurements with Split Bregman Iteration [Goldstein and Osher,2009]. These algorithms combine spatial Total Variation (TV) with smoothing in the spectral dimension. We examine models for three different CS sensors: the Coded Aperture Snapshot Spectral Imager-Single Disperser (CASSI-SD) [Wagadarikar et al.,2008] and Dual Disperser (CASSI-DD) [Gehm et al.,2007] cameras, and a hypothetical random sensing model closer to CS theory, but not necessarily implementable with existing technology. We simulate the capture of remotely sensed images by applying the sensor forward models to well-known HSI scenes - an AVIRIS image of Cuprite, Nevada and the HYMAP Urban image. To measure accuracy of the CS models, we compare the scenes constructed with our new algorithm to the original AVIRIS and HYMAP cubes. The results demonstrate the possibility of accurately sensing HSI remotely with significantly fewer measurements than standard hyperspectral cameras.

  16. A SPATIO-SPECTRAL CAMERA FOR HIGH RESOLUTION HYPERSPECTRAL IMAGING

    Directory of Open Access Journals (Sweden)

    S. Livens

    2017-08-01

    Full Text Available Imaging with a conventional frame camera from a moving remotely piloted aircraft system (RPAS is by design very inefficient. Less than 1 % of the flying time is used for collecting light. This unused potential can be utilized by an innovative imaging concept, the spatio-spectral camera. The core of the camera is a frame sensor with a large number of hyperspectral filters arranged on the sensor in stepwise lines. It combines the advantages of frame cameras with those of pushbroom cameras. By acquiring images in rapid succession, such a camera can collect detailed hyperspectral information, while retaining the high spatial resolution offered by the sensor. We have developed two versions of a spatio-spectral camera and used them in a variety of conditions. In this paper, we present a summary of three missions with the in-house developed COSI prototype camera (600–900 nm in the domains of precision agriculture (fungus infection monitoring in experimental wheat plots, horticulture (crop status monitoring to evaluate irrigation management in strawberry fields and geology (meteorite detection on a grassland field. Additionally, we describe the characteristics of the 2nd generation, commercially available ButterflEYE camera offering extended spectral range (475–925 nm, and we discuss future work.

  17. Hyperspectral estimation of corn fraction of photosynthetically active radiation

    International Nuclear Information System (INIS)

    Yang Fei; Zhang Bai; Song Kaishan

    2008-01-01

    Fraction of absorbed photosynthetically active radiation (FPAR) is one of the important variables in many productivity and biomass estimation models, this analyzed the effect of FPAR estimation with hyperspectral information, which could provide the scientific support on the improvement of FPAR estimation, remote sensing data validation, and the other ecological models. Based on the field experiment of corn, this paper analyzed the correlations between FPAR and spectral reflectance or the differential coefficient, and discussed the mechanism of FPAR estimation, studied corn FPAR estimation with reflectance, first differential coefficient, NDVI and RVI. The reflectance of visible bands showed much better correlations with FPAR than near-infrared bands. The correlation curve between FPAR and differential coefficient varied more frequently and greatly than the curve of FPAR and reflectance. Reflectance and differential coefficient both had good regressions with FPAR of the typical single band, with the maximum R2 of 0.791 and 0.882. In a word, differential coefficient and vegetation index were much effective than reflectance for corn FPAR estimating, and the stepwised regression of multibands differential coefficient showed the best regression with R2 of 0.944. 375 nm purpled band and 950 nm near-infraed band absorbed by water showed prodigious potential for FPAR estimating precision. On the whole, vegetation index and differential coefficient have good relationships with FPAR, and could be used for FAPR estimation. It would be effective of choosing right bands and excavating the hyperspectral data to improve FPAR estimating precision

  18. Estimations of Nitrogen Concentration in Sugarcane Using Hyperspectral Imagery

    Directory of Open Access Journals (Sweden)

    Poonsak Miphokasap

    2018-04-01

    Full Text Available This study aims to estimate the spatial variation of sugarcane Canopy Nitrogen Concentration (CNC using spectral data, which were measured from a spaceborne hyperspectral image. Stepwise Multiple Linear Regression (SMLR and Support Vector Regression (SVR were applied to calibrate and validate the CNC estimation models. The raw spectral reflectance was transformed into a First-Derivative Spectrum (FDS and absorption features to remove the spectral noise and finally used as input variables. The results indicate that the estimation models developed by non-linear SVR based Radial Basis Function (RBF kernel yield the higher correlation coefficient with CNC compared with the models computed by SMLR. The best model shows the coefficient of determination value of 0.78 and Root Mean Square Error (RMSE value of 0.035% nitrogen. The narrow sensitive spectral wavelengths for quantifying nitrogen content in the combined cultivar environments existed mainly in the electromagnetic spectrum of the visible-red, longer portion of red edge, shortwave infrared regions and far-near infrared. The most important conclusion from this experiment is that spectral signals from the space hyperspectral data contain the meaningful information for quantifying sugarcane CNC across larger geographic areas. The nutrient deficient areas could be corrected by applying suitable farm management.

  19. Spectral Band Characterization for Hyperspectral Monitoring of Water Quality

    Science.gov (United States)

    Vermillion, Stephanie C.; Raqueno, Rolando; Simmons, Rulon

    2001-01-01

    A method for selecting the set of spectral characteristics that provides the smallest increase in prediction error is of interest to those using hyperspectral imaging (HSI) to monitor water quality. The spectral characteristics of interest to these applications are spectral bandwidth and location. Three water quality constituents of interest that are detectable via remote sensing are chlorophyll (CHL), total suspended solids (TSS), and colored dissolved organic matter (CDOM). Hyperspectral data provides a rich source of information regarding the content and composition of these materials, but often provides more data than an analyst can manage. This study addresses the spectral characteristics need for water quality monitoring for two reasons. First, determination of the greatest contribution of these spectral characteristics would greatly improve computational ease and efficiency. Second, understanding the spectral capabilities of different spectral resolutions and specific regions is an essential part of future system development and characterization. As new systems are developed and tested, water quality managers will be asked to determine sensor specifications that provide the most accurate and efficient water quality measurements. We address these issues using data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and a set of models to predict constituent concentrations.

  20. Hyperspectral remote sensing of postfire soil properties

    Science.gov (United States)

    Sarah A. Lewis; Peter R. Robichaud; William J. Elliot; Bruce E. Frazier; Joan Q. Wu

    2004-01-01

    Forest fires may induce changes in soil organic properties that often lead to water repellent conditions within the soil profile that decrease soil infiltration capacity. The remote detection of water repellent soils after forest fires would lead to quicker and more accurate assessment of erosion potential. An airborne hyperspectral image was acquired over the Hayman...

  1. Demystifying autofluorescence with excitation scanning hyperspectral imaging

    Science.gov (United States)

    Deal, Joshua; Harris, Bradley; Martin, Will; Lall, Malvika; Lopez, Carmen; Rider, Paul; Boudreaux, Carole; Rich, Thomas; Leavesley, Silas J.

    2018-02-01

    Autofluorescence has historically been considered a nuisance in medical imaging. Many endogenous fluorophores, specifically, collagen, elastin, NADH, and FAD, are found throughout the human body. Diagnostically, these signals can be prohibitive since they can outcompete signals introduced for diagnostic purposes. Recent advances in hyperspectral imaging have allowed the acquisition of significantly more data in a shorter time period by scanning the excitation spectra of fluorophores. The reduced acquisition time and increased signal-to-noise ratio allow for separation of significantly more fluorophores than previously possible. Here, we propose to utilize excitation-scanning of autofluorescence to examine tissues and diagnose pathologies. Spectra of autofluorescent molecules were obtained using a custom inverted microscope (TE-2000, Nikon Instruments) with a Xe arc lamp and thin film tunable filter array (VersaChrome, Semrock, Inc.) Scans utilized excitation wavelengths from 360 nm to 550 nm in 5 nm increments. The resultant spectra were used to examine hyperspectral image stacks from various collaborative studies, including an atherosclerotic rat model and a colon cancer study. Hyperspectral images were analyzed with ENVI and custom Matlab scripts including linear spectral unmixing (LSU) and principal component analysis (PCA). Initial results suggest the ability to separate the signals of endogenous fluorophores and measure the relative concentrations of fluorophores among healthy and diseased states of similar tissues. These results suggest pathology-specific changes to endogenous fluorophores can be detected using excitationscanning hyperspectral imaging. Future work will expand the library of pure molecules and will examine more defined disease states.

  2. Hyperspectral data exploitation theory and applications

    CERN Document Server

    Chang, Chein-I

    2007-01-01

    Authored by a panel of experts in the field, this book focuses on hyperspectral image analysis, systems, and applications. With discussion of application-based projects and case studies, this professional reference will bring you up-to-date on this pervasive technology, wether you are working in the military and defense fields, or in remote sensing technology, geoscience, or agriculture.

  3. Airborne hyperspectral remote sensing in Italy

    Science.gov (United States)

    Bianchi, Remo; Marino, Carlo M.; Pignatti, Stefano

    1994-12-01

    The Italian National Research Council (CNR) in the framework of its `Strategic Project for Climate and Environment in Southern Italy' established a new laboratory for airborne hyperspectral imaging devoted to environmental problems. Since the end of June 1994, the LARA (Laboratorio Aereo per Ricerche Ambientali -- Airborne Laboratory for Environmental Studies) Project is fully operative to provide hyperspectral data to the national and international scientific community by means of deployments of its CASA-212 aircraft carrying the Daedalus AA5000 MIVIS (multispectral infrared and visible imaging spectrometer) system. MIVIS is a modular instrument consisting of 102 spectral channels that use independent optical sensors simultaneously sampled and recorded onto a compact computer compatible magnetic tape medium with a data capacity of 10.2 Gbytes. To support the preprocessing and production pipeline of the large hyperspectral data sets CNR housed in Pomezia, a town close to Rome, a ground based computer system with a software designed to handle MIVIS data. The software (MIDAS-Multispectral Interactive Data Analysis System), besides the data production management, gives to users a powerful and highly extensible hyperspectral analysis system. The Pomezia's ground station is designed to maintain and check the MIVIS instrument performance through the evaluation of data quality (like spectral accuracy, signal to noise performance, signal variations, etc.), and to produce, archive, and diffuse MIVIS data in the form of geometrically and radiometrically corrected data sets on low cost and easy access CC media.

  4. Hyperspectral signature analysis of skin parameters

    Science.gov (United States)

    Vyas, Saurabh; Banerjee, Amit; Garza, Luis; Kang, Sewon; Burlina, Philippe

    2013-02-01

    The temporal analysis of changes in biological skin parameters, including melanosome concentration, collagen concentration and blood oxygenation, may serve as a valuable tool in diagnosing the progression of malignant skin cancers and in understanding the pathophysiology of cancerous tumors. Quantitative knowledge of these parameters can also be useful in applications such as wound assessment, and point-of-care diagnostics, amongst others. We propose an approach to estimate in vivo skin parameters using a forward computational model based on Kubelka-Munk theory and the Fresnel Equations. We use this model to map the skin parameters to their corresponding hyperspectral signature. We then use machine learning based regression to develop an inverse map from hyperspectral signatures to skin parameters. In particular, we employ support vector machine based regression to estimate the in vivo skin parameters given their corresponding hyperspectral signature. We build on our work from SPIE 2012, and validate our methodology on an in vivo dataset. This dataset consists of 241 signatures collected from in vivo hyperspectral imaging of patients of both genders and Caucasian, Asian and African American ethnicities. In addition, we also extend our methodology past the visible region and through the short-wave infrared region of the electromagnetic spectrum. We find promising results when comparing the estimated skin parameters to the ground truth, demonstrating good agreement with well-established physiological precepts. This methodology can have potential use in non-invasive skin anomaly detection and for developing minimally invasive pre-screening tools.

  5. Hyperspectral Unmixing with Robust Collaborative Sparse Regression

    Directory of Open Access Journals (Sweden)

    Chang Li

    2016-07-01

    Full Text Available Recently, sparse unmixing (SU of hyperspectral data has received particular attention for analyzing remote sensing images. However, most SU methods are based on the commonly admitted linear mixing model (LMM, which ignores the possible nonlinear effects (i.e., nonlinearity. In this paper, we propose a new method named robust collaborative sparse regression (RCSR based on the robust LMM (rLMM for hyperspectral unmixing. The rLMM takes the nonlinearity into consideration, and the nonlinearity is merely treated as outlier, which has the underlying sparse property. The RCSR simultaneously takes the collaborative sparse property of the abundance and sparsely distributed additive property of the outlier into consideration, which can be formed as a robust joint sparse regression problem. The inexact augmented Lagrangian method (IALM is used to optimize the proposed RCSR. The qualitative and quantitative experiments on synthetic datasets and real hyperspectral images demonstrate that the proposed RCSR is efficient for solving the hyperspectral SU problem compared with the other four state-of-the-art algorithms.

  6. Kernel based subspace projection of hyperspectral images

    DEFF Research Database (Denmark)

    Larsen, Rasmus; Nielsen, Allan Aasbjerg; Arngren, Morten

    In hyperspectral image analysis an exploratory approach to analyse the image data is to conduct subspace projections. As linear projections often fail to capture the underlying structure of the data, we present kernel based subspace projections of PCA and Maximum Autocorrelation Factors (MAF...

  7. Mathematical model of statistical identification of information support of road transport

    Directory of Open Access Journals (Sweden)

    V. G. Kozlov

    2016-01-01

    Full Text Available In this paper based on the statistical identification method using the theory of self-organizing systems, built multifactor model the relationship of road transport and training system. Background information for the model represented by a number of parameters of average annual road transport operations and information provision, including training complex system parameters (inputs, road management and output parameters. Ask two criteria: stability criterion model and test correlation. The program determines their minimum, and is the only model of optimal complexity. The predetermined number of parameters established mathematical relationship of each output parameter with the others. To improve the accuracy and regularity of the forecast of the interpolation nodes allocated in the test data sequence. Other data form the training sequence. Decision model based on the principle of selection. Running it with the gradual complication of the mathematical description and exhaustive search of all possible variants of the models on the specified criteria. Advantages of the proposed model: adequately reflects the actual process, allows you to enter any additional input parameters and determine their impact on the individual output parameters of the road transport, allows in turn change the values of key parameters in a certain ratio and to determine the appropriate changes the output parameters of the road transport, allows to predict the output parameters road transport operations.

  8. Study of identification of geometrically shaped solids using colour and range information

    International Nuclear Information System (INIS)

    Ebihara, Kenichi

    1997-05-01

    This report is the revision of the Technical Report (MECSE 1996-7) of Monash University in Melbourne, Australia which has been distributed to the Department Library in this University. The main work which is described in this report was carried out at Intelligent Robotics Research Center (IRRC) in the Department of Electrical and Computer Systems Engineering of Monash University from March in 1995 to March in 1996 and was be supported by a grant from Research Development Corporation of Japan (JRDC). This report describes the study of identification of geometrically shaped solids with unique colour using colour and range information. This study aims at recognition of equipment in nuclear plants. For this purpose, it is hypothesized that equipment in nuclear plants can be represented by combination of geometrically shaped solids with unique colour, such as a sphere, an ellipsoid, a cone, a cylinder, a rectangular solid and a pyramid. In this report, the colour image of geometrically shaped solids could be segmented comparatively easily and effectively into regions of each solid by using colour and range information. The range data of each solid was extracted using the segmented colour image. Thus the extracted range data could be classified into a plane surface or a curved surface by checking its spatial distribution. (author)

  9. Study on Karst Information Identification of Qiandongnan Prefecture Based on RS and GIS Technology

    Science.gov (United States)

    Yao, M.; Zhou, G.; Wang, W.; Wu, Z.; Huang, Y.; Huang, X.

    2018-04-01

    Karst area is a pure natural resource base, at the same time, due to the special geological environment; there are droughts and floods alternating with frequent karst collapse, rocky desertification and other resource and environment problems, which seriously restrict the sustainable economic and social development in karst areas. Therefore, this paper identifies and studies the karst, and clarifies the distribution of karst. Provide basic data for the rational development of resources in the karst region and the governance of desertification. Due to the uniqueness of the karst landscape, it can't be directly recognized and extracted by computer in remote sensing images. Therefore, this paper uses the idea of "RS + DEM" to solve the above problems. this article is based on Landsat-5 TM imagery in 2010 and DEM data, proposes the methods to identify karst information research what is use of slope vector diagram, vegetation distribution map, distribution map of karst rocky desertification and other auxiliary data in combination with the signs for human-computer interaction interpretation, identification and extraction of peak forest, peaks cluster and isolated peaks, and further extraction of karst depression. Experiments show that this method achieves the "RS + DEM" mode through the reasonable combination of remote sensing images and DEM data. It not only effectively extracts karst areas covered with vegetation, but also quickly and accurately locks down the karst area and greatly improves the efficiency and precision of visual interpretation. The accurate interpretation rate of karst information in study area in this paper is 86.73 %.

  10. Identification of hidden relationships from the coupling of hydrophobic cluster analysis and domain architecture information.

    Science.gov (United States)

    Faure, Guilhem; Callebaut, Isabelle

    2013-07-15

    Describing domain architecture is a critical step in the functional characterization of proteins. However, some orphan domains do not match any profile stored in dedicated domain databases and are thereby difficult to analyze. We present here an original novel approach, called TREMOLO-HCA, for the analysis of orphan domain sequences and inspired from our experience in the use of Hydrophobic Cluster Analysis (HCA). Hidden relationships between protein sequences can be more easily identified from the PSI-BLAST results, using information on domain architecture, HCA plots and the conservation degree of amino acids that may participate in the protein core. This can lead to reveal remote relationships with known families of domains, as illustrated here with the identification of a hidden Tudor tandem in the human BAHCC1 protein and a hidden ET domain in the Saccharomyces cerevisiae Taf14p and human AF9 proteins. The results obtained in such a way are consistent with those provided by HHPRED, based on pairwise comparisons of HHMs. Our approach can, however, be applied even in absence of domain profiles or known 3D structures for the identification of novel families of domains. It can also be used in a reverse way for refining domain profiles, by starting from known protein domain families and identifying highly divergent members, hitherto considered as orphan. We provide a possible integration of this approach in an open TREMOLO-HCA package, which is fully implemented in python v2.7 and is available on request. Instructions are available at http://www.impmc.upmc.fr/∼callebau/tremolohca.html. isabelle.callebaut@impmc.upmc.fr Supplementary Data are available at Bioinformatics online.

  11. Mapping Soil Organic Matter with Hyperspectral Imaging

    Science.gov (United States)

    Moni, Christophe; Burud, Ingunn; Flø, Andreas; Rasse, Daniel

    2014-05-01

    Soil organic matter (SOM) plays a central role for both food security and the global environment. Soil organic matter is the 'glue' that binds soil particles together, leading to positive effects on soil water and nutrient availability for plant growth and helping to counteract the effects of erosion, runoff, compaction and crusting. Hyperspectral measurements of samples of soil profiles have been conducted with the aim of mapping soil organic matter on a macroscopic scale (millimeters and centimeters). Two soil profiles have been selected from the same experimental site, one from a plot amended with biochar and another one from a control plot, with the specific objective to quantify and map the distribution of biochar in the amended profile. The soil profiles were of size (30 x 10 x 10) cm3 and were scanned with two pushbroomtype hyperspectral cameras, one which is sensitive in the visible wavelength region (400 - 1000 nm) and one in the near infrared region (1000 - 2500 nm). The images from the two detectors were merged together into one full dataset covering the whole wavelength region. Layers of 15 mm were removed from the 10 cm high sample such that a total of 7 hyperspectral images were obtained from the samples. Each layer was analyzed with multivariate statistical techniques in order to map the different components in the soil profile. Moreover, a 3-dimensional visalization of the components through the depth of the sample was also obtained by combining the hyperspectral images from all the layers. Mid-infrared spectroscopy of selected samples of the measured soil profiles was conducted in order to correlate the chemical constituents with the hyperspectral results. The results show that hyperspectral imaging is a fast, non-destructive technique, well suited to characterize soil profiles on a macroscopic scale and hence to map elements and different organic matter quality present in a complete pedon. As such, we were able to map and quantify biochar in our

  12. Illumination compensation in ground based hyperspectral imaging

    Science.gov (United States)

    Wendel, Alexander; Underwood, James

    2017-07-01

    Hyperspectral imaging has emerged as an important tool for analysing vegetation data in agricultural applications. Recently, low altitude and ground based hyperspectral imaging solutions have come to the fore, providing very high resolution data for mapping and studying large areas of crops in detail. However, these platforms introduce a unique set of challenges that need to be overcome to ensure consistent, accurate and timely acquisition of data. One particular problem is dealing with changes in environmental illumination while operating with natural light under cloud cover, which can have considerable effects on spectral shape. In the past this has been commonly achieved by imaging known reference targets at the time of data acquisition, direct measurement of irradiance, or atmospheric modelling. While capturing a reference panel continuously or very frequently allows accurate compensation for illumination changes, this is often not practical with ground based platforms, and impossible in aerial applications. This paper examines the use of an autonomous unmanned ground vehicle (UGV) to gather high resolution hyperspectral imaging data of crops under natural illumination. A process of illumination compensation is performed to extract the inherent reflectance properties of the crops, despite variable illumination. This work adapts a previously developed subspace model approach to reflectance and illumination recovery. Though tested on a ground vehicle in this paper, it is applicable to low altitude unmanned aerial hyperspectral imagery also. The method uses occasional observations of reference panel training data from within the same or other datasets, which enables a practical field protocol that minimises in-field manual labour. This paper tests the new approach, comparing it against traditional methods. Several illumination compensation protocols for high volume ground based data collection are presented based on the results. The findings in this paper are

  13. Evaluation of atmospheric corrections on hyperspectral data with special reference to mineral mapping

    Directory of Open Access Journals (Sweden)

    Nisha Rani

    2017-07-01

    Full Text Available Hyperspectral images have wide applications in the fields of geology, mineral exploration, agriculture, forestry and environmental studies etc. due to their narrow band width with numerous channels. However, these images commonly suffer from atmospheric effects, thereby limiting their use. In such a situation, atmospheric correction becomes a necessary pre-requisite for any further processing and accurate interpretation of spectra of different surface materials/objects. In the present study, two very advance atmospheric approaches i.e. QUAC and FLAASH have been applied on the hyperspectral remote sensing imagery. The spectra of vegetation, man-made structure and different minerals from the Gadag area of Karnataka, were extracted from the raw image and also from the QUAC and FLAASH corrected images. These spectra were compared among themselves and also with the existing USGS and JHU spectral library. FLAASH is rigorous atmospheric algorithm and requires various parameters to perform but it has capability to compensate the effects of atmospheric absorption. These absorption curves in any spectra play an important role in identification of the compositions. Therefore, the presence of unwanted absorption features can lead to wrong interpretation and identification of mineral composition. FLAASH also has an advantage of spectral polishing which provides smooth spectral curves which helps in accurate identification of composition of minerals. Therefore, this study recommends that FLAASH is better than QUAC for atmospheric correction and correct interpretation and identification of composition of any object or minerals.

  14. Spectral unmixing of hyperspectral data to map bauxite deposits

    Science.gov (United States)

    Shanmugam, Sanjeevi; Abhishekh, P. V.

    2006-12-01

    This paper presents a study about the potential of remote sensing in bauxite exploration in the Kolli hills of Tamilnadu state, southern India. ASTER image (acquired in the VNIR and SWIR regions) has been used in conjunction with SRTM - DEM in this study. A new approach of spectral unmixing of ASTER image data delineated areas rich in alumina. Various geological and geomorphological parameters that control bauxite formation were also derived from the ASTER image. All these information, when integrated, showed that there are 16 cappings (including the existing mines) that satisfy most of the conditions favouring bauxitization in the Kolli Hills. The study concludes that spectral unmixing of hyperspectral satellite data in the VNIR and SWIR regions may be combined with the terrain parameters to get accurate information about bauxite deposits, including their quality.

  15. Vegetation cover analysis using a low budget hyperspectral proximal sensing system

    Directory of Open Access Journals (Sweden)

    C. Daquino

    2006-06-01

    Full Text Available This report describes the implementation of a hyperspectral proximal sensing low-budget acquisition system and its application to the detection of terrestrian vegetation cover anomalies in sites of high environmental quality. Anomalies can be due to stress for lack of water and/or pollution phenomena and weed presence in agricultural fields. The hyperspectral cube (90-bands ranging from 450 to 900 nm was acquired from the hill near Segni (RM, approximately 500 m far from the target, by means of electronically tunable filters and 8 bit CCD cameras. Spectral libraries were built using both endmember identification method and extraction of centroids of the clusters obtained from a k-means analysis of the image itself. Two classification methods were applied on the hyperspectral cube: Spectral Angle Mapper (hard and Mixed Tuned Matching Filters (MTMF. Results show the good capability of the system in detecting areas with an arboreal, shrub or leafage cover, distinguishing between zones with different spectral response. Better results were obtained using spectral library originated by the k-means method. The detected anomalies not correlated to seasonal phenomena suggest a ground true analysis to identify their origin.

  16. Deep convective cloud characterizations from both broadband imager and hyperspectral infrared sounder measurements

    Science.gov (United States)

    Ai, Yufei; Li, Jun; Shi, Wenjing; Schmit, Timothy J.; Cao, Changyong; Li, Wanbiao

    2017-02-01

    Deep convective storms have contributed to airplane accidents, making them a threat to aviation safety. The most common method to identify deep convective clouds (DCCs) is using the brightness temperature difference (BTD) between the atmospheric infrared (IR) window band and the water vapor (WV) absorption band. The effectiveness of the BTD method for DCC detection is highly related to the spectral resolution and signal-to-noise ratio (SNR) of the WV band. In order to understand the sensitivity of BTD to spectral resolution and SNR for DCC detection, a BTD to noise ratio method using the difference between the WV and IR window radiances is developed to assess the uncertainty of DCC identification for different instruments. We examined the case of AirAsia Flight QZ8501. The brightness temperatures (Tbs) over DCCs from this case are simulated for BTD sensitivity studies by a fast forward radiative transfer model with an opaque cloud assumption for both broadband imager (e.g., Multifunction Transport Satellite imager, MTSAT-2 imager) and hyperspectral IR sounder (e.g., Atmospheric Infrared Sounder) instruments; we also examined the relationship between the simulated Tb and the cloud top height. Results show that despite the coarser spatial resolution, BTDs measured by a hyperspectral IR sounder are much more sensitive to high cloud tops than broadband BTDs. As demonstrated in this study, a hyperspectral IR sounder can identify DCCs with better accuracy.

  17. A new application of hyperspectral radiometry: the characterization of painted surfaces

    Science.gov (United States)

    Wang, Cong; Salvatici, Teresa; Camaiti, Mara; Del Ventisette, Chiara; Moretti, Sandro

    2016-04-01

    Hyperspectral sensors, working in the Visible-Near Infrared and Short Wave Infrared (VNIR-SWIR) regions, are widely employed for geological applications since they can discriminate many inorganic (e.g. mineral phases) and organic compounds (i.e. vegetations and soils) [1]. Their advantage is to work in the portion of the solar spectrum used for remote sensors. Some examples of application of the hyperspectral sensors to the conservation of cultural heritage are also known. These applications concern the detection of gypsum on historical buildings [2], and the monitoring of organic protective materials on stone surfaces [3]. On the contrary, hyperspectral radiometry has not been employed on painted surfaces. Indeed, the characterization of these surfaces is mainly performed with sophisticated, micro-destractive and time-consuming laboratory analyses (i.e. SEM-EDS, FTIR and, GC-MS spectroscopy) or through portable and non-invasive instruments (mid FTIR, micro Raman, XRF, FORS) which work in different spectral ranges [4,5]. In this work the discrimination of many organic and inorganic components from paintings was investigated through a hyperspectral spectroradiometer ,which works in the 350-2500 nm region. The reflectance spectra were collected by the contact reflectance probe, equipped with an internal light source with fixed geometry of illumination and shot. Several standards samples, selected among the most common materials of paintings, were prepared and analysed in order to collect reference spectra. The standards were prepared with powders of 7 pure pigments, films of 5 varnishes (natural and synthetic), and films of 3 dried binding media. Monochromatic painted surfaces have also been prepared and investigated to verify the identification of different compounds on the surface. The results show that the discrimination of pure products is possible in the VNIR-SWIR region, except for compounds with similar composition (e.g. natural resins such as dammar and

  18. A Gimbal-Stabilized Compact Hyperspectral Imaging System, Phase II

    Data.gov (United States)

    National Aeronautics and Space Administration — The Gimbal-stabilized Compact Hyperspectral Imaging System (GCHIS) fully integrates multi-sensor spectral imaging, stereovision, GPS and inertial measurement,...

  19. Methods for automated identification of informative behaviors in natural bioptic driving.

    Science.gov (United States)

    Luo, Gang; Peli, Eli

    2012-06-01

    Visually impaired people may legally drive if wearing bioptic telescopes in some developed countries. To address the controversial safety issue of the practice, we have developed a low-cost in-car recording system that can be installed in study participants' own vehicles to record their daily driving activities. We also developed a set of automated identification techniques of informative behaviors to facilitate efficient manual review of important segments submerged in the vast amount of uncontrolled data. Here, we present the methods and quantitative results of the detection performance for six types of driving maneuvers and behaviors that are important for bioptic driving: bioptic telescope use, turns, curves, intersections, weaving, and rapid stops. The testing data were collected from one normally sighted and two visually impaired subjects across multiple days. The detection rates ranged from 82% up to 100%, and the false discovery rates ranged from 0% to 13%. In addition, two human observers were able to interpret about 80% of targets viewed through the telescope. These results indicate that with appropriate data processing the low-cost system is able to provide reliable data for natural bioptic driving studies.

  20. Observation of SO2 degassing at Stromboli volcano using a hyperspectral thermal infrared imager

    Science.gov (United States)

    Smekens, Jean-François; Gouhier, Mathieu

    2018-05-01

    Thermal infrared (TIR) imaging is a common tool for the monitoring of volcanic activity. Broadband cameras with increasing sampling frequency give great insight into the physical processes taking place during effusive and explosive event, while Fourier transform infrared (FTIR) methods provide high resolution spectral information used to assess the composition of volcanic gases but are often limited to a single point of interest. Continuing developments in detector technology have given rise to a new class of hyperspectral imagers combining the advantages of both approaches. In this work, we present the results of our observations of volcanic activity at Stromboli volcano with a ground-based imager, the Telops Hyper-Cam LW, when used to detect emissions of sulfur dioxide (SO2) produced at the vent, with data acquired at Stromboli volcano (Italy) in early October of 2015. We have developed an innovative technique based on a curve-fitting algorithm to quickly extract spectral information from high-resolution datasets, allowing fast and reliable identification of SO2. We show in particular that weak SO2 emissions, such as inter-eruptive gas puffing, can be easily detected using this technology, even with poor weather conditions during acquisition (e.g., high relative humidity, presence of fog and/or ash). Then, artificially reducing the spectral resolution of the instrument, we recreated a variety of commonly used multispectral configurations to examine the efficiency of four qualitative SO2 indicators based on simple Brightness Temperature Difference (BTD). Our results show that quickly changing conditions at the vent - including but not limited to the presence of summit fog - render the establishment of meaningful thresholds for BTD indicators difficult. Building on those results, we propose recommendations on the use of multispectral imaging for SO2 monitoring and routine measurements from ground-based instruments.

  1. Hyperspectral image analysis for the determination of alteration minerals in geothermal fields: Çürüksu (Denizli) Graben, Turkey

    Science.gov (United States)

    Uygur, Merve; Karaman, Muhittin; Kumral, Mustafa

    2016-04-01

    Çürüksu (Denizli) Graben hosts various geothermal fields such as Kızıldere, Yenice, Gerali, Karahayıt, and Tekkehamam. Neotectonic activities, which are caused by extensional tectonism, and deep circulation in sub-volcanic intrusions are heat sources of hydrothermal solutions. The temperature of hydrothermal solutions is between 53 and 260 degree Celsius. Phyllic, argillic, silicic, and carbonatization alterations and various hydrothermal minerals have been identified in various research studies of these areas. Surfaced hydrothermal alteration minerals are one set of potential indicators of geothermal resources. Developing the exploration tools to define the surface indicators of geothermal fields can assist in the recognition of geothermal resources. Thermal and hyperspectral imaging and analysis can be used for defining the surface indicators of geothermal fields. This study tests the hypothesis that hyperspectral image analysis based on EO-1 Hyperion images can be used for the delineation and definition of surfaced hydrothermal alteration in geothermal fields. Hyperspectral image analyses were applied to images covering the geothermal fields whose alteration characteristic are known. To reduce data dimensionality and identify spectral endmembers, Kruse's multi-step process was applied to atmospherically and geometrically-corrected hyperspectral images. Minimum Noise Fraction was used to reduce the spectral dimensions and isolate noise in the images. Extreme pixels were identified from high order MNF bands using the Pixel Purity Index. n-Dimensional Visualization was utilized for unique pixel identification. Spectral similarities between pixel spectral signatures and known endmember spectrum (USGS Spectral Library) were compared with Spectral Angle Mapper Classification. EO-1 Hyperion hyperspectral images and hyperspectral analysis are sensitive to hydrothermal alteration minerals, as their diagnostic spectral signatures span the visible and shortwave

  2. STUDY ON KARST INFORMATION IDENTIFICATION OF QIANDONGNAN PREFECTURE BASED ON RS AND GIS TECHNOLOGY

    Directory of Open Access Journals (Sweden)

    M. Yao

    2018-04-01

    Full Text Available Karst area is a pure natural resource base, at the same time, due to the special geological environment; there are droughts and floods alternating with frequent karst collapse, rocky desertification and other resource and environment problems, which seriously restrict the sustainable economic and social development in karst areas. Therefore, this paper identifies and studies the karst, and clarifies the distribution of karst. Provide basic data for the rational development of resources in the karst region and the governance of desertification. Due to the uniqueness of the karst landscape, it can’t be directly recognized and extracted by computer in remote sensing images. Therefore, this paper uses the idea of “RS + DEM” to solve the above problems. this article is based on Landsat-5 TM imagery in 2010 and DEM data, proposes the methods to identify karst information research what is use of slope vector diagram, vegetation distribution map, distribution map of karst rocky desertification and other auxiliary data in combination with the signs for human-computer interaction interpretation, identification and extraction of peak forest, peaks cluster and isolated peaks, and further extraction of karst depression. Experiments show that this method achieves the “RS + DEM” mode through the reasonable combination of remote sensing images and DEM data. It not only effectively extracts karst areas covered with vegetation, but also quickly and accurately locks down the karst area and greatly improves the efficiency and precision of visual interpretation. The accurate interpretation rate of karst information in study area in this paper is 86.73 %.

  3. [Study on Application of NIR Spectral Information Screening in Identification of Maca Origin].

    Science.gov (United States)

    Wang, Yuan-zhong; Zhao, Yan-li; Zhang, Ji; Jin, Hang

    2016-02-01

    Medicinal and edible plant Maca is rich in various nutrients and owns great medicinal value. Based on near infrared diffuse reflectance spectra, 139 Maca samples collected from Peru and Yunnan were used to identify their geographical origins. Multiplication signal correction (MSC) coupled with second derivative (SD) and Norris derivative filter (ND) was employed in spectral pretreatment. Spectrum range (7,500-4,061 cm⁻¹) was chosen by spectrum standard deviation. Combined with principal component analysis-mahalanobis distance (PCA-MD), the appropriate number of principal components was selected as 5. Based on the spectrum range and the number of principal components selected, two abnormal samples were eliminated by modular group iterative singular sample diagnosis method. Then, four methods were used to filter spectral variable information, competitive adaptive reweighted sampling (CARS), monte carlo-uninformative variable elimination (MC-UVE), genetic algorithm (GA) and subwindow permutation analysis (SPA). The spectral variable information filtered was evaluated by model population analysis (MPA). The results showed that RMSECV(SPA) > RMSECV(CARS) > RMSECV(MC-UVE) > RMSECV(GA), were 2. 14, 2. 05, 2. 02, and 1. 98, and the spectral variables were 250, 240, 250 and 70, respectively. According to the spectral variable filtered, partial least squares discriminant analysis (PLS-DA) was used to build the model, with random selection of 97 samples as training set, and the other 40 samples as validation set. The results showed that, R²: GA > MC-UVE > CARS > SPA, RMSEC and RMSEP: GA Maca. The method was aimed to lay the foundation for traditional Chinese medicine identification and quality evaluation.

  4. Testing a high-power LED based light source for hyperspectral imaging microscopy

    Science.gov (United States)

    Klomkaew, Phiwat; Mayes, Sam A.; Rich, Thomas C.; Leavesley, Silas J.

    2017-02-01

    Our lab has worked to develop high-speed hyperspectral imaging systems that scan the fluorescence excitation spectrum for biomedical imaging applications. Hyperspectral imaging can be used in remote sensing, medical imaging, reaction analysis, and other applications. Here, we describe the development of a hyperspectral imaging system that comprised an inverted Nikon Eclipse microscope, sCMOS camera, and a custom light source that utilized a series of high-power LEDs. LED selection was performed to achieve wavelengths of 350-590 nm. To reduce scattering, LEDs with low viewing angles were selected. LEDs were surface-mount soldered and powered by an RCD. We utilized 3D printed mounting brackets to assemble all circuit components. Spectraradiometric calibration was performed using a spectrometer (QE65000, Ocean Optics) and integrating sphere (FOIS-1, Ocean Optics). Optical output and LED driving current were measured over a range of illumination intensities. A normalization algorithm was used to calibrate and optimize the intensity of the light source. The highest illumination power was at 375 nm (3300 mW/cm2), while the lowest illumination power was at 515, 525, and 590 nm (5200 mW/cm2). Comparing the intensities supplied by each LED to the intensities measured at the microscope stage, we found there was a great loss in power output. Future work will focus on using two of the same LEDs to double the power and finding more LED and/or laser diodes and chips around the range. This custom hyperspectral imaging system could be used for the detection of cancer and the identification of biomolecules.

  5. Estimation of the fraction of absorbed photosynthetically active radiation (fPAR) in maize canopies using LiDAR data and hyperspectral imagery.

    Science.gov (United States)

    Qin, Haiming; Wang, Cheng; Zhao, Kaiguang; Xi, Xiaohuan

    2018-01-01

    Accurate estimation of the fraction of absorbed photosynthetically active radiation (fPAR) for maize canopies are important for maize growth monitoring and yield estimation. The goal of this study is to explore the potential of using airborne LiDAR and hyperspectral data to better estimate maize fPAR. This study focuses on estimating maize fPAR from (1) height and coverage metrics derived from airborne LiDAR point cloud data; (2) vegetation indices derived from hyperspectral imagery; and (3) a combination of these metrics. Pearson correlation analyses were conducted to evaluate the relationships among LiDAR metrics, hyperspectral metrics, and field-measured fPAR values. Then, multiple linear regression (MLR) models were developed using these metrics. Results showed that (1) LiDAR height and coverage metrics provided good explanatory power (i.e., R2 = 0.81); (2) hyperspectral vegetation indices provided moderate interpretability (i.e., R2 = 0.50); and (3) the combination of LiDAR metrics and hyperspectral metrics improved the LiDAR model (i.e., R2 = 0.88). These results indicate that LiDAR model seems to offer a reliable method for estimating maize fPAR at a high spatial resolution and it can be used for farmland management. Combining LiDAR and hyperspectral metrics led to better performance of maize fPAR estimation than LiDAR or hyperspectral metrics alone, which means that maize fPAR retrieval can benefit from the complementary nature of LiDAR-detected canopy structure characteristics and hyperspectral-captured vegetation spectral information.

  6. Refining the Concept of Combining Hyperspectral and Multi-Angle Sensors for Land Surface Applications

    Science.gov (United States)

    Simic, Anita

    Assessment of leaf and canopy chlorophyll content provides information on plant physiological status; it is related to nitrogen content and hence, photosynthesis process, net primary productivity and carbon budget. In this study, a method is developed for the retrieval of total chlorophyll content (Chlorophyll a+b) per unit leaf and per unit ground area based on improved vegetation structural parameters which are derived using multispectral multi-angle remote sensing data. Structural characteristics such as clumping and gaps within a canopy affect its solar radiation absorption and distribution and impact its reflected radiance acquired by a sensor. One of the main challenges for the remote sensing community is to accurately estimate vegetation structural parameters, which inevitably influence the retrieval of leaf chlorophyll content. Multi-angle optical measurements provide a means to characterize the anisotropy of surface reflectance, which has been shown to contain information on vegetation structural characteristics. Hyperspectral optical measurements, on the other hand, provide a fine spectral resolution at the red-edge, a narrow spectral range between the red and near infra-red spectra, which is particularly useful for retrieving chlorophyll content. This study explores a new refined measurement concept of combining multi-angle and hyperspectral remote sensing that employs hyperspectral signals only in the vertical (nadir) direction and multispectral measurements in two additional (off-nadir) directions within two spectral bands, red and near infra-red (NIR). The refinement has been proposed in order to reduce the redundancy of hyperspectral data at more than one angle and to better retrieve the three-dimensional vegetation structural information by choosing the two most useful angles of measurements. To illustrate that hyperspectral data acquired at multiple angles exhibit redundancy, a radiative transfer model was used to generate off-nadir hyperspectral

  7. Maximum relevance, minimum redundancy band selection based on neighborhood rough set for hyperspectral data classification

    International Nuclear Information System (INIS)

    Liu, Yao; Chen, Yuehua; Tan, Kezhu; Xie, Hong; Wang, Liguo; Xie, Wu; Yan, Xiaozhen; Xu, Zhen

    2016-01-01

    Band selection is considered to be an important processing step in handling hyperspectral data. In this work, we selected informative bands according to the maximal relevance minimal redundancy (MRMR) criterion based on neighborhood mutual information. Two measures MRMR difference and MRMR quotient were defined and a forward greedy search for band selection was constructed. The performance of the proposed algorithm, along with a comparison with other methods (neighborhood dependency measure based algorithm, genetic algorithm and uninformative variable elimination algorithm), was studied using the classification accuracy of extreme learning machine (ELM) and random forests (RF) classifiers on soybeans’ hyperspectral datasets. The results show that the proposed MRMR algorithm leads to promising improvement in band selection and classification accuracy. (paper)

  8. 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...

  9. Hyperspectral Data Analysis and Visualisation

    NARCIS (Netherlands)

    Hogervorst, M.A.; Schwering, P.B.W.

    2011-01-01

    Electro-Optical (EO) imaging sensors are widely used for a range of tasks, e.g. for Target Acquisition (TA: detection, recognition and identification of (military) relevant objects) or visual search. These tasks can be performed by a human observer, by an algorithm (Automatic Target Recognition) or

  10. Extended data analysis strategies for high resolution imaging MS : new methods to deal with extremely large image hyperspectral datasets

    NARCIS (Netherlands)

    Klerk, L.A.; Broersen, A.; Fletcher, I.W.; Liere, van R.; Heeren, R.M.A.

    2007-01-01

    The large size of the hyperspectral datasets that are produced with modern mass spectrometric imaging techniques makes it difficult to analyze the results. Unsupervised statistical techniques are needed to extract relevant information from these datasets and reduce the data into a surveyable

  11. Time Series Analysis of Vegetation Change using Hyperspectral and Multispectral Data

    Science.gov (United States)

    2012-09-01

    rivers clogged with sediment” (Hartman, 2008). In addition, backpackers, campers, and skiers are in danger of being hit by falling trees. Mountain...information from hyperspectral data without a priori knowledge or requiring ground observations” (Kruse & Perry, 2009). Figure 16. Spectral...known endmembers and the scene spectra (Boardman & Kruse, 2011). Known endmembers come from analysts’ knowledge of an area in a scene, or from

  12. Advances in hyperspectral remote sensing I: The visible Fourier transform hyperspectral imager

    Directory of Open Access Journals (Sweden)

    J. Bruce Rafert

    2015-05-01

    Full Text Available We discuss early hyperspectral research and development activities during the 1990s that led to the deployment of aircraft and satellite payloads whose heritage was based on the use of visible, spatially modulated, imaging Fourier transform spectrometers, beginning with early experiments at the Florida Institute of Technology, through successful launch and deployment of the Visible Fourier Transform Hyperspectral Imager on MightySat II.1 on 19 July 2000. In addition to a brief chronological overview, we also discuss several of the most interesting optical engineering challenges that were addressed over this timeframe, present some as-yet un-exploited features of field-widened (slit-less SMIFTS instruments, and present some images from ground-based, aircraft-based and satellite-based instruments that helped provide the impetus for the proliferation and development of entire new families of instruments and countless new applications for hyperspectral imaging.

  13. Classification of High Spatial Resolution, Hyperspectral ...

    Science.gov (United States)

    EPA announced the availability of the final report,Classification of High Spatial Resolution, Hyperspectral Remote Sensing Imagery of the Little Miami River Watershed in Southwest Ohio, USA . This report and associated land use/land cover (LULC) coverage is the result of a collaborative effort among an interdisciplinary team of scientists with the U.S. Environmental Protection Agency's (U.S. EPA's) Office of Research and Development in Cincinnati, Ohio. A primary goal of this project is to enhance the use of geography and spatial analytic tools in risk assessment, and to improve the scientific basis for risk management decisions affecting drinking water and water quality. The land use/land cover classification is derived from 82 flight lines of Compact Airborne Spectrographic Imager (CASI) hyperspectral imagery acquired from July 24 through August 9, 2002 via fixed-wing aircraft.

  14. Software for Simulation of Hyperspectral Images

    Science.gov (United States)

    Richtsmeier, Steven C.; Singer-Berk, Alexander; Bernstein, Lawrence S.

    2002-01-01

    A package of software generates simulated hyperspectral images for use in validating algorithms that generate estimates of Earth-surface spectral reflectance from hyperspectral images acquired by airborne and spaceborne instruments. This software is based on a direct simulation Monte Carlo approach for modeling three-dimensional atmospheric radiative transport as well as surfaces characterized by spatially inhomogeneous bidirectional reflectance distribution functions. In this approach, 'ground truth' is accurately known through input specification of surface and atmospheric properties, and it is practical to consider wide variations of these properties. The software can treat both land and ocean surfaces and the effects of finite clouds with surface shadowing. The spectral/spatial data cubes computed by use of this software can serve both as a substitute for and a supplement to field validation data.

  15. Estimating physiological skin parameters from hyperspectral signatures

    Science.gov (United States)

    Vyas, Saurabh; Banerjee, Amit; Burlina, Philippe

    2013-05-01

    We describe an approach for estimating human skin parameters, such as melanosome concentration, collagen concentration, oxygen saturation, and blood volume, using hyperspectral radiometric measurements (signatures) obtained from in vivo skin. We use a computational model based on Kubelka-Munk theory and the Fresnel equations. This model forward maps the skin parameters to a corresponding multiband reflectance spectra. Machine-learning-based regression is used to generate the inverse map, and hence estimate skin parameters from hyperspectral signatures. We test our methods using synthetic and in vivo skin signatures obtained in the visible through the short wave infrared domains from 24 patients of both genders and Caucasian, Asian, and African American ethnicities. Performance validation shows promising results: good agreement with the ground truth and well-established physiological precepts. These methods have potential use in the characterization of skin abnormalities and in minimally-invasive prescreening of malignant skin cancers.

  16. Atmospheric correction of APEX hyperspectral data

    Directory of Open Access Journals (Sweden)

    Sterckx Sindy

    2016-03-01

    Full Text Available Atmospheric correction plays a crucial role among the processing steps applied to remotely sensed hyperspectral data. Atmospheric correction comprises a group of procedures needed to remove atmospheric effects from observed spectra, i.e. the transformation from at-sensor radiances to at-surface radiances or reflectances. In this paper we present the different steps in the atmospheric correction process for APEX hyperspectral data as applied by the Central Data Processing Center (CDPC at the Flemish Institute for Technological Research (VITO, Mol, Belgium. The MODerate resolution atmospheric TRANsmission program (MODTRAN is used to determine the source of radiation and for applying the actual atmospheric correction. As part of the overall correction process, supporting algorithms are provided in order to derive MODTRAN configuration parameters and to account for specific effects, e.g. correction for adjacency effects, haze and shadow correction, and topographic BRDF correction. The methods and theory underlying these corrections and an example of an application are presented.

  17. LIFTERS-hyperspectral imaging at LLNL

    Energy Technology Data Exchange (ETDEWEB)

    Fields, D. [Lawrence Livermore National Lab., CA (United States); Bennett, C.; Carter, M.

    1994-11-15

    LIFTIRS, the Livermore Imaging Fourier Transform InfraRed Spectrometer, recently developed at LLNL, is an instrument which enables extremely efficient collection and analysis of hyperspectral imaging data. LIFTIRS produces a spatial format of 128x128 pixels, with spectral resolution arbitrarily variable up to a maximum of 0.25 inverse centimeters. Time resolution and spectral resolution can be traded off for each other with great flexibility. We will discuss recent measurements made with this instrument, and present typical images and spectra.

  18. Distributed Unmixing of Hyperspectral Datawith Sparsity Constraint

    Science.gov (United States)

    Khoshsokhan, S.; Rajabi, R.; Zayyani, H.

    2017-09-01

    Spectral unmixing (SU) is a data processing problem in hyperspectral remote sensing. The significant challenge in the SU problem is how to identify endmembers and their weights, accurately. For estimation of signature and fractional abundance matrices in a blind problem, nonnegative matrix factorization (NMF) and its developments are used widely in the SU problem. One of the constraints which was added to NMF is sparsity constraint that was regularized by L1/2 norm. In this paper, a new algorithm based on distributed optimization has been used for spectral unmixing. In the proposed algorithm, a network including single-node clusters has been employed. Each pixel in hyperspectral images considered as a node in this network. The distributed unmixing with sparsity constraint has been optimized with diffusion LMS strategy, and then the update equations for fractional abundance and signature matrices are obtained. Simulation results based on defined performance metrics, illustrate advantage of the proposed algorithm in spectral unmixing of hyperspectral data compared with other methods. The results show that the AAD and SAD of the proposed approach are improved respectively about 6 and 27 percent toward distributed unmixing in SNR=25dB.

  19. 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.

  20. Evaluation of camouflage effectiveness using hyperspectral images

    Science.gov (United States)

    Zavvartorbati, Ahmad; Dehghani, Hamid; Rashidi, Ali Jabar

    2017-10-01

    Recent advances in camouflage engineering have made it more difficult to detect targets. Assessing the effectiveness of camouflage against different target detection methods leads to identifying the strengths and weaknesses of camouflage designs. One of the target detection methods is to analyze the content of the scene using remote sensing hyperspectral images. In the process of evaluating camouflage designs, there must be comprehensive and efficient evaluation criteria. Three parameters were considered as the main factors affecting the target detection and based on these factors, camouflage effectiveness assessment criteria were proposed. To combine the criteria in the form of a single equation, the equation used in target visual search models was employed and for determining the criteria, a model was presented based on the structure of the computational visual attention systems. Also, in software implementations on the HyMap hyperspectral image, a variety of camouflage levels were created for the real targets in the image. Assessing the camouflage levels using the proposed criteria, comparing and analyzing the results can show that the provided criteria and model are effective for the evaluation of camouflage designs using hyperspectral images.

  1. Binomial probability distribution model-based protein identification algorithm for tandem mass spectrometry utilizing peak intensity information.

    Science.gov (United States)

    Xiao, Chuan-Le; Chen, Xiao-Zhou; Du, Yang-Li; Sun, Xuesong; Zhang, Gong; He, Qing-Yu

    2013-01-04

    Mass spectrometry has become one of the most important technologies in proteomic analysis. Tandem mass spectrometry (LC-MS/MS) is a major tool for the analysis of peptide mixtures from protein samples. The key step of MS data processing is the identification of peptides from experimental spectra by searching public sequence databases. Although a number of algorithms to identify peptides from MS/MS data have been already proposed, e.g. Sequest, OMSSA, X!Tandem, Mascot, etc., they are mainly based on statistical models considering only peak-matches between experimental and theoretical spectra, but not peak intensity information. Moreover, different algorithms gave different results from the same MS data, implying their probable incompleteness and questionable reproducibility. We developed a novel peptide identification algorithm, ProVerB, based on a binomial probability distribution model of protein tandem mass spectrometry combined with a new scoring function, making full use of peak intensity information and, thus, enhancing the ability of identification. Compared with Mascot, Sequest, and SQID, ProVerB identified significantly more peptides from LC-MS/MS data sets than the current algorithms at 1% False Discovery Rate (FDR) and provided more confident peptide identifications. ProVerB is also compatible with various platforms and experimental data sets, showing its robustness and versatility. The open-source program ProVerB is available at http://bioinformatics.jnu.edu.cn/software/proverb/ .

  2. Hyperspectral microscopy to identify foodborne bacteria with optimum lighting source

    Science.gov (United States)

    Hyperspectral microscopy is an emerging technology for rapid detection of foodborne pathogenic bacteria. Since scattering spectral signatures from hyperspectral microscopic images (HMI) vary with lighting sources, it is important to select optimal lights. The objective of this study is to compare t...

  3. Blind estimation of blur in hyperspectral images

    Science.gov (United States)

    Zhang, Mo; Vozel, Benoit; Chehdi, Kacem; Uss, Mykhail; Abramov, Sergey; Lukin, Vladimir

    2017-10-01

    Hyperspectral images acquired by remote sensing systems are generally degraded by noise and can be sometimes more severely degraded by blur. When no knowledge is available about the degradations present on the original image, blind restoration methods can only be considered. By blind, we mean absolutely no knowledge neither of the blur point spread function (PSF) nor the original latent channel and the noise level. In this study, we address the blind restoration of the degraded channels component-wise, according to a sequential scheme. For each degraded channel, the sequential scheme estimates the blur point spread function (PSF) in a first stage and deconvolves the degraded channel in a second and final stage by means of using the PSF previously estimated. We propose a new component-wise blind method for estimating effectively and accurately the blur point spread function. This method follows recent approaches suggesting the detection, selection and use of sufficiently salient edges in the current processed channel for supporting the regularized blur PSF estimation. Several modifications are beneficially introduced in our work. A new selection of salient edges through thresholding adequately the cumulative distribution of their corresponding gradient magnitudes is introduced. Besides, quasi-automatic and spatially adaptive tuning of the involved regularization parameters is considered. To prove applicability and higher efficiency of the proposed method, we compare it against the method it originates from and four representative edge-sparsifying regularized methods of the literature already assessed in a previous work. Our attention is mainly paid to the objective analysis (via ݈l1-norm) of the blur PSF error estimation accuracy. The tests are performed on a synthetic hyperspectral image. This synthetic hyperspectral image has been built from various samples from classified areas of a real-life hyperspectral image, in order to benefit from realistic spatial

  4. Hyperspectral imaging detection of decayed honey peaches based on their chlorophyll content.

    Science.gov (United States)

    Sun, Ye; Wang, Yihang; Xiao, Hui; Gu, Xinzhe; Pan, Leiqing; Tu, Kang

    2017-11-15

    Honey peach is a very common but highly perishable market fruit. When pathogens infect fruit, chlorophyll as one of the important components related to fruit quality, decreased significantly. Here, the feasibility of hyperspectral imaging to determine the chlorophyll content thus distinguishing diseased peaches was investigated. Three optimal wavelengths (617nm, 675nm, and 818nm) were selected according to chlorophyll content via successive projections algorithm. Partial least square regression models were established to determine chlorophyll content. Three band ratios were obtained using these optimal wavelengths, which improved spatial details, but also integrates the information of chemical composition from spectral characteristics. The band ratio values were suitable to classify the diseased peaches with 98.75% accuracy and clearly show the spatial distribution of diseased parts. This study provides a new perspective for the selection of optimal wavelengths of hyperspectral imaging via chlorophyll content, thus enabling the detection of fungal diseases in peaches. Copyright © 2017 Elsevier Ltd. All rights reserved.

  5. 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.

  6. Supplemental Blue LED Lighting Array to Improve the Signal Quality in Hyperspectral Imaging of Plants

    Directory of Open Access Journals (Sweden)

    Anne-Katrin Mahlein

    2015-06-01

    Full Text Available Hyperspectral imaging systems used in plant science or agriculture often have suboptimal signal-to-noise ratio in the blue region (400–500 nm of the electromagnetic spectrum. Typically there are two principal reasons for this effect, the low sensitivity of the imaging sensor and the low amount of light available from the illuminating source. In plant science, the blue region contains relevant information about the physiology and the health status of a plant. We report on the improvement in sensitivity of a hyperspectral imaging system in the blue region of the spectrum by using supplemental illumination provided by an array of high brightness light emitting diodes (LEDs with an emission peak at 470 nm.

  7. Detection of Chlorophyll and Leaf Area Index Dynamics from Sub-weekly Hyperspectral Imagery

    Science.gov (United States)

    Houborg, Rasmus; McCabe, Matthew F.; Angel, Yoseline; Middleton, Elizabeth M.

    2016-01-01

    Temporally rich hyperspectral time-series can provide unique time critical information on within-field variations in vegetation health and distribution needed by farmers to effectively optimize crop production. In this study, a dense time series of images were acquired from the Earth Observing-1 (EO-1) Hyperion sensor over an intensive farming area in the center of Saudi Arabia. After correction for atmospheric effects, optimal links between carefully selected explanatory hyperspectral vegetation indices and target vegetation characteristics were established using a machine learning approach. A dataset of in-situ measured leaf chlorophyll (Chll) and leaf area index (LAI), collected during five intensive field campaigns over a variety of crop types, were used to train the rule-based predictive models. The ability of the narrow-band hyperspectral reflectance information to robustly assess and discriminate dynamics in foliar biochemistry and biomass through empirical relationships were investigated. This also involved evaluations of the generalization and reproducibility of the predictions beyond the conditions of the training dataset. The very high temporal resolution of the satellite retrievals constituted a specifically intriguing feature that facilitated detection of total canopy Chl and LAI dynamics down to sub-weekly intervals. The study advocates the benefits associated with the availability of optimum spectral and temporal resolution spaceborne observations for agricultural management purposes.

  8. [Investigation of the hyperspectral image characteristics of wheat leaves under different stress].

    Science.gov (United States)

    Zhang, Dong-Yan; Zhang, Jing-Cheng; Zhu, Da-Zhou; Wang, Ji-Hua; Luo, Ju-Hua; Zhao, Jin-Ling; Huang, Wen-Jiang

    2011-04-01

    The diagnosis of growing status and vigor of crops under various stresses is an important step in precision agriculture. Hyperspectral imaging technology has the advantage of providing both spectral and spatial information simultaneously, and has become a research hot spot. In the present study, auto-development of the pushbroom imaging spectrometer (PIS) was utilized to collect hyperspectral images of wheat leaves which suffer from shortage of nutrient, pest and disease stress. The hyperspectral cube was processed by the method of pixel average step by step to highlight the spectral characteristics, which facilitate the analysis based on the differences of leaves reflectance. The results showed that the hyperspectra of leaves from different layers can display nutrient differences, and recognize intuitively different stress extent by imaging figures. With the 2 nanometer spectral resolution and millimeter level spatial resolution of PIS, the number of disease spot can be qualitatively calculated when crop is infected with diseases, and, the area of plant disease could also be quantitatively analyzed; when crop suffered from pest and insect, the spectral information of leaves with single aphid and aphids can be detected by PIS, which provides a new means to quantitatively detect the aphid destroying of wheat leaf. The present study demonstrated that hyperspecral imaging has a great potential in quantitative and qualitative analysis of crop growth.

  9. Detection of chlorophyll and leaf area index dynamics from sub-weekly hyperspectral imagery

    KAUST Repository

    Houborg, Rasmus

    2016-10-25

    Temporally rich hyperspectral time-series can provide unique time critical information on within-field variations in vegetation health and distribution needed by farmers to effectively optimize crop production. In this study, a dense timeseries of images were acquired from the Earth Observing-1 (EO-1) Hyperion sensor over an intensive farming area in the center of Saudi Arabia. After correction for atmospheric effects, optimal links between carefully selected explanatory hyperspectral vegetation indices and target vegetation characteristics were established using a machine learning approach. A dataset of in-situ measured leaf chlorophyll (Chll) and leaf area index (LAI), collected during five intensive field campaigns over a variety of crop types, were used to train the rule-based predictive models. The ability of the narrow-band hyperspectral reflectance information to robustly assess and discriminate dynamics in foliar biochemistry and biomass through empirical relationships were investigated. This also involved evaluations of the generalization and reproducibility of the predictions beyond the conditions of the training dataset. The very high temporal resolution of the satellite retrievals constituted a specifically intriguing feature that facilitated detection of total canopy Chl and LAI dynamics down to sub-weekly intervals. The study advocates the benefits associated with the availability of optimum spectral and temporal resolution spaceborne observations for agricultural management purposes.

  10. Hyperspectral band selection based on consistency-measure of neighborhood rough set theory

    International Nuclear Information System (INIS)

    Liu, Yao; Xie, Hong; Wang, Liguo; Tan, Kezhu; Chen, Yuehua; Xu, Zhen

    2016-01-01

    Band selection is a well-known approach for reducing dimensionality in hyperspectral imaging. In this paper, a band selection method based on consistency-measure of neighborhood rough set theory (CMNRS) was proposed to select informative bands from hyperspectral images. A decision-making information system was established by the reflection spectrum of soybeans’ hyperspectral data between 400 nm and 1000 nm wavelengths. The neighborhood consistency-measure, which reflects not only the size of the decision positive region, but also the sample distribution in the boundary region, was used as the evaluation function of band significance. The optimal band subset was selected by a forward greedy search algorithm. A post-pruning strategy was employed to overcome the over-fitting problem and find the minimum subset. To assess the effectiveness of the proposed band selection technique, two classification models (extreme learning machine (ELM) and random forests (RF)) were built. The experimental results showed that the proposed algorithm can effectively select key bands and obtain satisfactory classification accuracy. (paper)

  11. Detection of chlorophyll and leaf area index dynamics from sub-weekly hyperspectral imagery

    KAUST Repository

    Houborg, Rasmus; McCabe, Matthew; Angel, Yoseline; Middleton, Elizabeth M.

    2016-01-01

    Temporally rich hyperspectral time-series can provide unique time critical information on within-field variations in vegetation health and distribution needed by farmers to effectively optimize crop production. In this study, a dense timeseries of images were acquired from the Earth Observing-1 (EO-1) Hyperion sensor over an intensive farming area in the center of Saudi Arabia. After correction for atmospheric effects, optimal links between carefully selected explanatory hyperspectral vegetation indices and target vegetation characteristics were established using a machine learning approach. A dataset of in-situ measured leaf chlorophyll (Chll) and leaf area index (LAI), collected during five intensive field campaigns over a variety of crop types, were used to train the rule-based predictive models. The ability of the narrow-band hyperspectral reflectance information to robustly assess and discriminate dynamics in foliar biochemistry and biomass through empirical relationships were investigated. This also involved evaluations of the generalization and reproducibility of the predictions beyond the conditions of the training dataset. The very high temporal resolution of the satellite retrievals constituted a specifically intriguing feature that facilitated detection of total canopy Chl and LAI dynamics down to sub-weekly intervals. The study advocates the benefits associated with the availability of optimum spectral and temporal resolution spaceborne observations for agricultural management purposes.

  12. 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.

  13. HYPERSPECTRAL REMOTE SENSING WITH THE UAS "STUTTGARTER ADLER" – CHALLENGES, EXPERIENCES AND FIRST RESULTS

    Directory of Open Access Journals (Sweden)

    A. Buettner

    2013-08-01

    Full Text Available The UAS "Stuttgarter Adler" was designed as a flexible and cost-effective remote-sensing platform for acquisition of high quality environmental data. Different missions for precision agriculture applications and BRDF-research have been successfully performed with a multispectral camera system and a spectrometer as main payloads. Currently, an imaging spectrometer is integrated in the UAS as a new payload, which enables the recording of hyperspectral data in more than 200 spectral bands in the visible and near infrared spectrum. The recording principle of the hyperspectral instrument is based on a line scanner. Each line is stored as a matrix image with spectral information in one axis and spatial information in the other axis of the image. Besides a detailed specification of the system concept and instrument design, the calibration procedure of the hyperspectral sensor system is discussed and results of the laboratory calibration are presented. The complete processing chain of measurement data is described and first results of measurement-flights over agricultural test sites are presented.

  14. Hyperspectral Remote Sensing of Terrestrial Ecosystem Productivity from ISS

    Science.gov (United States)

    Huemmrich, K. F.; Campbell, P. K. E.; Gao, B. C.; Flanagan, L. B.; Goulden, M.

    2017-12-01

    Data from the Hyperspectral Imager for Coastal Ocean (HICO), mounted on the International Space Station (ISS), were used to develop and test algorithms for remotely retrieving ecosystem productivity. The ISS orbit introduces both limitations and opportunities for observing ecosystem dynamics. Twenty six HICO images were used from four study sites representing different vegetation types: grasslands, shrubland, and forest. Gross ecosystem production (GEP) data from eddy covariance were matched with HICO-derived spectra. Multiple algorithms were successful relating spectral reflectance with GEP, including: Spectral Vegetation Indices (SVI), SVI in a light use efficiency model framework, spectral shape characteristics through spectral derivatives and absorption feature analysis, and statistical models leading to Multiband Hyperspectral Indices (MHI) from stepwise regressions and Partial Least Squares Regression (PLSR). Algorithms were able to achieve r2 better than 0.7 for both GEP at the overpass time and daily GEP. These algorithms were successful using a diverse set of observations combining data from multiple years, multiple times during growing season, different times of day, with different view angles, and different vegetation types. The demonstrated robustness of the algorithms presented in this study over these conditions provides some confidence in mapping spatial patterns of GEP, describing variability within fields as well as the regional patterns based only on spectral reflectance information. The ISS orbit provides periods with multiple observations collected at different times of the day within a period of a few days. Diurnal GEP patterns were estimated comparing the half-hourly average GEP from the flux tower against HICO estimates of GEP (r2=0.87) if morning, midday, and afternoon observations were available for average fluxes in the time period.

  15. Multi- and hyperspectral geologic remote sensing: A review

    Science.gov (United States)

    van der Meer, Freek D.; van der Werff, Harald M. A.; van Ruitenbeek, Frank J. A.; Hecker, Chris A.; Bakker, Wim H.; Noomen, Marleen F.; van der Meijde, Mark; Carranza, E. John M.; Smeth, J. Boudewijn de; Woldai, Tsehaie

    2012-02-01

    Geologists have used remote sensing data since the advent of the technology for regional mapping, structural interpretation and to aid in prospecting for ores and hydrocarbons. This paper provides a review of multispectral and hyperspectral remote sensing data, products and applications in geology. During the early days of Landsat Multispectral scanner and Thematic Mapper, geologists developed band ratio techniques and selective principal component analysis to produce iron oxide and hydroxyl images that could be related to hydrothermal alteration. The advent of the Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) with six channels in the shortwave infrared and five channels in the thermal region allowed to produce qualitative surface mineral maps of clay minerals (kaolinite, illite), sulfate minerals (alunite), carbonate minerals (calcite, dolomite), iron oxides (hematite, goethite), and silica (quartz) which allowed to map alteration facies (propylitic, argillic etc.). The step toward quantitative and validated (subpixel) surface mineralogic mapping was made with the advent of high spectral resolution hyperspectral remote sensing. This led to a wealth of techniques to match image pixel spectra to library and field spectra and to unravel mixed pixel spectra to pure endmember spectra to derive subpixel surface compositional information. These products have found their way to the mining industry and are to a lesser extent taken up by the oil and gas sector. The main threat for geologic remote sensing lies in the lack of (satellite) data continuity. There is however a unique opportunity to develop standardized protocols leading to validated and reproducible products from satellite remote sensing for the geology community. By focusing on geologic mapping products such as mineral and lithologic maps, geochemistry, P-T paths, fluid pathways etc. the geologic remote sensing community can bridge the gap with the geosciences community. Increasingly

  16. Detection of hypercholesterolemia using hyperspectral imaging of human skin

    Science.gov (United States)

    Milanic, Matija; Bjorgan, Asgeir; Larsson, Marcus; Strömberg, Tomas; Randeberg, Lise L.

    2015-07-01

    Hypercholesterolemia is characterized by high blood levels of cholesterol and is associated with increased risk of atherosclerosis and cardiovascular disease. Xanthelasma is a subcutaneous lesion appearing in the skin around the eyes. Xanthelasma is related to hypercholesterolemia. Identifying micro-xanthelasma can thereforeprovide a mean for early detection of hypercholesterolemia and prevent onset and progress of disease. The goal of this study was to investigate spectral and spatial characteristics of hypercholesterolemia in facial skin. Optical techniques like hyperspectral imaging (HSI) might be a suitable tool for such characterization as it simultaneously provides high resolution spatial and spectral information. In this study a 3D Monte Carlo model of lipid inclusions in human skin was developed to create hyperspectral images in the spectral range 400-1090 nm. Four lesions with diameters 0.12-1.0 mm were simulated for three different skin types. The simulations were analyzed using three algorithms: the Tissue Indices (TI), the two layer Diffusion Approximation (DA), and the Minimum Noise Fraction transform (MNF). The simulated lesions were detected by all methods, but the best performance was obtained by the MNF algorithm. The results were verified using data from 11 volunteers with known cholesterol levels. The face of the volunteers was imaged by a LCTF system (400- 720 nm), and the images were analyzed using the previously mentioned algorithms. The identified features were then compared to the known cholesterol levels of the subjects. Significant correlation was obtained for the MNF algorithm only. This study demonstrates that HSI can be a promising, rapid modality for detection of hypercholesterolemia.

  17. Progressive sample processing of band selection for hyperspectral imagery

    Science.gov (United States)

    Liu, Keng-Hao; Chien, Hung-Chang; Chen, Shih-Yu

    2017-10-01

    Band selection (BS) is one of the most important topics in hyperspectral image (HSI) processing. The objective of BS is to find a set of representative bands that can represent the whole image with lower inter-band redundancy. Many types of BS algorithms were proposed in the past. However, most of them can be carried on in an off-line manner. It means that they can only be implemented on the pre-collected data. Those off-line based methods are sometime useless for those applications that are timeliness, particular in disaster prevention and target detection. To tackle this issue, a new concept, called progressive sample processing (PSP), was proposed recently. The PSP is an "on-line" framework where the specific type of algorithm can process the currently collected data during the data transmission under band-interleavedby-sample/pixel (BIS/BIP) protocol. This paper proposes an online BS method that integrates a sparse-based BS into PSP framework, called PSP-BS. In PSP-BS, the BS can be carried out by updating BS result recursively pixel by pixel in the same way that a Kalman filter does for updating data information in a recursive fashion. The sparse regression is solved by orthogonal matching pursuit (OMP) algorithm, and the recursive equations of PSP-BS are derived by using matrix decomposition. The experiments conducted on a real hyperspectral image show that the PSP-BS can progressively output the BS status with very low computing time. The convergence of BS results during the transmission can be quickly achieved by using a rearranged pixel transmission sequence. This significant advantage allows BS to be implemented in a real time manner when the HSI data is transmitted pixel by pixel.

  18. Hyperspectral and multispectral bioluminescence optical tomography for small animal imaging

    International Nuclear Information System (INIS)

    Chaudhari, Abhijit J; Darvas, Felix; Bading, James R; Moats, Rex A; Conti, Peter S; Smith, Desmond J; Cherry, Simon R; Leahy, Richard M

    2005-01-01

    For bioluminescence imaging studies in small animals, it is important to be able to accurately localize the three-dimensional (3D) distribution of the underlying bioluminescent source. The spectrum of light produced by the source that escapes the subject varies with the depth of the emission source because of the wavelength-dependence of the optical properties of tissue. Consequently, multispectral or hyperspectral data acquisition should help in the 3D localization of deep sources. In this paper, we describe a framework for fully 3D bioluminescence tomographic image acquisition and reconstruction that exploits spectral information. We describe regularized tomographic reconstruction techniques that use semi-infinite slab or FEM-based diffusion approximations of photon transport through turbid media. Singular value decomposition analysis was used for data dimensionality reduction and to illustrate the advantage of using hyperspectral rather than achromatic data. Simulation studies in an atlas-mouse geometry indicated that sub-millimeter resolution may be attainable given accurate knowledge of the optical properties of the animal. A fixed arrangement of mirrors and a single CCD camera were used for simultaneous acquisition of multispectral imaging data over most of the surface of the animal. Phantom studies conducted using this system demonstrated our ability to accurately localize deep point-like sources and show that a resolution of 1.5 to 2.2 mm for depths up to 6 mm can be achieved. We also include an in vivo study of a mouse with a brain tumour expressing firefly luciferase. Co-registration of the reconstructed 3D bioluminescent image with magnetic resonance images indicated good anatomical localization of the tumour

  19. High speed measurement of corn seed viability using hyperspectral imaging

    Science.gov (United States)

    Ambrose, Ashabahebwa; Kandpal, Lalit Mohan; Kim, Moon S.; Lee, Wang-Hee; Cho, Byoung-Kwan

    2016-03-01

    Corn is one of the most cultivated crops all over world as food for humans as well as animals. Optimized agronomic practices and improved technological interventions during planting, harvesting and post-harvest handling are critical to improving the quantity and quality of corn production. Seed germination and vigor are the primary determinants of high yield notwithstanding any other factors that may play during the growth period. Seed viability may be lost during storage due to unfavorable conditions e.g. moisture content and temperatures, or physical damage during mechanical processing e.g. shelling, or over heating during drying. It is therefore vital for seed companies and farmers to test and ascertain seed viability to avoid losses of any kind. This study aimed at investigating the possibility of using hyperspectral imaging (HSI) technique to discriminate viable and nonviable corn seeds. A group of corn samples were heat treated by using microwave process while a group of seeds were kept as control group (untreated). The hyperspectral images of corn seeds of both groups were captured between 400 and 2500 nm wave range. Partial least squares discriminant analysis (PLS-DA) was built for the classification of aged (heat treated) and normal (untreated) corn seeds. The model showed highest classification accuracy of 97.6% (calibration) and 95.6% (prediction) in the SWIR region of the HSI. Furthermore, the PLS-DA and binary images were capable to provide the visual information of treated and untreated corn seeds. The overall results suggest that HSI technique is accurate for classification of viable and non-viable seeds with non-destructive manner.

  20. The power grid AGC frequency bias coefficient online identification method based on wide area information

    Science.gov (United States)

    Wang, Zian; Li, Shiguang; Yu, Ting

    2015-12-01

    This paper propose online identification method of regional frequency deviation coefficient based on the analysis of interconnected grid AGC adjustment response mechanism of regional frequency deviation coefficient and the generator online real-time operation state by measured data through PMU, analyze the optimization method of regional frequency deviation coefficient in case of the actual operation state of the power system and achieve a more accurate and efficient automatic generation control in power system. Verify the validity of the online identification method of regional frequency deviation coefficient by establishing the long-term frequency control simulation model of two-regional interconnected power system.

  1. AIRBORNE HYPERSPECTRAL IDENTIFICATION OF INVASIVE AND OPPORTUNISTIC WETLANDS PLANT SPECIES

    Science.gov (United States)

    Coastal wetlands are among the most fragmented and disturbed ecosystems and the Great Lakes are no exception. One possible result is the observed increase in the presence and dominance of invasive and other opportunistic plant species, such as the common reed (Phragmites australi...

  2. 78 FR 14084 - Request for Information To Gather Technical Expertise Pertaining to the Identification and...

    Science.gov (United States)

    2013-03-04

    ... Native American students, as English learners. For these reasons this RFI seeks solutions; advice... Expertise Pertaining to the Identification and Placement of Native American Students Who Are English... students in grades K-12 as English learners and to appropriately place these students in language...

  3. 77 FR 16209 - Proposed Information Collection; Comment Request; Permitting, Vessel Identification, and...

    Science.gov (United States)

    2012-03-20

    ... the stocks, assess the effectiveness of management measures, evaluate the benefits and costs of...: Individuals; Business or other for-profit organizations. Estimated Number of Respondents: 30. Estimated Time... identification, 45 minutes. Estimated Total Annual Burden Hours: 265 hours. Estimated Total Annual Cost to Public...

  4. 75 FR 55301 - Proposed Information Collection; Comment Request; Northeast Region Gear Identification Requirements

    Science.gov (United States)

    2010-09-10

    ...., buoys, radar reflectors, or other methods identified in the regulations). The number of gear in the case... be responsible for marking several strings of a given type of gear, or may use multiple different gear types that require marking for identification and visibility. The display of the identifying...

  5. Impact of a radio-frequency identification system and information interchange on clearance processes for cargo at border posts

    Directory of Open Access Journals (Sweden)

    Ernest Bhero

    2015-11-01

    Full Text Available Background: Improved operational efficiency is important to role players in cross-border logistics and trade corridors. Cargo owners and cargo forwarders have been particularly concerned about long delays in the processing and clearing of cargo at border posts. Field studies suggest that these delays are due to a combination of factors, such as a lack of optimum system configurations and non-optimised human-dependent operations, which make the operations prone to corruption and other malpractices. Objectives: This article presents possible strategies for improving some of the operations in this sector. The research hinges on two key questions: (1 what is the impact of information interchange between stakeholders on the cargo transit time and (2 how will cargo transit time be impacted upon by automatic identification of cargo and the status of cargo seals on arriving vehicles at the border? Method: The use of information communication systems enabled by automatic identification systems (incorporating radio-frequency identification technology is suggested. Results: Results obtained by the described simulation model indicate that improvements of up to 82% with regard to transit time are possible using these techniques. Conclusion: The findings therefore demonstrate how operations at border posts can be improved through the use of appropriate technology and configuration of the operations.

  6. Self vs. other: neural correlates underlying agent identification based on unimodal auditory information as revealed by electrotomography (sLORETA).

    Science.gov (United States)

    Justen, C; Herbert, C; Werner, K; Raab, M

    2014-02-14

    Recent neuroscientific studies have identified activity changes in an extensive cerebral network consisting of medial prefrontal cortex, precuneus, temporo-parietal junction, and temporal pole during the perception and identification of self- and other-generated stimuli. Because this network is supposed to be engaged in tasks which require agent identification, it has been labeled the evaluation network (e-network). The present study used self- versus other-generated movement sounds (long jumps) and electroencephalography (EEG) in order to unravel the neural dynamics of agent identification for complex auditory information. Participants (N=14) performed an auditory self-other identification task with EEG. Data was then subjected to a subsequent standardized low-resolution brain electromagnetic tomography (sLORETA) analysis (source localization analysis). Differences between conditions were assessed using t-statistics (corrected for multiple testing) on the normalized and log-transformed current density values of the sLORETA images. Three-dimensional sLORETA source localization analysis revealed cortical activations in brain regions mostly associated with the e-network, especially in the medial prefrontal cortex (bilaterally in the alpha-1-band and right-lateralized in the gamma-band) and the temporo-parietal junction (right hemisphere in the alpha-1-band). Taken together, the findings are partly consistent with previous functional neuroimaging studies investigating unimodal visual or multimodal agent identification tasks (cf. e-network) and extent them to the auditory domain. Cortical activations in brain regions of the e-network seem to have functional relevance, especially the significantly higher cortical activation in the right medial prefrontal cortex. Copyright © 2013 IBRO. Published by Elsevier Ltd. All rights reserved.

  7. Decision Fusion Based on Hyperspectral and Multispectral Satellite Imagery for Accurate Forest Species Mapping

    Directory of Open Access Journals (Sweden)

    Dimitris G. Stavrakoudis

    2014-07-01

    Full Text Available This study investigates the effectiveness of combining multispectral very high resolution (VHR and hyperspectral satellite imagery through a decision fusion approach, for accurate forest species mapping. Initially, two fuzzy classifications are conducted, one for each satellite image, using a fuzzy output support vector machine (SVM. The classification result from the hyperspectral image is then resampled to the multispectral’s spatial resolution and the two sources are combined using a simple yet efficient fusion operator. Thus, the complementary information provided from the two sources is effectively exploited, without having to resort to computationally demanding and time-consuming typical data fusion or vector stacking approaches. The effectiveness of the proposed methodology is validated in a complex Mediterranean forest landscape, comprising spectrally similar and spatially intermingled species. The decision fusion scheme resulted in an accuracy increase of 8% compared to the classification using only the multispectral imagery, whereas the increase was even higher compared to the classification using only the hyperspectral satellite image. Perhaps most importantly, its accuracy was significantly higher than alternative multisource fusion approaches, although the latter are characterized by much higher computation, storage, and time requirements.

  8. Advances in feature selection methods for hyperspectral image processing in food industry applications: a review.

    Science.gov (United States)

    Dai, Qiong; Cheng, Jun-Hu; Sun, Da-Wen; Zeng, Xin-An

    2015-01-01

    There is an increased interest in the applications of hyperspectral imaging (HSI) for assessing food quality, safety, and authenticity. HSI provides abundance of spatial and spectral information from foods by combining both spectroscopy and imaging, resulting in hundreds of contiguous wavebands for each spatial position of food samples, also known as the curse of dimensionality. It is desirable to employ feature selection algorithms for decreasing computation burden and increasing predicting accuracy, which are especially relevant in the development of online applications. Recently, a variety of feature selection algorithms have been proposed that can be categorized into three groups based on the searching strategy namely complete search, heuristic search and random search. This review mainly introduced the fundamental of each algorithm, illustrated its applications in hyperspectral data analysis in the food field, and discussed the advantages and disadvantages of these algorithms. It is hoped that this review should provide a guideline for feature selections and data processing in the future development of hyperspectral imaging technique in foods.

  9. Hyperspectral optical imaging of human iris in vivo: characteristics of reflectance spectra

    Science.gov (United States)

    Medina, José M.; Pereira, Luís M.; Correia, Hélder T.; Nascimento, Sérgio M. C.

    2011-07-01

    We report a hyperspectral imaging system to measure the reflectance spectra of real human irises with high spatial resolution. A set of ocular prosthesis was used as the control condition. Reflectance data were decorrelated by the principal-component analysis. The main conclusion is that spectral complexity of the human iris is considerable: between 9 and 11 principal components are necessary to account for 99% of the cumulative variance in human irises. Correcting image misalignments associated with spontaneous ocular movements did not influence this result. The data also suggests a correlation between the first principal component and different levels of melanin present in the irises. It was also found that although the spectral characteristics of the first five principal components were not affected by the radial and angular position of the selected iridal areas, they affect the higher-order ones, suggesting a possible influence of the iris texture. The results show that hyperspectral imaging in the iris, together with adequate spectroscopic analyses provide more information than conventional colorimetric methods, making hyperspectral imaging suitable for the characterization of melanin and the noninvasive diagnosis of ocular diseases and iris color.

  10. Radiometric Correction of Multitemporal Hyperspectral Uas Image Mosaics of Seedling Stands

    Science.gov (United States)

    Markelin, L.; Honkavaara, E.; Näsi, R.; Viljanen, N.; Rosnell, T.; Hakala, T.; Vastaranta, M.; Koivisto, T.; Holopainen, M.

    2017-10-01

    Novel miniaturized multi- and hyperspectral imaging sensors on board of unmanned aerial vehicles have recently shown great potential in various environmental monitoring and measuring tasks such as precision agriculture and forest management. These systems can be used to collect dense 3D point clouds and spectral information over small areas such as single forest stands or sample plots. Accurate radiometric processing and atmospheric correction is required when data sets from different dates and sensors, collected in varying illumination conditions, are combined. Performance of novel radiometric block adjustment method, developed at Finnish Geospatial Research Institute, is evaluated with multitemporal hyperspectral data set of seedling stands collected during spring and summer 2016. Illumination conditions during campaigns varied from bright to overcast. We use two different methods to produce homogenous image mosaics and hyperspectral point clouds: image-wise relative correction and image-wise relative correction with BRDF. Radiometric datasets are converted to reflectance using reference panels and changes in reflectance spectra is analysed. Tested methods improved image mosaic homogeneity by 5 % to 25 %. Results show that the evaluated method can produce consistent reflectance mosaics and reflectance spectra shape between different areas and dates.

  11. 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.

  12. RADIOMETRIC CORRECTION OF MULTITEMPORAL HYPERSPECTRAL UAS IMAGE MOSAICS OF SEEDLING STANDS

    Directory of Open Access Journals (Sweden)

    L. Markelin

    2017-10-01

    Full Text Available Novel miniaturized multi- and hyperspectral imaging sensors on board of unmanned aerial vehicles have recently shown great potential in various environmental monitoring and measuring tasks such as precision agriculture and forest management. These systems can be used to collect dense 3D point clouds and spectral information over small areas such as single forest stands or sample plots. Accurate radiometric processing and atmospheric correction is required when data sets from different dates and sensors, collected in varying illumination conditions, are combined. Performance of novel radiometric block adjustment method, developed at Finnish Geospatial Research Institute, is evaluated with multitemporal hyperspectral data set of seedling stands collected during spring and summer 2016. Illumination conditions during campaigns varied from bright to overcast. We use two different methods to produce homogenous image mosaics and hyperspectral point clouds: image-wise relative correction and image-wise relative correction with BRDF. Radiometric datasets are converted to reflectance using reference panels and changes in reflectance spectra is analysed. Tested methods improved image mosaic homogeneity by 5 % to 25 %. Results show that the evaluated method can produce consistent reflectance mosaics and reflectance spectra shape between different areas and dates.

  13. Geodesic Flow Kernel Support Vector Machine for Hyperspectral Image Classification by Unsupervised Subspace Feature Transfer

    Directory of Open Access Journals (Sweden)

    Alim Samat

    2016-03-01

    Full Text Available In order to deal with scenarios where the training data, used to deduce a model, and the validation data have different statistical distributions, we study the problem of transformed subspace feature transfer for domain adaptation (DA in the context of hyperspectral image classification via a geodesic Gaussian flow kernel based support vector machine (GFKSVM. To show the superior performance of the proposed approach, conventional support vector machines (SVMs and state-of-the-art DA algorithms, including information-theoretical learning of discriminative cluster for domain adaptation (ITLDC, joint distribution adaptation (JDA, and joint transfer matching (JTM, are also considered. Additionally, unsupervised linear and nonlinear subspace feature transfer techniques including principal component analysis (PCA, randomized nonlinear principal component analysis (rPCA, factor analysis (FA and non-negative matrix factorization (NNMF are investigated and compared. Experiments on two real hyperspectral images show the cross-image classification performances of the GFKSVM, confirming its effectiveness and suitability when applied to hyperspectral images.

  14. Research on hyperspectral dynamic scene and image sequence simulation

    Science.gov (United States)

    Sun, Dandan; Liu, Fang; Gao, Jiaobo; Sun, Kefeng; Hu, Yu; Li, Yu; Xie, Junhu; Zhang, Lei

    2016-10-01

    This paper presents a simulation method of hyperspectral dynamic scene and image sequence for hyperspectral equipment evaluation and target detection algorithm. Because of high spectral resolution, strong band continuity, anti-interference and other advantages, in recent years, hyperspectral imaging technology has been rapidly developed and is widely used in many areas such as optoelectronic target detection, military defense and remote sensing systems. Digital imaging simulation, as a crucial part of hardware in loop simulation, can be applied to testing and evaluation hyperspectral imaging equipment with lower development cost and shorter development period. Meanwhile, visual simulation can produce a lot of original image data under various conditions for hyperspectral image feature extraction and classification algorithm. Based on radiation physic model and material characteristic parameters this paper proposes a generation method of digital scene. By building multiple sensor models under different bands and different bandwidths, hyperspectral scenes in visible, MWIR, LWIR band, with spectral resolution 0.01μm, 0.05μm and 0.1μm have been simulated in this paper. The final dynamic scenes have high real-time and realistic, with frequency up to 100 HZ. By means of saving all the scene gray data in the same viewpoint image sequence is obtained. The analysis results show whether in the infrared band or the visible band, the grayscale variations of simulated hyperspectral images are consistent with the theoretical analysis results.

  15. Reconfigurable Hardware for Compressing Hyperspectral Image Data

    Science.gov (United States)

    Aranki, Nazeeh; Namkung, Jeffrey; Villapando, Carlos; Kiely, Aaron; Klimesh, Matthew; Xie, Hua

    2010-01-01

    High-speed, low-power, reconfigurable electronic hardware has been developed to implement ICER-3D, an algorithm for compressing hyperspectral-image data. The algorithm and parts thereof have been the topics of several NASA Tech Briefs articles, including Context Modeler for Wavelet Compression of Hyperspectral Images (NPO-43239) and ICER-3D Hyperspectral Image Compression Software (NPO-43238), which appear elsewhere in this issue of NASA Tech Briefs. As described in more detail in those articles, the algorithm includes three main subalgorithms: one for computing wavelet transforms, one for context modeling, and one for entropy encoding. For the purpose of designing the hardware, these subalgorithms are treated as modules to be implemented efficiently in field-programmable gate arrays (FPGAs). The design takes advantage of industry- standard, commercially available FPGAs. The implementation targets the Xilinx Virtex II pro architecture, which has embedded PowerPC processor cores with flexible on-chip bus architecture. It incorporates an efficient parallel and pipelined architecture to compress the three-dimensional image data. The design provides for internal buffering to minimize intensive input/output operations while making efficient use of offchip memory. The design is scalable in that the subalgorithms are implemented as independent hardware modules that can be combined in parallel to increase throughput. The on-chip processor manages the overall operation of the compression system, including execution of the top-level control functions as well as scheduling, initiating, and monitoring processes. The design prototype has been demonstrated to be capable of compressing hyperspectral data at a rate of 4.5 megasamples per second at a conservative clock frequency of 50 MHz, with a potential for substantially greater throughput at a higher clock frequency. The power consumption of the prototype is less than 6.5 W. The reconfigurability (by means of reprogramming) of

  16. Inland excess water mapping using hyperspectral imagery

    Directory of Open Access Journals (Sweden)

    Csendes Bálint

    2016-01-01

    Full Text Available Hyperspectral imaging combined with the potentials of airborne scanning is a powerful tool to monitor environmental processes. The aim of this research was to use high resolution remotely sensed data to map the spatial extent of inland excess water patches in a Hungarian study area that is known for its oil and gas production facilities. Periodic floodings show high spatial and temporal variability, nevertheless, former studies have proven that the affected soil surfaces can be accurately identified. Besides separability measurements, we performed spectral angle classification, which gave a result of 85% overall accuracy and we also compared the generated land cover map with LIDAR elevation data.

  17. Snapshot hyperspectral imaging and practical applications

    International Nuclear Information System (INIS)

    Wong, G

    2009-01-01

    Traditional broadband imaging involves the digital representation of a remote scene within a reduced colour space. Hyperspectral imaging exploits the full spectral dimension, which better reflects the continuous nature of actual spectra. Conventional techniques are all time-delayed whereby spatial or spectral scanning is required for hypercube generation. An innovative and patented technique developed at Heriot-Watt University offers significant potential as a snapshot sensor, to enable benefits for the wider public beyond aerospace imaging. This student-authored paper seeks to promote awareness of this field within the photonic community and its potential advantages for real-time practical applications.

  18. ANOMALY DETECTION AND COMPARATIVE ANALYSIS OF HYDROTHERMAL ALTERATION MATERIALS TROUGH HYPERSPECTRAL MULTISENSOR DATA IN THE TURRIALBA VOLCANO

    Directory of Open Access Journals (Sweden)

    J. G. Rejas

    2012-07-01

    Full Text Available The aim of this work is the comparative study of the presence of hydrothermal alteration materials in the Turrialba volcano (Costa Rica in relation with computed spectral anomalies from multitemporal and multisensor data adquired in spectral ranges of the visible (VIS, short wave infrared (SWIR and thermal infrared (TIR. We used for this purposes hyperspectral and multispectral images from the HyMAP and MASTER airborne sensors, and ASTER and Hyperion scenes in a period between 2002 and 2010. Field radiometry was applied in order to remove the atmospheric contribution in an empirical line method. HyMAP and MASTER images were georeferenced directly thanks to positioning and orientation data that were measured at the same time in the acquisition campaign from an inertial system based on GPS/IMU. These two important steps were allowed the identification of spectral diagnostic bands of hydrothermal alteration minerals and the accuracy spatial correlation. Enviromental impact of the volcano activity has been studied through different vegetation indexes and soil patterns. Have been mapped hydrothermal materials in the crater of the volcano, in fact currently active, and their surrounding carrying out a principal components analysis differentiated for a high and low absorption bands to characterize accumulations of kaolinite, illite, alunite and kaolinite+smectite, delimitating zones with the presence of these minerals. Spectral anomalies have been calculated on a comparative study of methods pixel and subpixel focused in thermal bands fused with high-resolution images. Results are presented as an approach based on expert whose main interest lies in the automated identification of patterns of hydrothermal altered materials without prior knowledge or poor information on the area.

  19. Anomaly Detection and Comparative Analysis of Hydrothermal Alteration Materials Trough Hyperspectral Multisensor Data in the Turrialba Volcano

    Science.gov (United States)

    Rejas, J. G.; Martínez-Frías, J.; Bonatti, J.; Martínez, R.; Marchamalo, M.

    2012-07-01

    The aim of this work is the comparative study of the presence of hydrothermal alteration materials in the Turrialba volcano (Costa Rica) in relation with computed spectral anomalies from multitemporal and multisensor data adquired in spectral ranges of the visible (VIS), short wave infrared (SWIR) and thermal infrared (TIR). We used for this purposes hyperspectral and multispectral images from the HyMAP and MASTER airborne sensors, and ASTER and Hyperion scenes in a period between 2002 and 2010. Field radiometry was applied in order to remove the atmospheric contribution in an empirical line method. HyMAP and MASTER images were georeferenced directly thanks to positioning and orientation data that were measured at the same time in the acquisition campaign from an inertial system based on GPS/IMU. These two important steps were allowed the identification of spectral diagnostic bands of hydrothermal alteration minerals and the accuracy spatial correlation. Enviromental impact of the volcano activity has been studied through different vegetation indexes and soil patterns. Have been mapped hydrothermal materials in the crater of the volcano, in fact currently active, and their surrounding carrying out a principal components analysis differentiated for a high and low absorption bands to characterize accumulations of kaolinite, illite, alunite and kaolinite+smectite, delimitating zones with the presence of these minerals. Spectral anomalies have been calculated on a comparative study of methods pixel and subpixel focused in thermal bands fused with high-resolution images. Results are presented as an approach based on expert whose main interest lies in the automated identification of patterns of hydrothermal altered materials without prior knowledge or poor information on the area.

  20. Models of information markets: Analysis of markets, identification of services, and design models

    NARCIS (Netherlands)

    Wijnhoven, Alphonsus B.J.M.

    2001-01-01

    The Internet reduces much of the costs of information sharing, but it does not solve information receivers’ reading and interpretation limitations. Search engines ease information retrieval but do not solve the problems of specifying information needs and evaluating retrieval results. This article

  1. Contraction of information and its inverse problem in reactor system identification and stochastic diagnosis

    International Nuclear Information System (INIS)

    Kishida, K.

    1996-01-01

    Research concerning power reactor noise analysis makes rapid progress in the areas of the system identification, prediction and diagnosis. Keywords in these studies are artificial intelligence, neural network, fuzzy, and chaos. Nonlinear, nonstationary, or non-Gaussian processes as well as linear and steady processes are also studied in fluctuation analysis. However, we have not enough time to study a fundamental theory, since we are urged to obtain results or applications in power reactor fluctuations. Furthermore, we have no systematic approach to handle observed time series data in the linear process, since power reactor noise phenomena are complicated. Hence, it is important to study it from the fundamental viewpoint. It is a main aim of the present review paper to describe a unified formalism for reactor system identification and stochastic diagnosis

  2. The Federal University of Minas Gerais into context of open access to scientific information: identification of their information systems

    Directory of Open Access Journals (Sweden)

    Ráisa Mendes Fernandes de Souza

    2014-12-01

    Full Text Available The obstacles faced by the scientific community in the dissemination and access to their own productions, contextualized in need of open access to scientific information, boosted the creation of Institutional Repositories. These are technologies adopted by educational institutions and research that aim to manage and provide scientific production site. Objective: to characterize Federal University of Minas Gerais’ information systems, analyzing the perception of the actors responsible for their existence / maintenance within the context of open access to scientific information. Methodology: This is a descriptive and qualitative research, which is engaged in the information systems developed within the university to support the teaching, research and extension, which contain the records of productions or productions, scientific or not, the local academic community. Results: we found some awareness of the actors interviewed in relation to the existence of other systems, although such awareness is not ideal. Generally, UFMG has Managers and depositors competent enough to manage a repository greater, as is the case of an RI. Conclusion: It is necessary an information policy that is born of consolidated scientific sectors hierarchically superior and inferior sectors to be transferred to, well, be possible to articulate the entire university community in support of a common cause. The study emphasizes the community of UFMG and optimization of open access to publications of the University as the scope for possible future studies.

  3. Identification of Classified Information in Unclassified DoD Systems During the Audit of Internal Controls and Data Reliability in the Deployable Disbursing System

    Science.gov (United States)

    2009-02-17

    Identification of Classified Information in Unclassified DoD Systems During the Audit of Internal Controls and Data Reliability in the Deployable...TITLE AND SUBTITLE Identification of Classified Information in Unclassified DoD Systems During the Audit of Internal Controls and Data Reliability...Systems During the Audit ofInternal Controls and Data Reliability in the Deployable Disbursing System (Report No. D-2009-054) Weare providing this

  4. Extraction of Plant Physiological Status from Hyperspectral Signatures Using Machine Learning Methods

    Directory of Open Access Journals (Sweden)

    Daniel Doktor

    2014-12-01

    Full Text Available The machine learning method, random forest (RF, is applied in order to derive biophysical and structural vegetation parameters from hyperspectral signatures. Hyperspectral data are, among other things, characterized by their high dimensionality and autocorrelation. Common multivariate regression approaches, which usually include only a limited number of spectral indices as predictors, do not make full use of the available information. In contrast, machine learning methods, such as RF, are supposed to be better suited to extract information on vegetation status. First, vegetation parameters are extracted from hyperspectral signatures simulated with the radiative transfer model, PROSAIL. Second, the transferability of these results with respect to laboratory and field measurements is investigated. In situ observations of plant physiological parameters and corresponding spectra are gathered in the laboratory for summer barley (Hordeum vulgare. Field in situ measurements focus on winter crops over several growing seasons. Chlorophyll content, Leaf Area Index and phenological growth stages are derived from simulated and measured spectra. RF performs very robustly and with a very high accuracy on PROSAIL simulated data. Furthermore, it is almost unaffected by introduced noise and bias in the data. When applied to laboratory data, the prediction accuracy is still good (C\\(_{ab}\\: \\(R^2\\ = 0.94/ LAI: \\(R^2\\ = 0.80/BBCH (Growth stages of mono-and dicotyledonous plants : \\(R^2\\ = 0.91, but not as high as for simulated spectra. Transferability to field measurements is given with prediction levels as high as for laboratory data (C\\(_{ab}\\: \\(R^2\\ = 0.89/LAI: \\(R^2\\ = 0.89/BBCH: \\(R^2\\ = \\(\\sim\\0.8. Wavelengths for deriving plant physiological status based on simulated and measured hyperspectral signatures are mostly selected from appropriate spectral regions (both field and laboratory: 700–800 nm regressing on C\\(_{ab}\\ and 800–1300

  5. An algorithm for hyperspectral remote sensing of aerosols: 1. Development of theoretical framework

    International Nuclear Information System (INIS)

    Hou, Weizhen; Wang, Jun; Xu, Xiaoguang; Reid, Jeffrey S.; Han, Dong

    2016-01-01

    This paper describes the first part of a series of investigations to develop algorithms for simultaneous retrieval of aerosol parameters and surface reflectance from a newly developed hyperspectral instrument, the GEOstationary Trace gas and Aerosol Sensor Optimization (GEO-TASO), by taking full advantage of available hyperspectral measurement information in the visible bands. We describe the theoretical framework of an inversion algorithm for the hyperspectral remote sensing of the aerosol optical properties, in which major principal components (PCs) for surface reflectance is assumed known, and the spectrally dependent aerosol refractive indices are assumed to follow a power-law approximation with four unknown parameters (two for real and two for imaginary part of refractive index). New capabilities for computing the Jacobians of four Stokes parameters of reflected solar radiation at the top of the atmosphere with respect to these unknown aerosol parameters and the weighting coefficients for each PC of surface reflectance are added into the UNified Linearized Vector Radiative Transfer Model (UNL-VRTM), which in turn facilitates the optimization in the inversion process. Theoretical derivations of the formulas for these new capabilities are provided, and the analytical solutions of Jacobians are validated against the finite-difference calculations with relative error less than 0.2%. Finally, self-consistency check of the inversion algorithm is conducted for the idealized green-vegetation and rangeland surfaces that were spectrally characterized by the U.S. Geological Survey digital spectral library. It shows that the first six PCs can yield the reconstruction of spectral surface reflectance with errors less than 1%. Assuming that aerosol properties can be accurately characterized, the inversion yields a retrieval of hyperspectral surface reflectance with an uncertainty of 2% (and root-mean-square error of less than 0.003), which suggests self-consistency in the

  6. Lncident: A Tool for Rapid Identification of Long Noncoding RNAs Utilizing Sequence Intrinsic Composition and Open Reading Frame Information

    Directory of Open Access Journals (Sweden)

    Siyu Han

    2016-01-01

    Full Text Available More and more studies have demonstrated that long noncoding RNAs (lncRNAs play critical roles in diversity of biological process and are also associated with various types of disease. How to rapidly identify lncRNAs and messenger RNA is the fundamental step to uncover the function of lncRNAs identification. Here, we present a novel method for rapid identification of lncRNAs utilizing sequence intrinsic composition features and open reading frame information based on support vector machine model, named as Lncident (LncRNAs identification. The 10-fold cross-validation and ROC curve are used to evaluate the performance of Lncident. The main advantage of Lncident is high speed without the loss of accuracy. Compared with the exiting popular tools, Lncident outperforms Coding-Potential Calculator, Coding-Potential Assessment Tool, Coding-Noncoding Index, and PLEK. Lncident is also much faster than Coding-Potential Calculator and Coding-Noncoding Index. Lncident presents an outstanding performance on microorganism, which offers a great application prospect to the analysis of microorganism. In addition, Lncident can be trained by users’ own collected data. Furthermore, R package and web server are simultaneously developed in order to maximize the convenience for the users. The R package “Lncident” can be easily installed on multiple operating system platforms, as long as R is supported.

  7. Dried fruits quality assessment by hyperspectral imaging

    Science.gov (United States)

    Serranti, Silvia; Gargiulo, Aldo; Bonifazi, Giuseppe

    2012-05-01

    Dried fruits products present different market values according to their quality. Such a quality is usually quantified in terms of freshness of the products, as well as presence of contaminants (pieces of shell, husk, and small stones), defects, mould and decays. The combination of these parameters, in terms of relative presence, represent a fundamental set of attributes conditioning dried fruits humans-senses-detectable-attributes (visual appearance, organolectic properties, etc.) and their overall quality in terms of marketable products. Sorting-selection strategies exist but sometimes they fail when a higher degree of detection is required especially if addressed to discriminate between dried fruits of relatively small dimensions and when aiming to perform an "early detection" of pathogen agents responsible of future moulds and decays development. Surface characteristics of dried fruits can be investigated by hyperspectral imaging (HSI). In this paper, specific and "ad hoc" applications addressed to propose quality detection logics, adopting a hyperspectral imaging (HSI) based approach, are described, compared and critically evaluated. Reflectance spectra of selected dried fruits (hazelnuts) of different quality and characterized by the presence of different contaminants and defects have been acquired by a laboratory device equipped with two HSI systems working in two different spectral ranges: visible-near infrared field (400-1000 nm) and near infrared field (1000-1700 nm). The spectra have been processed and results evaluated adopting both a simple and fast wavelength band ratio approach and a more sophisticated classification logic based on principal component (PCA) analysis.

  8. Hyperspectral monitoring of chemically sensitive plant sentinels

    Science.gov (United States)

    Simmons, Danielle A.; Kerekes, John P.; Raqueno, Nina G.

    2009-08-01

    Automated detection of chemical threats is essential for an early warning of a potential attack. Harnessing plants as bio-sensors allows for distributed sensing without a power supply. Monitoring the bio-sensors requires a specifically tailored hyperspectral system. Tobacco plants have been genetically engineered to de-green when a material of interest (e.g. zinc, TNT) is introduced to their immediate vicinity. The reflectance spectra of the bio-sensors must be accurately characterized during the de-greening process for them to play a role in an effective warning system. Hyperspectral data have been collected under laboratory conditions to determine the key regions in the reflectance spectra associated with the degreening phenomenon. Bio-sensor plants and control (nongenetically engineered) plants were exposed to TNT over the course of two days and their spectra were measured every six hours. Rochester Institute of Technologys Digital Imaging and Remote Sensing Image Generation Model (DIRSIG) was used to simulate detection of de-greened plants in the field. The simulated scene contains a brick school building, sidewalks, trees and the bio-sensors placed at the entrances to the buildings. Trade studies of the bio-sensor monitoring system were also conducted using DIRSIG simulations. System performance was studied as a function of field of view, pixel size, illumination conditions, radiometric noise, spectral waveband dependence and spectral resolution. Preliminary results show that the most significant change in reflectance during the degreening period occurs in the near infrared region.

  9. Maximum Margin Clustering of Hyperspectral Data

    Science.gov (United States)

    Niazmardi, S.; Safari, A.; Homayouni, S.

    2013-09-01

    In recent decades, large margin methods such as Support Vector Machines (SVMs) are supposed to be the state-of-the-art of supervised learning methods for classification of hyperspectral data. However, the results of these algorithms mainly depend on the quality and quantity of available training data. To tackle down the problems associated with the training data, the researcher put effort into extending the capability of large margin algorithms for unsupervised learning. One of the recent proposed algorithms is Maximum Margin Clustering (MMC). The MMC is an unsupervised SVMs algorithm that simultaneously estimates both the labels and the hyperplane parameters. Nevertheless, the optimization of the MMC algorithm is a non-convex problem. Most of the existing MMC methods rely on the reformulating and the relaxing of the non-convex optimization problem as semi-definite programs (SDP), which are computationally very expensive and only can handle small data sets. Moreover, most of these algorithms are two-class classification, which cannot be used for classification of remotely sensed data. In this paper, a new MMC algorithm is used that solve the original non-convex problem using Alternative Optimization method. This algorithm is also extended for multi-class classification and its performance is evaluated. The results of the proposed algorithm show that the algorithm has acceptable results for hyperspectral data clustering.

  10. Geometric correction of APEX hyperspectral data

    Directory of Open Access Journals (Sweden)

    Vreys Kristin

    2016-03-01

    Full Text Available Hyperspectral imagery originating from airborne sensors is nowadays widely used for the detailed characterization of land surface. The correct mapping of the pixel positions to ground locations largely contributes to the success of the applications. Accurate geometric correction, also referred to as “orthorectification”, is thus an important prerequisite which must be performed prior to using airborne imagery for evaluations like change detection, or mapping or overlaying the imagery with existing data sets or maps. A so-called “ortho-image” provides an accurate representation of the earth’s surface, having been adjusted for lens distortions, camera tilt and topographic relief. In this paper, we describe the different steps in the geometric correction process of APEX hyperspectral data, as applied in the Central Data Processing Center (CDPC at the Flemish Institute for Technological Research (VITO, Mol, Belgium. APEX ortho-images are generated through direct georeferencing of the raw images, thereby making use of sensor interior and exterior orientation data, boresight calibration data and elevation data. They can be referenced to any userspecified output projection system and can be resampled to any output pixel size.

  11. APEX - the Hyperspectral ESA Airborne Prism Experiment

    Directory of Open Access Journals (Sweden)

    Koen Meuleman

    2008-10-01

    Full Text Available The airborne ESA-APEX (Airborne Prism Experiment hyperspectral mission simulator is described with its distinct specifications to provide high quality remote sensing data. The concept of an automatic calibration, performed in the Calibration Home Base (CHB by using the Control Test Master (CTM, the In-Flight Calibration facility (IFC, quality flagging (QF and specific processing in a dedicated Processing and Archiving Facility (PAF, and vicarious calibration experiments are presented. A preview on major applications and the corresponding development efforts to provide scientific data products up to level 2/3 to the user is presented for limnology, vegetation, aerosols, general classification routines and rapid mapping tasks. BRDF (Bidirectional Reflectance Distribution Function issues are discussed and the spectral database SPECCHIO (Spectral Input/Output introduced. The optical performance as well as the dedicated software utilities make APEX a state-of-the-art hyperspectral sensor, capable of (a satisfying the needs of several research communities and (b helping the understanding of the Earth’s complex mechanisms.

  12. 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.

  13. Hyperspectral chemical plume detection algorithms based on multidimensional iterative filtering decomposition.

    Science.gov (United States)

    Cicone, A; Liu, J; Zhou, H

    2016-04-13

    Chemicals released in the air can be extremely dangerous for human beings and the environment. Hyperspectral images can be used to identify chemical plumes, however the task can be extremely challenging. Assuming we know a priori that some chemical plume, with a known frequency spectrum, has been photographed using a hyperspectral sensor, we can use standard techniques such as the so-called matched filter or adaptive cosine estimator, plus a properly chosen threshold value, to identify the position of the chemical plume. However, due to noise and inadequate sensing, the accurate identification of chemical pixels is not easy even in this apparently simple situation. In this paper, we present a post-processing tool that, in a completely adaptive and data-driven fashion, allows us to improve the performance of any classification methods in identifying the boundaries of a plume. This is done using the multidimensional iterative filtering (MIF) algorithm (Cicone et al. 2014 (http://arxiv.org/abs/1411.6051); Cicone & Zhou 2015 (http://arxiv.org/abs/1507.07173)), which is a non-stationary signal decomposition method like the pioneering empirical mode decomposition method (Huang et al. 1998 Proc. R. Soc. Lond. A 454, 903. (doi:10.1098/rspa.1998.0193)). Moreover, based on the MIF technique, we propose also a pre-processing method that allows us to decorrelate and mean-centre a hyperspectral dataset. The cosine similarity measure, which often fails in practice, appears to become a successful and outperforming classifier when equipped with such a pre-processing method. We show some examples of the proposed methods when applied to real-life problems. © 2016 The Author(s).

  14. Inclusion probability for DNA mixtures is a subjective one-sided match statistic unrelated to identification information.

    Science.gov (United States)

    Perlin, Mark William

    2015-01-01

    DNA mixtures of two or more people are a common type of forensic crime scene evidence. A match statistic that connects the evidence to a criminal defendant is usually needed for court. Jurors rely on this strength of match to help decide guilt or innocence. However, the reliability of unsophisticated match statistics for DNA mixtures has been questioned. The most prevalent match statistic for DNA mixtures is the combined probability of inclusion (CPI), used by crime labs for over 15 years. When testing 13 short tandem repeat (STR) genetic loci, the CPI(-1) value is typically around a million, regardless of DNA mixture composition. However, actual identification information, as measured by a likelihood ratio (LR), spans a much broader range. This study examined probability of inclusion (PI) mixture statistics for 517 locus experiments drawn from 16 reported cases and compared them with LR locus information calculated independently on the same data. The log(PI(-1)) values were examined and compared with corresponding log(LR) values. The LR and CPI methods were compared in case examples of false inclusion, false exclusion, a homicide, and criminal justice outcomes. Statistical analysis of crime laboratory STR data shows that inclusion match statistics exhibit a truncated normal distribution having zero center, with little correlation to actual identification information. By the law of large numbers (LLN), CPI(-1) increases with the number of tested genetic loci, regardless of DNA mixture composition or match information. These statistical findings explain why CPI is relatively constant, with implications for DNA policy, criminal justice, cost of crime, and crime prevention. Forensic crime laboratories have generated CPI statistics on hundreds of thousands of DNA mixture evidence items. However, this commonly used match statistic behaves like a random generator of inclusionary values, following the LLN rather than measuring identification information. A quantitative

  15. Inclusion probability for DNA mixtures is a subjective one-sided match statistic unrelated to identification information

    Directory of Open Access Journals (Sweden)

    Mark William Perlin

    2015-01-01

    Full Text Available Background: DNA mixtures of two or more people are a common type of forensic crime scene evidence. A match statistic that connects the evidence to a criminal defendant is usually needed for court. Jurors rely on this strength of match to help decide guilt or innocence. However, the reliability of unsophisticated match statistics for DNA mixtures has been questioned. Materials and Methods: The most prevalent match statistic for DNA mixtures is the combined probability of inclusion (CPI, used by crime labs for over 15 years. When testing 13 short tandem repeat (STR genetic loci, the CPI -1 value is typically around a million, regardless of DNA mixture composition. However, actual identification information, as measured by a likelihood ratio (LR, spans a much broader range. This study examined probability of inclusion (PI mixture statistics for 517 locus experiments drawn from 16 reported cases and compared them with LR locus information calculated independently on the same data. The log(PI -1 values were examined and compared with corresponding log(LR values. Results: The LR and CPI methods were compared in case examples of false inclusion, false exclusion, a homicide, and criminal justice outcomes. Statistical analysis of crime laboratory STR data shows that inclusion match statistics exhibit a truncated normal distribution having zero center, with little correlation to actual identification information. By the law of large numbers (LLN, CPI -1 increases with the number of tested genetic loci, regardless of DNA mixture composition or match information. These statistical findings explain why CPI is relatively constant, with implications for DNA policy, criminal justice, cost of crime, and crime prevention. Conclusions: Forensic crime laboratories have generated CPI statistics on hundreds of thousands of DNA mixture evidence items. However, this commonly used match statistic behaves like a random generator of inclusionary values, following the LLN

  16. Hyperspectral stimulated emission depletion microscopy and methods of use thereof

    Science.gov (United States)

    Timlin, Jerilyn A; Aaron, Jesse S

    2014-04-01

    A hyperspectral stimulated emission depletion ("STED") microscope system for high-resolution imaging of samples labeled with multiple fluorophores (e.g., two to ten fluorophores). The hyperspectral STED microscope includes a light source, optical systems configured for generating an excitation light beam and a depletion light beam, optical systems configured for focusing the excitation and depletion light beams on a sample, and systems for collecting and processing data generated by interaction of the excitation and depletion light beams with the sample. Hyperspectral STED data may be analyzed using multivariate curve resolution analysis techniques to deconvolute emission from the multiple fluorophores. The hyperspectral STED microscope described herein can be used for multi-color, subdiffraction imaging of samples (e.g., materials and biological materials) and for analyzing a tissue by Forster Resonance Energy Transfer ("FRET").

  17. Upconversion applied for mid-IR hyperspectral image acquisition

    DEFF Research Database (Denmark)

    Tidemand-Lichtenberg, Peter; Kehlet, Louis Martinus; Sanders, Nicolai Højer

    2015-01-01

    Different schemes for upconversion mid-IR hyperspectral imaging is implemented and compared in terms of spectral coverage, spectral resolution, speed and noise. Phasematch scanning and scanning of the object within the field of view is considered....

  18. A survey of landmine detection using hyperspectral imaging

    Science.gov (United States)

    Makki, Ihab; Younes, Rafic; Francis, Clovis; Bianchi, Tiziano; Zucchetti, Massimo

    2017-02-01

    Hyperspectral imaging is a trending technique in remote sensing that finds its application in many different areas, such as agriculture, mapping, target detection, food quality monitoring, etc. This technique gives the ability to remotely identify the composition of each pixel of the image. Therefore, it is a natural candidate for the purpose of landmine detection, thanks to its inherent safety and fast response time. In this paper, we will present the results of several studies that employed hyperspectral imaging for the purpose of landmine detection, discussing the different signal processing techniques used in this framework for hyperspectral image processing and target detection. Our purpose is to highlight the progresses attained in the detection of landmines using hyperspectral imaging and to identify possible perspectives for future work, in order to achieve a better detection in real-time operation mode.

  19. Parallel Hyperspectral Image Processing on Distributed Multi-Cluster Systems

    NARCIS (Netherlands)

    Liu, F.; Seinstra, F.J.; Plaza, A.J.

    2011-01-01

    Computationally efficient processing of hyperspectral image cubes can be greatly beneficial in many application domains, including environmental modeling, risk/hazard prevention and response, and defense/security. As individual cluster computers often cannot satisfy the computational demands of

  20. Automated Feature Extraction from Hyperspectral Imagery, Phase II

    Data.gov (United States)

    National Aeronautics and Space Administration — The proposed activities will result in the development of a novel hyperspectral feature-extraction toolkit that will provide a simple, automated, and accurate...

  1. Automated Feature Extraction from Hyperspectral Imagery, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — In response to NASA Topic S7.01, Visual Learning Systems, Inc. (VLS) will develop a novel hyperspectral plug-in toolkit for its award winning Feature AnalystREG...

  2. Pubertal changes in emotional information processing: pupillary, behavioral, and subjective evidence during emotional word identification.

    Science.gov (United States)

    Silk, Jennifer S; Siegle, Greg J; Whalen, Diana J; Ostapenko, Laura J; Ladouceur, Cecile D; Dahl, Ronald E

    2009-01-01

    This study investigated pupillary and behavioral responses to an emotional word valence identification paradigm among 32 pre-/early pubertal and 34 mid-/late pubertal typically developing children and adolescents. Participants were asked to identify the valence of positive, negative, and neutral words while pupil dilation was assessed using an eyetracker. Mid-/late pubertal children showed greater peak pupillary reactivity to words presented during the emotional word identification task than pre-/early pubertal children, regardless of word valence. Mid-/late pubertal children also showed smaller sustained pupil dilation than pre-/early pubertal children after the word was no longer on screen. These findings were replicated controlling for participants' age. In addition, mid-/late pubertal children had faster reaction times to all words, and rated themselves as more emotional during their laboratory visit compared to pre-/early pubertal children. Greater recall of emotional words following the task was associated with mid-/late pubertal status, and greater recall of emotional words was also associated with higher peak pupil dilation. These results provide physiological, behavioral, and subjective evidence consistent with a model of puberty-specific changes in neurobehavioral systems underpinning emotional reactivity.

  3. Identification and Analysis of Multi-tasking Product Information Search Sessions with Query Logs

    Directory of Open Access Journals (Sweden)

    Xiang Zhou

    2016-09-01

    Full Text Available Purpose: This research aims to identify product search tasks in online shopping and analyze the characteristics of consumer multi-tasking search sessions. Design/methodology/approach: The experimental dataset contains 8,949 queries of 582 users from 3,483 search sessions. A sequential comparison of the Jaccard similarity coefficient between two adjacent search queries and hierarchical clustering of queries is used to identify search tasks. Findings: (1 Users issued a similar number of queries (1.43 to 1.47 with similar lengths (7.3-7.6 characters per task in mono-tasking and multi-tasking sessions, and (2 Users spent more time on average in sessions with more tasks, but spent less time for each task when the number of tasks increased in a session. Research limitations: The task identification method that relies only on query terms does not completely reflect the complex nature of consumer shopping behavior. Practical implications: These results provide an exploratory understanding of the relationships among multiple shopping tasks, and can be useful for product recommendation and shopping task prediction. Originality/value: The originality of this research is its use of query clustering with online shopping task identification and analysis, and the analysis of product search session characteristics.

  4. Robust identification of noncoding RNA from transcriptomes requires phylogenetically-informed sampling.

    Directory of Open Access Journals (Sweden)

    Stinus Lindgreen

    2014-10-01

    Full Text Available Noncoding RNAs are integral to a wide range of biological processes, including translation, gene regulation, host-pathogen interactions and environmental sensing. While genomics is now a mature field, our capacity to identify noncoding RNA elements in bacterial and archaeal genomes is hampered by the difficulty of de novo identification. The emergence of new technologies for characterizing transcriptome outputs, notably RNA-seq, are improving noncoding RNA identification and expression quantification. However, a major challenge is to robustly distinguish functional outputs from transcriptional noise. To establish whether annotation of existing transcriptome data has effectively captured all functional outputs, we analysed over 400 publicly available RNA-seq datasets spanning 37 different Archaea and Bacteria. Using comparative tools, we identify close to a thousand highly-expressed candidate noncoding RNAs. However, our analyses reveal that capacity to identify noncoding RNA outputs is strongly dependent on phylogenetic sampling. Surprisingly, and in stark contrast to protein-coding genes, the phylogenetic window for effective use of comparative methods is perversely narrow: aggregating public datasets only produced one phylogenetic cluster where these tools could be used to robustly separate unannotated noncoding RNAs from a null hypothesis of transcriptional noise. Our results show that for the full potential of transcriptomics data to be realized, a change in experimental design is paramount: effective transcriptomics requires phylogeny-aware sampling.

  5. Processing of hyperspectral medical images applications in dermatology using Matlab

    CERN Document Server

    Koprowski, Robert

    2017-01-01

    This book presents new methods of analyzing and processing hyperspectral medical images, which can be used in diagnostics, for example for dermatological images. The algorithms proposed are fully automatic and the results obtained are fully reproducible. Their operation was tested on a set of several thousands of hyperspectral images and they were implemented in Matlab. The presented source code can be used without licensing restrictions. This is a valuable resource for computer scientists, bioengineers, doctoral students, and dermatologists interested in contemporary analysis methods.

  6. Integrated ground-based hyperspectral imaging and geochemical study of the Eagle Ford Group in West Texas

    Science.gov (United States)

    Sun, Lei; Khan, Shuhab; Godet, Alexis

    2018-01-01

    This study used ground-based hyperspectral imaging to map an outcrop of the Eagle Ford Group in west Texas. The Eagle Ford Group consists of alternating layers of mudstone - wackestone, grainstone - packstone facies and volcanic ash deposits with high total organic content deposited during the Cenomanian - Turonian time period. It is one of the few unconventional source rock and reservoirs that have surface representations. Ground-based hyperspectral imaging scanned an outcrop and hand samples at close ranges with very fine spatial resolution (centimeter to sub-millimeter). Spectral absorption modeling of clay minerals and calcite with the modified Gaussian model (MGM) allowed quantification of variations of mineral abundances. Petrographic analysis confirmed mineral identifications and shed light on sedimentary textures, and major element geochemistry supported the mineral quantification. Mineral quantification resulted in mapping of mudstone - wackestone, grainstone - packstone facies and bentonites (volcanic ash beds). The lack of spatial associations between the grainstones and bentonites on the outcrop calls into question the hypothesis that the primary productivity is controlled by iron availability from volcanic ash beds. Enrichment of molybdenum (Mo) and uranium (U) indicated "unrestricted marine" paleo-hydrogeology and anoxic to euxinic paleo-redox bottom water conditions. Hyperspectral remote sensing data also helped in creating a virtual outcrop model with detailed mineralogical compositions, and provided reservoir analog to extract compositional and geo-mechanical characteristics and variations. The utilization of these new techniques in geo-statistical analysis provides a workflow for employing remote sensing in resource exploration and exploitation.

  7. A Reliable Methodology for Determining Seed Viability by Using Hyperspectral Data from Two Sides of Wheat Seeds.

    Science.gov (United States)

    Zhang, Tingting; Wei, Wensong; Zhao, Bin; Wang, Ranran; Li, Mingliu; Yang, Liming; Wang, Jianhua; Sun, Qun

    2018-03-08

    This study investigated the possibility of using visible and near-infrared (VIS/NIR) hyperspectral imaging techniques to discriminate viable and non-viable wheat seeds. Both sides of individual seeds were subjected to hyperspectral imaging (400-1000 nm) to acquire reflectance spectral data. Four spectral datasets, including the ventral groove side, reverse side, mean (the mean of two sides' spectra of every seed), and mixture datasets (two sides' spectra of every seed), were used to construct the models. Classification models, partial least squares discriminant analysis (PLS-DA), and support vector machines (SVM), coupled with some pre-processing methods and successive projections algorithm (SPA), were built for the identification of viable and non-viable seeds. Our results showed that the standard normal variate (SNV)-SPA-PLS-DA model had high classification accuracy for whole seeds (>85.2%) and for viable seeds (>89.5%), and that the prediction set was based on a mixed spectral dataset by only using 16 wavebands. After screening with this model, the final germination of the seed lot could be higher than 89.5%. Here, we develop a reliable methodology for predicting the viability of wheat seeds, showing that the VIS/NIR hyperspectral imaging is an accurate technique for the classification of viable and non-viable wheat seeds in a non-destructive manner.

  8. 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.

  9. Hyperspectral Imaging Using Flexible Endoscopy for Laryngeal Cancer Detection

    Directory of Open Access Journals (Sweden)

    Bianca Regeling

    2016-08-01

    Full Text Available Hyperspectral imaging (HSI is increasingly gaining acceptance in the medical field. Up until now, HSI has been used in conjunction with rigid endoscopy to detect cancer in vivo. The logical next step is to pair HSI with flexible endoscopy, since it improves access to hard-to-reach areas. While the flexible endoscope’s fiber optic cables provide the advantage of flexibility, they also introduce an interfering honeycomb-like pattern onto images. Due to the substantial impact this pattern has on locating cancerous tissue, it must be removed before the HS data can be further processed. Thereby, the loss of information is to minimize avoiding the suppression of small-area variations of pixel values. We have developed a system that uses flexible endoscopy to record HS cubes of the larynx and designed a special filtering technique to remove the honeycomb-like pattern with minimal loss of information. We have confirmed its feasibility by comparing it to conventional filtering techniques using an objective metric and by applying unsupervised and supervised classifications to raw and pre-processed HS cubes. Compared to conventional techniques, our method successfully removes the honeycomb-like pattern and considerably improves classification performance, while preserving image details.

  10. 78 FR 68817 - Proposed Information Collection; Comment Request; Northeast Region Gear Identification

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    2013-11-15

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  12. Infrared hyperspectral imaging miniaturized for UAV applications

    Science.gov (United States)

    Hinnrichs, Michele; Hinnrichs, Bradford; McCutchen, Earl

    2017-02-01

    Pacific Advanced Technology (PAT) has developed an infrared hyperspectral camera, both MWIR and LWIR, small enough to serve as a payload on a miniature unmanned aerial vehicles. The optical system has been integrated into the cold-shield of the sensor enabling the small size and weight of the sensor. This new and innovative approach to infrared hyperspectral imaging spectrometer uses micro-optics and will be explained in this paper. The micro-optics are made up of an area array of diffractive optical elements where each element is tuned to image a different spectral region on a common focal plane array. The lenslet array is embedded in the cold-shield of the sensor and actuated with a miniature piezo-electric motor. This approach enables rapid infrared spectral imaging with multiple spectral images collected and processed simultaneously each frame of the camera. This paper will present our optical mechanical design approach which results in an infrared hyper-spectral imaging system that is small enough for a payload on a mini-UAV or commercial quadcopter. Also, an example of how this technology can easily be used to quantify a hydrocarbon gas leak's volume and mass flowrates. The diffractive optical elements used in the lenslet array are blazed gratings where each lenslet is tuned for a different spectral bandpass. The lenslets are configured in an area array placed a few millimeters above the focal plane and embedded in the cold-shield to reduce the background signal normally associated with the optics. We have developed various systems using a different number of lenslets in the area array. Depending on the size of the focal plane and the diameter of the lenslet array will determine the spatial resolution. A 2 x 2 lenslet array will image four different spectral images of the scene each frame and when coupled with a 512 x 512 focal plane array will give spatial resolution of 256 x 256 pixel each spectral image. Another system that we developed uses a 4 x 4

  13. Fisher information and asymptotic normality in system identification for quantum Markov chains

    International Nuclear Information System (INIS)

    Guta, Madalin

    2011-01-01

    This paper deals with the problem of estimating the coupling constant θ of a mixing quantum Markov chain. For a repeated measurement on the chain's output we show that the outcomes' time average has an asymptotically normal (Gaussian) distribution, and we give the explicit expressions of its mean and variance. In particular, we obtain a simple estimator of θ whose classical Fisher information can be optimized over different choices of measured observables. We then show that the quantum state of the output together with the system is itself asymptotically Gaussian and compute its quantum Fisher information, which sets an absolute bound to the estimation error. The classical and quantum Fisher information are compared in a simple example. In the vicinity of θ=0 we find that the quantum Fisher information has a quadratic rather than linear scaling in output size, and asymptotically the Fisher information is localized in the system, while the output is independent of the parameter.

  14. A systematic identification of species-specific protein succinylation sites using joint element features information

    Directory of Open Access Journals (Sweden)

    Hasan MM

    2017-08-01

    Full Text Available Md Mehedi Hasan,1 Mst Shamima Khatun,2 Md Nurul Haque Mollah,2 Cao Yong,3 Dianjing Guo1 1School of Life Sciences and the State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, New Territory, Hong Kong, People’s Republic of China; 2Laboratory of Bioinformatics, Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh; 3Department of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen Graduate School, Shenzhen, People’s Republic of China Abstract: Lysine succinylation, an important type of protein posttranslational modification, plays significant roles in many cellular processes. Accurate identification of succinylation sites can facilitate our understanding about the molecular mechanism and potential roles of lysine succinylation. However, even in well-studied systems, a majority of the succinylation sites remain undetected because the traditional experimental approaches to succinylation site identification are often costly, time-consuming, and laborious. In silico approach, on the other hand, is potentially an alternative strategy to predict succinylation substrates. In this paper, a novel computational predictor SuccinSite2.0 was developed for predicting generic and species-specific protein succinylation sites. This predictor takes the composition of profile-based amino acid and orthogonal binary features, which were used to train a random forest classifier. We demonstrated that the proposed SuccinSite2.0 predictor outperformed other currently existing implementations on a complementarily independent dataset. Furthermore, the important features that make visible contributions to species-specific and cross-species-specific prediction of protein succinylation site were analyzed. The proposed predictor is anticipated to be a useful computational resource for lysine succinylation site prediction. The integrated species-specific online tool of SuccinSite2.0 is publicly

  15. Hyperspectral Longwave Infrared Focal Plane Array and Camera Based on Quantum Well Infrared Photodetectors, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — We propose to develop a hyperspectral focal plane array and camera imaging in a large number of sharp hyperspectral bands in the thermal infrared. The camera is...

  16. Detection of environmental change using hyperspectral remote sensing at Olkiluoto repository site

    International Nuclear Information System (INIS)

    Tuominen, J.; Lipping, T.

    2011-03-01

    In this report methods related to hyperspectral monitoring of Olkiluoto repository site are described. A short introduction to environmental remote sensing is presented, followed by more detailed description of hyperspectral imaging and a review of applications of hyperspectral remote sensing presented in the literature. The trends of future hyperspectral imaging are discussed exploring the possibilities of long-wave infrared hyperspectral imaging. A detailed description of HYPE08 hyperspectral flight campaign at the Olkiluoto region in 2008 is presented. In addition, related pre-processing and atmospheric correction methods, necessary in monitoring use, and the quality control methods applied, are described. Various change detection methods presented in the literature are described, too. Finally, a system for hyperspectral monitoring is proposed. The system is based on continued hyperspectral airborne flight campaigns and precisely defined data processing procedure. (orig.)

  17. A lightweight hyperspectral mapping system and photogrammetric processing chain for unmanned aerial vehicles

    NARCIS (Netherlands)

    Suomalainen, J.M.; Anders, N.S.; Iqbal, S.; Roerink, G.J.; Franke, G.J.; Wenting, P.F.M.; Hünniger, D.; Bartholomeus, H.; Becker, R.; Kooistra, L.

    2014-01-01

    During the last years commercial hyperspectral imaging sensors have been miniaturized and their performance has been demonstrated on Unmanned Aerial Vehicles (UAV). However currently the commercial hyperspectral systems still require minimum payload capacity of approximately 3 kg, forcing usage of

  18. 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.

  19. Hyperspectral image analysis for rapid and accurate discrimination of bacterial infections: A benchmark study.

    Science.gov (United States)

    Arrigoni, Simone; Turra, Giovanni; Signoroni, Alberto

    2017-09-01

    With the rapid diffusion of Full Laboratory Automation systems, Clinical Microbiology is currently experiencing a new digital revolution. The ability to capture and process large amounts of visual data from microbiological specimen processing enables the definition of completely new objectives. These include the direct identification of pathogens growing on culturing plates, with expected improvements in rapid definition of the right treatment for patients affected by bacterial infections. In this framework, the synergies between light spectroscopy and image analysis, offered by hyperspectral imaging, are of prominent interest. This leads us to assess the feasibility of a reliable and rapid discrimination of pathogens through the classification of their spectral signatures extracted from hyperspectral image acquisitions of bacteria colonies growing on blood agar plates. We designed and implemented the whole data acquisition and processing pipeline and performed a comprehensive comparison among 40 combinations of different data preprocessing and classification techniques. High discrimination performance has been achieved also thanks to improved colony segmentation and spectral signature extraction. Experimental results reveal the high accuracy and suitability of the proposed approach, driving the selection of most suitable and scalable classification pipelines and stimulating clinical validations. Copyright © 2017 Elsevier Ltd. All rights reserved.

  20. Automatic detection and classification of EOL-concrete and resulting recovered products by hyperspectral imaging

    Science.gov (United States)

    Palmieri, Roberta; Bonifazi, Giuseppe; Serranti, Silvia

    2014-05-01

    The recovery of materials from Demolition Waste (DW) represents one of the main target of the recycling industry and the its characterization is important in order to set up efficient sorting and/or quality control systems. End-Of-Life (EOL) concrete materials identification is necessary to maximize DW conversion into useful secondary raw materials, so it is fundamental to develop strategies for the implementation of an automatic recognition system of the recovered products. In this paper, HyperSpectral Imaging (HSI) technique was applied in order to detect DW composition. Hyperspectral images were acquired by a laboratory device equipped with a HSI sensing device working in the near infrared range (1000-1700 nm): NIR Spectral Camera™, embedding an ImSpector™ N17E (SPECIM Ltd, Finland). Acquired spectral data were analyzed adopting the PLS_Toolbox (Version 7.5, Eigenvector Research, Inc.) under Matlab® environment (Version 7.11.1, The Mathworks, Inc.), applying different chemometric methods: Principal Component Analysis (PCA) for exploratory data approach and Partial Least Square- Discriminant Analysis (PLS-DA) to build classification models. Results showed that it is possible to recognize DW materials, distinguishing recycled aggregates from contaminants (e.g. bricks, gypsum, plastics, wood, foam, etc.). The developed procedure is cheap, fast and non-destructive: it could be used to make some steps of the recycling process more efficient and less expensive.

  1. Detection of explosives on the surface of banknotes by Raman hyperspectral imaging and independent component analysis.

    Science.gov (United States)

    Almeida, Mariana R; Correa, Deleon N; Zacca, Jorge J; Logrado, Lucio Paulo Lima; Poppi, Ronei J

    2015-02-20

    The aim of this study was to develop a methodology using Raman hyperspectral imaging and chemometric methods for identification of pre- and post-blast explosive residues on banknote surfaces. The explosives studied were of military, commercial and propellant uses. After the acquisition of the hyperspectral imaging, independent component analysis (ICA) was applied to extract the pure spectra and the distribution of the corresponding image constituents. The performance of the methodology was evaluated by the explained variance and the lack of fit of the models, by comparing the ICA recovered spectra with the reference spectra using correlation coefficients and by the presence of rotational ambiguity in the ICA solutions. The methodology was applied to forensic samples to solve an automated teller machine explosion case. Independent component analysis proved to be a suitable method of resolving curves, achieving equivalent performance with the multivariate curve resolution with alternating least squares (MCR-ALS) method. At low concentrations, MCR-ALS presents some limitations, as it did not provide the correct solution. The detection limit of the methodology presented in this study was 50 μg cm(-2). Copyright © 2014 Elsevier B.V. All rights reserved.

  2. 76 FR 16609 - Proposed Information Collection; Comment Request; Identification of Human Cell Lines Project

    Science.gov (United States)

    2011-03-24

    ... differentiate among cell lines, as described in Designation: ASN-0002 Authentication of Human Cell Lines... NIST (contact information above). III. Data OMB Control Number: None. Form Number: None. Type of Review...

  3. 75 FR 9158 - Proposed Information Collection; Comment Request; Identification of Northeast Regional Ocean...

    Science.gov (United States)

    2010-03-01

    ... DEPARTMENT OF COMMERCE National Oceanic and Atmospheric Administration Proposed Information... Social Network Analysis AGENCY: National Oceanic and Atmospheric Administration (NOAA). ACTION: Notice. SUMMARY: The Department of Commerce, as part of its continuing effort to reduce paperwork and respondent...

  4. Band Subset Selection for Hyperspectral Image Classification

    Directory of Open Access Journals (Sweden)

    Chunyan Yu

    2018-01-01

    Full Text Available This paper develops a new approach to band subset selection (BSS for hyperspectral image classification (HSIC which selects multiple bands simultaneously as a band subset, referred to as simultaneous multiple band selection (SMMBS, rather than one band at a time sequentially, referred to as sequential multiple band selection (SQMBS, as most traditional band selection methods do. In doing so, a criterion is particularly developed for BSS that can be used for HSIC. It is a linearly constrained minimum variance (LCMV derived from adaptive beamforming in array signal processing which can be used to model misclassification errors as the minimum variance. To avoid an exhaustive search for all possible band subsets, two numerical algorithms, referred to as sequential (SQ and successive (SC algorithms are also developed for LCMV-based SMMBS, called SQ LCMV-BSS and SC LCMV-BSS. Experimental results demonstrate that LCMV-based BSS has advantages over SQMBS.

  5. Recent applications of hyperspectral imaging in microbiology.

    Science.gov (United States)

    Gowen, Aoife A; Feng, Yaoze; Gaston, Edurne; Valdramidis, Vasilis

    2015-05-01

    Hyperspectral chemical imaging (HSI) is a broad term encompassing spatially resolved spectral data obtained through a variety of modalities (e.g. Raman scattering, Fourier transform infrared microscopy, fluorescence and near-infrared chemical imaging). It goes beyond the capabilities of conventional imaging and spectroscopy by obtaining spatially resolved spectra from objects at spatial resolutions varying from the level of single cells up to macroscopic objects (e.g. foods). In tandem with recent developments in instrumentation and sampling protocols, applications of HSI in microbiology have increased rapidly. This article gives a brief overview of the fundamentals of HSI and a comprehensive review of applications of HSI in microbiology over the past 10 years. Technical challenges and future perspectives for these techniques are also discussed. Copyright © 2015 Elsevier B.V. All rights reserved.

  6. Hyperspectral remote sensing for light pollution monitoring

    Directory of Open Access Journals (Sweden)

    P. Marcoionni

    2006-06-01

    Full Text Available industries. In this paper we introduce the results from a remote sensing campaign performed in September 2001 at night time. For the first time nocturnal light pollution was measured at high spatial and spectral resolution using two airborne hyperspectral sensors, namely the Multispectral Infrared and Visible Imaging Spectrometer (MIVIS and the Visible InfraRed Scanner (VIRS-200. These imagers, generally employed for day-time Earth remote sensing, were flown over the Tuscany coast (Italy on board of a Casa 212/200 airplane from an altitude of 1.5-2.0 km. We describe the experimental activities which preceded the remote sensing campaign, the optimization of sensor configuration, and the images as far acquired. The obtained results point out the novelty of the performed measurements and highlight the need to employ advanced remote sensing techniques as a spectroscopic tool for light pollution monitoring.

  7. Metric Learning for Hyperspectral Image Segmentation

    Science.gov (United States)

    Bue, Brian D.; Thompson, David R.; Gilmore, Martha S.; Castano, Rebecca

    2011-01-01

    We present a metric learning approach to improve the performance of unsupervised hyperspectral image segmentation. Unsupervised spatial segmentation can assist both user visualization and automatic recognition of surface features. Analysts can use spatially-continuous segments to decrease noise levels and/or localize feature boundaries. However, existing segmentation methods use tasks-agnostic measures of similarity. Here we learn task-specific similarity measures from training data, improving segment fidelity to classes of interest. Multiclass Linear Discriminate Analysis produces a linear transform that optimally separates a labeled set of training classes. The defines a distance metric that generalized to a new scenes, enabling graph-based segmentation that emphasizes key spectral features. We describe tests based on data from the Compact Reconnaissance Imaging Spectrometer (CRISM) in which learned metrics improve segment homogeneity with respect to mineralogical classes.

  8. A Reliable Measure of Information Security Awareness and the Identification of Bias in Responses

    Directory of Open Access Journals (Sweden)

    Agata McCormac

    2017-11-01

    Full Text Available The Human Aspects of Information Security Questionnaire (HAIS-Q is designed to measure Information Security Awareness. More specifically, the tool measures an individual’s knowledge, attitude, and self-reported behaviour relating to information security in the workplace. This paper reports on the reliability of the HAIS-Q, including test-retest reliability and internal consistency. The paper also assesses the reliability of three preliminary over-claiming items, designed specifically to complement the HAIS-Q, and identify those individuals who provide socially desirable responses. A total of 197 working Australians completed two iterations of the HAIS-Q and the over-claiming items, approximately 4 weeks apart. Results of the analysis showed that the HAIS-Q was externally reliable and internally consistent. Therefore, the HAIS-Q can be used to reliably measure information security awareness. Reliability testing on the preliminary over-claiming items was not as robust and further development is required and recommended. The implications of these findings mean that organisations can confidently use the HAIS-Q to not only measure the current state of employee information security awareness within their organisation, but they can also measure the effectiveness and impacts of training interventions, information security awareness programs and campaigns. The influence of cultural changes and the effect of security incidents can also be assessed.

  9. [Advances in the research on hyperspectral remote sensing in biodiversity and conservation].

    Science.gov (United States)

    He, Cheng; Feng, Zhong-Ke; Yuan, Jin-Jun; Wang, Jia; Gong, Yin-Xi; Dong, Zhi-Hai

    2012-06-01

    With the species reduction and the habitat destruction becoming serious increasingly, the biodiversity conservation has become one of the hottest topics. Remote sensing, the science of non-contact collection information, has the function of corresponding estimates of biodiversity, building model between species diversity relationship and mapping the index of biodiversity, which has been used widely in the field of biodiversity conservation. The present paper discussed the application of hyperspectral technology to the biodiversity conservation from two aspects, remote sensors and remote sensing techniques, and after, enumerated successful applications for emphasis. All these had a certain reference value in the development of biodiversity conservation.

  10. Hyperspectral remote sensing application for monitoring and preservation of plant ecosystems

    Science.gov (United States)

    Krezhova, Dora; Maneva, Svetla; Zdravev, Tomas; Petrov, Nikolay; Stoev, Antoniy

    Remote sensing technologies have advanced significantly at last decade and have improved the capability to gather information about Earth’s resources and environment. They have many applications in Earth observation, such as mapping and updating land-use and cover, weather forecasting, biodiversity determination, etc. Hyperspectral remote sensing offers unique opportunities in the environmental monitoring and sustainable use of natural resources. Remote sensing sensors on space-based platforms, aircrafts, or on ground, are capable of providing detailed spectral, spatial and temporal information on terrestrial ecosystems. Ground-based sensors are used to record detailed information about the land surface and to create a data base for better characterizing the objects which are being imaged by the other sensors. In this paper some applications of two hyperspectral remote sensing techniques, leaf reflectance and chlorophyll fluorescence, for monitoring and assessment of the effects of adverse environmental conditions on plant ecosystems are presented. The effect of stress factors such as enhanced UV-radiation, acid rain, salinity, viral infections applied to some young plants (potato, pea, tobacco) and trees (plums, apples, paulownia) as well as of some growth regulators were investigated. Hyperspectral reflectance and fluorescence data were collected by means of a portable fiber-optics spectrometer in the visible and near infrared spectral ranges (450-850 nm and 600-900 nm), respectively. The differences between the reflectance data of healthy (control) and injured (stressed) plants were assessed by means of statistical (Student’s t-criterion), first derivative, and cluster analysis and calculation of some vegetation indices in four most informative for the investigated species regions: green (520-580 nm), red (640-680 nm), red edge (690-720 nm) and near infrared (720-780 nm). Fluorescence spectra were analyzed at five characteristic wavelengths located at the

  11. Hydrocarbon Reservoir Identification in Volcanic Zone by using Magnetotelluric and Geochemistry Information

    Science.gov (United States)

    Firda, S. I.; Permadi, A. N.; Supriyanto; Suwardi, B. N.

    2018-03-01

    The resistivity of Magnetotelluric (MT) data show the resistivity mapping in the volcanic reservoir zone and the geochemistry information for confirm the reservoir and source rock formation. In this research, we used 132 data points divided with two line at exploration area. We used several steps to make the resistivity mapping. There are time series correction, crosspower correction, then inversion of Magnetotelluric (MT) data. Line-2 and line-3 show anomaly geological condition with Gabon fault. The geology structure from the resistivity mapping show the fault and the geological formation with the geological rock data mapping distribution. The geochemistry information show the maturity of source rock formation. According to core sample analysis information, we get the visual porosity for reservoir rock formation in several geological structure. Based on that, we make the geological modelling where the potential reservoir and the source rock around our interest area.

  12. Demographic and health surveillance of mobile pastoralists in Chad: integration of biometric fingerprint identification into a geographical information system.

    Science.gov (United States)

    Weibel, Daniel; Schelling, Esther; Bonfoh, Bassirou; Utzinger, Jürg; Hattendorf, Jan; Abdoulaye, Mahamat; Madjiade, Toguina; Zinsstag, Jakob

    2008-11-01

    There is a pressing need for baseline demographic and health-related data to plan, implement and evaluate health interventions in developing countries, and to monitor progress towards international development goals. However, mobile pastoralists, i.e. people who depend on a livestock production system and follow their herds as they move, remain marginalized from rural development plans and interventions. The fact that mobile people are hard to reach and stay in contact with is a plausible reason why they are underrepresented in national censuses and/or alternative sequential sample survey systems. We present a proof-of-concept of monitoring highly mobile, pastoral people by recording demographic and health-related data from 933 women and 2020 children and establishing a biometric identification system (BIS) based on the registration and identification of digital fingerprints. Although only 22 women, representing 2.4% of the total registered women, were encountered twice in the four survey rounds, the approach implemented is shown to be feasible. The BIS described here is linked to a geographical information system to facilitate the creation of the first health and demographic surveillance system in a mobile, pastoralist setting. Our ultimate goal is to implement and monitor interventions with the "one health" concept, thus integrating and improving human, animal and ecosystem health.

  13. Demographic and health surveillance of mobile pastoralists in Chad: integration of biometric fingerprint identification into a geographical information system

    Directory of Open Access Journals (Sweden)

    Daniel Weibel

    2008-11-01

    Full Text Available There is a pressing need for baseline demographic and health-related data to plan, implement and evaluate health interventions in developing countries, and to monitor progress towards international development goals. However, mobile pastoralists, i.e. people who depend on a livestock production system and follow their herds as they move, remain marginalized from rural development plans and interventions. The fact that mobile people are hard to reach and stay in contact with is a plausible reason why they are underrepresented in national censuses and/or alternative sequential sample survey systems. We present a proof-of-concept of monitoring highly mobile, pastoral people by recording demographic and health-related data from 933 women and 2020 children and establishing a biometric identification system (BIS based on the registration and identification of digital fingerprints. Although only 22 women, representing 2.4% of the total registered women, were encountered twice in the four survey rounds, the approach implemented is shown to be feasible. The BIS described here is linked to a geographical information system to facilitate the creation of the first health and demographic surveillance system in a mobile, pastoralist setting. Our ultimate goal is to implement and monitor interventions with the “one health” concept, thus integrating and improving human, animal and ecosystem health.

  14. Informed consent in contrast-enhanced CT. Understanding of risks and identification of possible prognostic factors

    International Nuclear Information System (INIS)

    Roehrl, S.; Dendl, L.M.; Scharf, G.; Stroszczynski, C.; Schreyer, A.G.; Zeman, F.

    2015-01-01

    Aim of our study was to assess understanding of risks associated with intravascular application of contrast media in patients undergoing CT examination. We wanted to evaluate epidemiologic and socio-economic prognostic factors for a higher understanding of risks. Additionally, we evaluated a possible correlation between an extensive, outcome-oriented oral informed consent and better understanding of risks. 120 patients distributed in 2 study arms participated in this prospective study. In study arm I, the treating physician was not informed that his patients participated in a study whereas the physician in study arm II knew about the survey. After the informed consent we performed a standardized, semi-structured interview to enquire the 3 most frequent risks of intravascular application of contrast agents (anaphylactoid reactions, nephropathy and thyrotoxic crisis) and epidemiologic data. The understanding of the risks was evaluated using a 6 point scale. Patients scored 3.73 points in study arm I and 4.93 points in arm II on average. The statistical difference between both study arms was highly significant (p < 0.001). In a combined logistic regression analysis, only ''higher education'' (p = 0.001) and participation in study arm II (p =0.001) showed a significant connection to a better understanding of risks. Patients profit from an outcome-oriented and individualized informed consent. Due to the significant correlation between educational level and understanding of risks, informed consent should be adjusted to the educational status of the individual patient, e.g. by using didactic aids or individualized information sheets.

  15. Implementation of webcam-based hyperspectral imaging system

    Science.gov (United States)

    Balooch, Ali; Nazeri, Majid; Abbasi, Hamed

    2018-02-01

    In the present work, a hyperspectral imaging system (imaging spectrometer) using a commercial webcam has been designed and developed. This system was able to capture two-dimensional spectra (in emission, transmission and reflection modes) directly from the scene in the desired wavelengths. Imaging of the object is done directly by linear sweep (pushbroom method). To do so, the spectrometer is equipped with a suitable collecting lens and a linear travel stage. A 1920 x 1080 pixel CMOS webcam was used as a detector. The spectrometer has been calibrated by the reference spectral lines of standard lamps. The spectral resolution of this system was about 2nm and its spatial resolution was about 1 mm for a 10 cm long object. The hardware solution is based on data acquisition working on the USB platform and controlled by a LabVIEW program. In this system, the initial output was a three-dimensional matrix in which two dimensions of the matrix were related to the spatial information of the object and the third dimension was the spectrum of any point of the object. Finally, the images in different wavelengths were created by reforming the data of the matrix. The free spectral range (FSR) of the system was 400 to 1100 nm. The system was successfully tested for some applications, such as plasma diagnosis as well as applications in food and agriculture sciences.

  16. Distributed Source Coding Techniques for Lossless Compression of Hyperspectral Images

    Directory of Open Access Journals (Sweden)

    Barni Mauro

    2007-01-01

    Full Text Available This paper deals with the application of distributed source coding (DSC theory to remote sensing image compression. Although DSC exhibits a significant potential in many application fields, up till now the results obtained on real signals fall short of the theoretical bounds, and often impose additional system-level constraints. The objective of this paper is to assess the potential of DSC for lossless image compression carried out onboard a remote platform. We first provide a brief overview of DSC of correlated information sources. We then focus on onboard lossless image compression, and apply DSC techniques in order to reduce the complexity of the onboard encoder, at the expense of the decoder's, by exploiting the correlation of different bands of a hyperspectral dataset. Specifically, we propose two different compression schemes, one based on powerful binary error-correcting codes employed as source codes, and one based on simpler multilevel coset codes. The performance of both schemes is evaluated on a few AVIRIS scenes, and is compared with other state-of-the-art 2D and 3D coders. Both schemes turn out to achieve competitive compression performance, and one of them also has reduced complexity. Based on these results, we highlight the main issues that are still to be solved to further improve the performance of DSC-based remote sensing systems.

  17. Hyperspectral imaging of colonic polyps in vivo (Conference Presentation)

    Science.gov (United States)

    Clancy, Neil T.; Elson, Daniel S.; Teare, Julian

    2017-02-01

    Standard endoscopic tools restrict clinicians to making subjective visual assessments of lesions detected in the bowel, with classification results depending strongly on experience level and training. Histological examination of resected tissue remains the diagnostic gold standard, meaning that all detected lesions are routinely removed. This subjects the patient to risk of polypectomy-related injury, and places significant workload and economic burdens on the hospital. An objective endoscopic classification method would allow hyperplastic polyps, with no malignant potential, to be left in situ, or low grade adenomas to be resected and discarded without histology. A miniature multimodal flexible endoscope is proposed to obtain hyperspectral reflectance and dual excitation autofluorescence information from polyps in vivo. This is placed inside the working channel of a conventional colonoscope, with the external scanning and detection optics on a bedside trolley. A blue and violet laser diode pair excite endogenous fluorophores in the respiration chain, while the colonoscope's xenon light source provides broadband white light for diffuse reflectance measurements. A push-broom HSI scanner collects the hypercube. System characterisation experiments are presented, defining resolution limits as well as acquisition settings for optimal spectral, spatial and temporal performance. The first in vivo results in human subjects are presented, demonstrating the clinical utility of the device. The optical properties (reflectance and autofluorescence) of imaged polyps are quantified and compared to the histologically-confirmed tissue type as well as the clinician's visual assessment. Further clinical studies will allow construction of a full robust training dataset for development of classification schemes.

  18. Subspace Learning via Local Probability Distribution for Hyperspectral Image Classification

    Directory of Open Access Journals (Sweden)

    Huiwu Luo

    2015-01-01

    Full Text Available The computational procedure of hyperspectral image (HSI is extremely complex, not only due to the high dimensional information, but also due to the highly correlated data structure. The need of effective processing and analyzing of HSI has met many difficulties. It has been evidenced that dimensionality reduction has been found to be a powerful tool for high dimensional data analysis. Local Fisher’s liner discriminant analysis (LFDA is an effective method to treat HSI processing. In this paper, a novel approach, called PD-LFDA, is proposed to overcome the weakness of LFDA. PD-LFDA emphasizes the probability distribution (PD in LFDA, where the maximum distance is replaced with local variance for the construction of weight matrix and the class prior probability is applied to compute the affinity matrix. The proposed approach increases the discriminant ability of the transformed features in low dimensional space. Experimental results on Indian Pines 1992 data indicate that the proposed approach significantly outperforms the traditional alternatives.

  19. [Analysis of influencing factors of snow hyperspectral polarized reflections].

    Science.gov (United States)

    Sun, Zhong-Qiu; Zhao, Yun-Sheng; Yan, Guo-Qian; Ning, Yan-Ling; Zhong, Gui-Xin

    2010-02-01

    Due to the need of snow monitoring and the impact of the global change on the snow, on the basis of the traditional research on snow, starting from the perspective of multi-angle polarized reflectance, we analyzed the influencing factors of snow from the incidence zenith angles, the detection zenith angles, the detection azimuth angles, polarized angles, the density of snow, the degree of pollution, and the background of the undersurface. It was found that these factors affected the spectral reflectance values of the snow, and the effect of some factors on the polarization hyperspectral reflectance observation is more evident than in the vertical observation. Among these influencing factors, the pollution of snow leads to an obvious change in the snow reflectance spectrum curve, while other factors have little effect on the shape of the snow reflectance spectrum curve and mainly impact the reflection ratio of the snow. Snow reflectance polarization information has not only important theoretical significance, but also wide application prospect, and provides new ideas and methods for the quantitative research on snow using the remote sensing technology.

  20. Multiangular hyperspectral investigation of polarized light in case 2 waters

    Science.gov (United States)

    Tonizzo, A.; Zhou, J.; Gilerson, A.; Chowdhary, J.; Gross, B.; Moshary, F.; Ahmed, S.

    2009-09-01

    The focus of this work is on the dependence of in situ hyperspectral and multiangular polarized data on the size distribution and refractive index of the suspended particles. Underwater polarization measurements were obtained using a polarimeter developed at the Optical Remote Sensing Laboratory of the City College of New York, NY. The degree of polarization (DOP) of the underwater light field in coastal environments was measured and the water-leaving polarized radiance was derived. In-water optical properties were also measured with an ac-9 (WET Labs). Absorption and attenuation spectra are then used to derive information on the dissolved and suspend components in the water medium which are used in a vector radiative transfer code which provides the upwelling radiance. The model was run for various values of the refractive index of mineral particles until the modeled DOP matched the measured one. The relationship between the intensity of the maximum of the DOP and both the refractive index of the mineral particles and the shapes of their size distributions is analyzed in detail.

  1. Identification of informative features for predicting proinflammatory potentials of engine exhausts.

    Science.gov (United States)

    Wang, Chia-Chi; Lin, Ying-Chi; Lin, Yuan-Chung; Jhang, Syu-Ruei; Tung, Chun-Wei

    2017-08-18

    The immunotoxicity of engine exhausts is of high concern to human health due to the increasing prevalence of immune-related diseases. However, the evaluation of immunotoxicity of engine exhausts is currently based on expensive and time-consuming experiments. It is desirable to develop efficient methods for immunotoxicity assessment. To accelerate the development of safe alternative fuels, this study proposed a computational method for identifying informative features for predicting proinflammatory potentials of engine exhausts. A principal component regression (PCR) algorithm was applied to develop prediction models. The informative features were identified by a sequential backward feature elimination (SBFE) algorithm. A total of 19 informative chemical and biological features were successfully identified by SBFE algorithm. The informative features were utilized to develop a computational method named FS-CBM for predicting proinflammatory potentials of engine exhausts. FS-CBM model achieved a high performance with correlation coefficient values of 0.997 and 0.943 obtained from training and independent test sets, respectively. The FS-CBM model was developed for predicting proinflammatory potentials of engine exhausts with a large improvement on prediction performance compared with our previous CBM model. The proposed method could be further applied to construct models for bioactivities of mixtures.

  2. Economic Regime identification and prediction in TAC SCM using sales and procurement information

    NARCIS (Netherlands)

    Hogenboom, F.P.; Ketter, W.; Dalen, van Jan; Kaymak, U.; Collins, J.; Gupta, Alok

    2009-01-01

    Our research is focused on the effects of the additionof procurement information (offer prices) to a sales-based economic regime model. This model is used for strategic, tactical, and operational decision making in dynamic supply chains. We evaluate the performance of the regime model through

  3. A Manual of Cherokee Herbal Remedies: History, Information, Identification, Medicinal Healing.

    Science.gov (United States)

    Schafer, Patricia D.

    This thesis reports on the research of 25 plants, used as herbal remedies since the 1800s by the author's Native American ancestors (the Day family) and the Cherokee tribe. The plants were identified in four state parks in southwestern Indiana. Information sources included the research literature, articles on Cherokee herbal remedies, and…

  4. Genome-wide identification of breed-informative single-nucleotide ...

    African Journals Online (AJOL)

    This is because the SNPs on BovineSNP50 and GGP-80K assays were ascertained as being common in European taurine breeds. Lower MAF and SNP informativeness observed in this study limits the application of these assays in breed assignment, and could have other implications for genome-wide studies in South ...

  5. 78 FR 23332 - Supplemental Identification Information for Two (2) Individuals Designated Pursuant to Executive...

    Science.gov (United States)

    2013-04-18

    ... two (2) individuals in this notice, pursuant is effective on April 11, 2013. FOR FURTHER INFORMATION... determined by the Secretary of State, in consultation with the Secretary of the Treasury, the Secretary of..., in consultation with the Departments of State, Homeland [[Page 23333

  6. Burden of informal caregiving for stroke patients: Identification of caregivers at risk of adverse health effects

    NARCIS (Netherlands)

    Exel, N.J.A. van; Koopmanschap, M.A.; Berg, B. van den; Brouwer, W.B.F.; Bos, G.A.M. van den

    2005-01-01

    Background: We assessed the objective and subjective burden of caregiving for stroke patients and investigated which characteristics of the patient, the informal caregiver and the objective burden contribute most to subjective burden and to the condition of feeling substantially burdened. Methods:

  7. COBACORE Community Based Comprehensive Recovery : D2.1: Data identification - WP2: Information framework

    NARCIS (Netherlands)

    Anoniem

    2013-01-01

    The collation, effective management and timely transfer of robust and credible data impact all activities and transcend all phases of the disaster cycle. All actors involved in recovery and reconstruction are dependent upon specific datasets in order to inform decision making, prioritise and

  8. Identification of Mendelian inconsistencies between SNP and pedigree Information of Sibs

    NARCIS (Netherlands)

    Calus, M.P.L.; Mulder, H.A.; Bastiaansen, J.W.M.

    2011-01-01

    Background Using SNP genotypes to apply genomic selection in breeding programs is becoming common practice. Tools to edit and check the quality of genotype data are required. Checking for Mendelian inconsistencies makes it possible to identify animals for which pedigree information and genotype

  9. Investigation of grapevine photosynthesis using hyperspectral techniques and development of hyperspectral band ratio indices sensitive to photosynthesis.

    Science.gov (United States)

    Ozelkan, Emre; Karaman, Muhittin; Candar, Serkan; Coskun, Zafer; Ormeci, Cankut

    2015-01-01

    The photosynthetic rate of 9 different grapevines were analyzed with simultaneous photosynthesis and spectroradiometric measurements on 08.08.2012 (veraison) and 06.09.2012 (harvest). The wavelengths and spectral regions, which most properly express photosynthetic rate, were determined using correlation and regression analysis. In addition, hyperspectral band ratio (BR) indices sensitive to photosynthesis were developed using optimum band ratio (OBRA) method. The relation of BR results with photosynthesis values are presented with the correlation matrix maps created in this study. The examinations were performed for both specific dates (i.e., veraison and harvest) and also in aggregate (i.e., correlation between total spectra and photosynthesis data). For specific dates wavelength based analysis, the photosynthesis were best determined with -0.929 correlation coefficient (r) 609 nm of yellow region at veraison stage, and -0.870 at 641 nm of red region at harvest stage. For wavelength based aggregate analysis, 640 nm of red region was found to be correlated with 0.921 and -0.867 r values respectively and red edge (RE) (695 nm) was found to be correlated with -0.922 and -0.860 r values, respectively. When BR indices results were analyzed with photosynthetic values for specific dates, -0.987 r with R8../R, at veraison stage and -0.911 r with R696/R944 at harvest stage were found most correlated. For aggregate analysis of BR, common BR presenting great correlation with photosynthesis for both measurements was found to be R632/R971 with -0.974, -0.881 r values, respectively and other R610/R760 with -0.976, -0.879 r values. The final results of this study indicate that the proportion of RE region to a region with direct or indirect correlation with photosynthetic provides information about rate of photosynthesis. With the indices created in this study, the photosynthesis rate of vineyards can be determined using in-situ hyperspectral remote sensing. The findings of this

  10. Semantic Technology Application for Collective Knowledge and Information Management: Prospective Consumer Needs Identification

    Directory of Open Access Journals (Sweden)

    Ilma Pranciulytė-Bagdziunienė

    2013-01-01

    Full Text Available Increasing the global flow of information forms qualitatively new complex information processing and filing requirements. The flow of information, data and knowledge manages the various activities of the original search for technological solutions. Very abundant and rapidly growing technology solutions groups are based on semantic technologies. Therefore, this article aims to provide user access needs for producing perspective survey methodology and the empirical study is based on the prospective development of innovative product lines. This article is formed based on the recommendations of the semantics of the applicability of technology development to business end users, public administration, organization of information flows the value of the generation of knowledge—based on environment and development issues. At a practical level, based on empirical evidence substantiates the semantics it is based on technology solutions for organizations in the integration of business processes, which can become the modern aspect of the success factors of the value of domestic and global market and facilitate the diffusion of innovation. The field of qualitative research has revealed the final consumer habits and problems of information search, organization, grouping aspects. Secondly, the study determined the idea of the necessity of technology in business processes, innovation generation and diffusion of knowledge issues aspects. Third, the authors submit proposals based on the semantics of the applicability of technology development opportunities in the business. Finally—users, public administrations and their mutual interaction activities. ST applicability of these segments may occur based on ST integration of IT systems in organizations, the general structure of existing products or used as a service by buying them from outside suppliers. It is important to emphasize that the ST innovative methods to ensure successful use of advanced, modern

  11. 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.

  12. [Exploring novel hyperspectral band and key index for leaf nitrogen accumulation in wheat].

    Science.gov (United States)

    Yao, Xia; Zhu, Yan; Feng, Wei; Tian, Yong-Chao; Cao, Wei-Xing

    2009-08-01

    The objectives of the present study were to explore new sensitive spectral bands and ratio spectral indices based on precise analysis of ground-based hyperspectral information, and then develop regression model for estimating leaf N accumulation per unit soil area (LNA) in winter wheat (Triticum aestivum L.). Three field experiments were conducted with different N rates and cultivar types in three consecutive growing seasons, and time-course measurements were taken on canopy hyperspectral reflectance and LNA tinder the various treatments. By adopting the method of reduced precise sampling, the detailed ratio spectral indices (RSI) within the range of 350-2 500 nm were constructed, and the quantitative relationships between LNA (gN m(-2)) and RSI (i, j) were analyzed. It was found that several key spectral bands and spectral indices were suitable for estimating LNA in wheat, and the spectral parameter RSI (990, 720) was the most reliable indicator for LNA in wheat. The regression model based on the best RSI was formulated as y = 5.095x - 6.040, with R2 of 0.814. From testing of the derived equations with independent experiment data, the model on RSI (990, 720) had R2 of 0.847 and RRMSE of 24.7%. Thus, it is concluded that the present hyperspectral parameter of RSI (990, 720) and derived regression model can be reliably used for estimating LNA in winter wheat. These results provide the feasible key bands and technical basis for developing the portable instrument of monitoring wheat nitrogen status and for extracting useful spectral information from remote sensing images.

  13. Networks of high mutual information define the structural proximity of catalytic sites: implications for catalytic residue identification.

    Directory of Open Access Journals (Sweden)

    Cristina Marino Buslje

    Full Text Available Identification of catalytic residues (CR is essential for the characterization of enzyme function. CR are, in general, conserved and located in the functional site of a protein in order to attain their function. However, many non-catalytic residues are highly conserved and not all CR are conserved throughout a given protein family making identification of CR a challenging task. Here, we put forward the hypothesis that CR carry a particular signature defined by networks of close proximity residues with high mutual information (MI, and that this signature can be applied to distinguish functional from other non-functional conserved residues. Using a data set of 434 Pfam families included in the catalytic site atlas (CSA database, we tested this hypothesis and demonstrated that MI can complement amino acid conservation scores to detect CR. The Kullback-Leibler (KL conservation measurement was shown to significantly outperform both the Shannon entropy and maximal frequency measurements. Residues in the proximity of catalytic sites were shown to be rich in shared MI. A structural proximity MI average score (termed pMI was demonstrated to be a strong predictor for CR, thus confirming the proposed hypothesis. A structural proximity conservation average score (termed pC was also calculated and demonstrated to carry distinct information from pMI. A catalytic likeliness score (Cls, combining the KL, pC and pMI measures, was shown to lead to significantly improved prediction accuracy. At a specificity of 0.90, the Cls method was found to have a sensitivity of 0.816. In summary, we demonstrate that networks of residues with high MI provide a distinct signature on CR and propose that such a signature should be present in other classes of functional residues where the requirement to maintain a particular function places limitations on the diversification of the structural environment along the course of evolution.

  14. Networks of high mutual information define the structural proximity of catalytic sites: implications for catalytic residue identification.

    Science.gov (United States)

    Marino Buslje, Cristina; Teppa, Elin; Di Doménico, Tomas; Delfino, José María; Nielsen, Morten

    2010-11-04

    Identification of catalytic residues (CR) is essential for the characterization of enzyme function. CR are, in general, conserved and located in the functional site of a protein in order to attain their function. However, many non-catalytic residues are highly conserved and not all CR are conserved throughout a given protein family making identification of CR a challenging task. Here, we put forward the hypothesis that CR carry a particular signature defined by networks of close proximity residues with high mutual information (MI), and that this signature can be applied to distinguish functional from other non-functional conserved residues. Using a data set of 434 Pfam families included in the catalytic site atlas (CSA) database, we tested this hypothesis and demonstrated that MI can complement amino acid conservation scores to detect CR. The Kullback-Leibler (KL) conservation measurement was shown to significantly outperform both the Shannon entropy and maximal frequency measurements. Residues in the proximity of catalytic sites were shown to be rich in shared MI. A structural proximity MI average score (termed pMI) was demonstrated to be a strong predictor for CR, thus confirming the proposed hypothesis. A structural proximity conservation average score (termed pC) was also calculated and demonstrated to carry distinct information from pMI. A catalytic likeliness score (Cls), combining the KL, pC and pMI measures, was shown to lead to significantly improved prediction accuracy. At a specificity of 0.90, the Cls method was found to have a sensitivity of 0.816. In summary, we demonstrate that networks of residues with high MI provide a distinct signature on CR and propose that such a signature should be present in other classes of functional residues where the requirement to maintain a particular function places limitations on the diversification of the structural environment along the course of evolution.

  15. Affect as Information in Persuasion: A Model of Affect Identification and Discounting

    Science.gov (United States)

    Albarracín, Dolores; Kumkale, G. Tarcan

    2016-01-01

    Three studies examined the implications of a model of affect as information in persuasion. According to this model, extraneous affect may have an influence when message recipients exert moderate amounts of thought, because they identify their affective reactions as potential criteria but fail to discount them as irrelevant. However, message recipients may not use affect as information when they deem affect irrelevant or when they do not identify their affective reactions at all. Consistent with this curvilinear prediction, recipients of a message that either favored or opposed comprehensive exams used affect as a basis for attitudes in situations that elicited moderate thought. Affect, however, had no influence on attitudes in conditions that elicited either large or small amounts of thought. PMID:12635909

  16. Information flow between interacting human brains: Identification, validation, and relationship to social expertise.

    Science.gov (United States)

    Bilek, Edda; Ruf, Matthias; Schäfer, Axel; Akdeniz, Ceren; Calhoun, Vince D; Schmahl, Christian; Demanuele, Charmaine; Tost, Heike; Kirsch, Peter; Meyer-Lindenberg, Andreas

    2015-04-21

    Social interactions are fundamental for human behavior, but the quantification of their neural underpinnings remains challenging. Here, we used hyperscanning functional MRI (fMRI) to study information flow between brains of human dyads during real-time social interaction in a joint attention paradigm. In a hardware setup enabling immersive audiovisual interaction of subjects in linked fMRI scanners, we characterize cross-brain connectivity components that are unique to interacting individuals, identifying information flow between the sender's and receiver's temporoparietal junction. We replicate these findings in an independent sample and validate our methods by demonstrating that cross-brain connectivity relates to a key real-world measure of social behavior. Together, our findings support a central role of human-specific cortical areas in the brain dynamics of dyadic interactions and provide an approach for the noninvasive examination of the neural basis of healthy and disturbed human social behavior with minimal a priori assumptions.

  17. Compendium of information on identification and testing of materials for plastic solar thermal collectors

    Energy Technology Data Exchange (ETDEWEB)

    McGinniss, V.D.; Sliemers, F.A.; Landstrom, D.K.; Talbert, S.G.

    1980-07-31

    This report is intended to organize and summarize prior and current literature concerning the weathering, aging, durability, degradation, and testing methodologies as applied to materials for plastic solar thermal collectors. Topics covered include (1) rate of aging of polymeric materials; (2) environmental factors affecting performance; (3) evaluation and prediction of service life; (4) measurement of physical and chemical properties; (5) discussion of evaluation techniques and specific instrumentation; (6) degradation reactions and mechanisms; (7) weathering of specific polymeric materials; and (8) exposure testing methodology. Major emphasis has been placed on defining the current state of the art in plastics degradation and on identifying information that can be utilized in applying appropriate and effective aging tests for use in projecting service life of plastic solar thermal collectors. This information will also be of value where polymeric components are utilized in the construction of conventional solar collectors or any application where plastic degradation and weathering are prime factors in material selection.

  18. Identification of Breast Cancer Using Integrated Information from MRI and Mammography.

    Directory of Open Access Journals (Sweden)

    Shih-Neng Yang

    Full Text Available Integration of information from corresponding regions between the breast MRI and an X-ray mammogram could benefit the detection of breast cancer in clinical diagnosis. We aimed to provide a framework of registration from breast MRI to mammography and to evaluate the diagnosis using the combined information.43 patients with 46 lesions underwent both MRI and mammography scans, and the interval between the two examinations was around one month. The distribution of malignant to benign lesions was 31/46 based on histological results. Maximum intensity projection and thin-plate spline methods were applied for image registration for MRI to mammography. The diagnosis using integrated information was evaluated using results of histology as the reference. The assessment of annotations and statistical analysis were performed by the two radiologists.For the cranio-caudal view, the mean post-registration error between MRI and mammography was 2.2±1.9 mm. For the medio-lateral oblique view, the proposed approach performed even better with a mean error of 3.0±2.4 mm. In the diagnosis using MRI assessment with information of mammography, the sensitivity was 91.9±2.3% (29/31, 28/31, specificity 70.0±4.7% (11/15, 10/15, accuracy 84.8±3.1% (40/46, 38/46, positive predictive value 86.4±2.1% (29/33, 28/33 and negative predictive value 80.8±5.4% (11/13, 10/13.MRI with the aid of mammography shows potential improvements of sensitivity, specificity, accuracy, PPV and NPV in clinical breast cancer diagnosis compared to the use of MRI alone.

  19. Determination of the Relationship between Information Capacity and Identification by Simulated Aerial Photography.

    Science.gov (United States)

    1978-06-27

    experiment by Frank Scott, Peter Hollanda , and Albert Harabedian 7 7Scott, F., Hollanda , P. A., and Harabedian, A., Phot. Sci. Eng., 14 1 pp 21-27...photographs of models of military tanks and trucks. The information capacity of these photos was varied by defocussing the taking camera and the simulated...photographic reconnaissance mission. This was done by photographing models of military targets, I processing and duplicating the resulting images, and

  20. Hydrothermal alteration and diagenesis of terrestrial lacustrine pillow basalts: Coordination of hyperspectral imaging with laboratory measurements

    Science.gov (United States)

    Greenberger, Rebecca N.; Mustard, John F.; Cloutis, Edward A.; Mann, Paul; Wilson, Janette H.; Flemming, Roberta L.; Robertson, Kevin M.; Salvatore, Mark R.; Edwards, Christopher S.

    2015-12-01

    identification of the alteration phases and help synthesize the aqueous history of pillow lavas of the Talcott Formation. These results are also relevant to Mars, where volcanically-resurfaced open basin lakes have been found, and this Hartford Basin outcrop may be a valuable analog for any potential volcano-lacustrine interactions. The results can also help to inform the utility and optimization of potentially complementary, synergistic, and uniquely-suited techniques for characterization of hydrothermally-altered terrains.

  1. Feasibility for detection of autofluorescent signatures in rat organs using a novel excitation-scanning hyperspectral imaging system

    Science.gov (United States)

    Favreau, Peter F.; Deal, Joshua A.; Weber, David S.; Rich, Thomas C.; Leavesley, Silas J.

    2016-04-01

    The natural fluorescence (autofluorescence) of tissues has been noted as a biomarker for cancer for several decades. Autofluorescence contrast between tumors and healthy tissues has been of significant interest in endoscopy, leading to development of autofluorescence endoscopes capable of visualizing 2-3 fluorescence emission wavelengths to achieve maximal contrast. However, tumor detection with autofluorescence endoscopes is hindered by low fluorescence signal and limited quantitative information, resulting in prolonged endoscopic procedures, prohibitive acquisition times, and reduced specificity of detection. Our lab has designed a novel excitation-scanning hyperspectral imaging system with high fluorescence signal detection, low acquisition time, and enhanced spectral discrimination. In this study, we surveyed a comprehensive set of excised tissues to assess the feasibility of detecting tissue-specific pathologies using excitation-scanning. Fresh, untreated tissue specimens were imaged from 360 to 550 nm on an inverted fluorescence microscope equipped with a set of thin-film tunable filters (Semrock, A Unit of IDEX). Images were subdivided into training and test sets. Automated endmember extraction (ENVI 5.1, Exelis) with PCA identified endmembers within training images of autofluorescence. A spectral library was created from 9 endmembers. The library was used for identification of endmembers in test images. Our results suggest (1) spectral differentiation of multiple tissue types is possible using excitation scanning; (2) shared spectra between tissue types; and (3) the ability to identify unique morphological features in disparate tissues from shared autofluorescent components. Future work will focus on isolating specific molecular signatures present in tissue spectra, and elucidating the contribution of these signatures in pathologies.

  2. Optimized hyperspectral band selection using hybrid genetic algorithm and gravitational search algorithm

    Science.gov (United States)

    Zhang, Aizhu; Sun, Genyun; Wang, Zhenjie

    2015-12-01

    The serious information redundancy in hyperspectral images (HIs) cannot contribute to the data analysis accuracy, instead it require expensive computational resources. Consequently, to identify the most useful and valuable information from the HIs, thereby improve the accuracy of data analysis, this paper proposed a novel hyperspectral band selection method using the hybrid genetic algorithm and gravitational search algorithm (GA-GSA). In the proposed method, the GA-GSA is mapped to the binary space at first. Then, the accuracy of the support vector machine (SVM) classifier and the number of selected spectral bands are utilized to measure the discriminative capability of the band subset. Finally, the band subset with the smallest number of spectral bands as well as covers the most useful and valuable information is obtained. To verify the effectiveness of the proposed method, studies conducted on an AVIRIS image against two recently proposed state-of-the-art GSA variants are presented. The experimental results revealed the superiority of the proposed method and indicated that the method can indeed considerably reduce data storage costs and efficiently identify the band subset with stable and high classification precision.

  3. A hyperspectral fluorescence system for 3D in vivo optical imaging

    International Nuclear Information System (INIS)

    Zavattini, Guido; Vecchi, Stefania; Mitchell, Gregory; Weisser, Ulli; Leahy, Richard M; Pichler, Bernd J; Smith, Desmond J; Cherry, Simon R

    2006-01-01

    In vivo optical instruments designed for small animal imaging generally measure the integrated light intensity across a broad band of wavelengths, or make measurements at a small number of selected wavelengths, and primarily use any spectral information to characterize and remove autofluorescence. We have developed a flexible hyperspectral imaging instrument to explore the use of spectral information to determine the 3D source location for in vivo fluorescence imaging applications. We hypothesize that the spectral distribution of the emitted fluorescence signal can be used to provide additional information to 3D reconstruction algorithms being developed for optical tomography. To test this hypothesis, we have designed and built an in vivo hyperspectral imaging system, which can acquire data from 400 to 1000 nm with 3 nm spectral resolution and which is flexible enough to allow the testing of a wide range of illumination and detection geometries. It also has the capability to generate a surface contour map of the animal for input into the reconstruction process. In this paper, we present the design of the system, demonstrate the depth dependence of the spectral signal in phantoms and show the ability to reconstruct 3D source locations using the spectral data in a simple phantom. We also characterize the basic performance of the imaging system

  4. CLASSIFICATION OF HYPERSPECTRAL DATA BASED ON GUIDED FILTERING AND RANDOM FOREST

    Directory of Open Access Journals (Sweden)

    H. Ma

    2017-09-01

    Full Text Available Hyperspectral images usually consist of more than one hundred spectral bands, which have potentials to provide rich spatial and spectral information. However, the application of hyperspectral data is still challengeable due to “the curse of dimensionality”. In this context, many techniques, which aim to make full use of both the spatial and spectral information, are investigated. In order to preserve the geometrical information, meanwhile, with less spectral bands, we propose a novel method, which combines principal components analysis (PCA, guided image filtering and the random forest classifier (RF. In detail, PCA is firstly employed to reduce the dimension of spectral bands. Secondly, the guided image filtering technique is introduced to smooth land object, meanwhile preserving the edge of objects. Finally, the features are fed into RF classifier. To illustrate the effectiveness of the method, we carry out experiments over the popular Indian Pines data set, which is collected by Airborne Visible/Infrared Imaging Spectrometer (AVIRIS sensor. By comparing the proposed method with the method of only using PCA or guided image filter, we find that effect of the proposed method is better.

  5. Software for hyperspectral, joint photographic experts group (.JPG), portable network graphics (.PNG) and tagged image file format (.TIFF) segmentation

    Science.gov (United States)

    Bruno, L. S.; Rodrigo, B. P.; Lucio, A. de C. Jorge

    2016-10-01

    This paper presents a system developed by an application of a neural network Multilayer Perceptron for drone acquired agricultural image segmentation. This application allows a supervised user training the classes that will posteriorly be interpreted by neural network. These classes will be generated manually with pre-selected attributes in the application. After the attribute selection a segmentation process is made to allow the relevant information extraction for different types of images, RGB or Hyperspectral. The application allows extracting the geographical coordinates from the image metadata, geo referencing all pixels on the image. In spite of excessive memory consume on hyperspectral images regions of interest, is possible to perform segmentation, using bands chosen by user that can be combined in different ways to obtain different results.

  6. A Vision-Based System for Object Identification and Information Retrieval in a Smart Home

    Science.gov (United States)

    Grech, Raphael; Monekosso, Dorothy; de Jager, Deon; Remagnino, Paolo

    This paper describes a hand held device developed to assist people to locate and retrieve information about objects in a home. The system developed is a standalone device to assist persons with memory impairments such as people suffering from Alzheimer's disease. A second application is object detection and localization for a mobile robot operating in an ambient assisted living environment. The device relies on computer vision techniques to locate a tagged object situated in the environment. The tag is a 2D color printed pattern with a detection range and a field of view such that the user may point from a distance of over 1 meter.

  7. Identification of calculation hierarchy and information flow for postclosure performance assessment

    International Nuclear Information System (INIS)

    Avci, H.I.; Cunnane, J.C.; Brandstetter, A.

    1990-01-01

    A management tool consisting of calculation hierarchy and information flow diagrams is being prepared to address the resolution of major postclosure performance issues for a geologic high-level radioactive waste repository in the U.S.A. The diagrams will indicate the types of calculations and data needed to assess the postclosure performance of the repository. Separate diagrams will be generated for different scenario classes and conceptual models. The methodology used in developing these diagrams and their contents are illustrated for a single scenario and conceptual model. 5 refs., 5 figs

  8. [Non-destructive detection research for hollow heart of potato based on semi-transmission hyperspectral imaging and SVM].

    Science.gov (United States)

    Huang, Tao; Li, Xiao-yu; Xu, Meng-ling; Jin, Rui; Ku, Jing; Xu, Sen-miao; Wu, Zhen-zhong

    2015-01-01

    The quality of potato is directly related to their edible value and industrial value. Hollow heart of potato, as a physiological disease occurred inside the tuber, is difficult to be detected. This paper put forward a non-destructive detection method by using semi-transmission hyperspectral imaging with support vector machine (SVM) to detect hollow heart of potato. Compared to reflection and transmission hyperspectral image, semi-transmission hyperspectral image can get clearer image which contains the internal quality information of agricultural products. In this study, 224 potato samples (149 normal samples and 75 hollow samples) were selected as the research object, and semi-transmission hyperspectral image acquisition system was constructed to acquire the hyperspectral images (390-1 040 nn) of the potato samples, and then the average spectrum of region of interest were extracted for spectral characteristics analysis. Normalize was used to preprocess the original spectrum, and prediction model were developed based on SVM using all wave bands, the accurate recognition rate of test set is only 87. 5%. In order to simplify the model competitive.adaptive reweighed sampling algorithm (CARS) and successive projection algorithm (SPA) were utilized to select important variables from the all 520 spectral variables and 8 variables were selected (454, 601, 639, 664, 748, 827, 874 and 936 nm). 94. 64% of the accurate recognition rate of test set was obtained by using the 8 variables to develop SVM model. Parameter optimization algorithms, including artificial fish swarm algorithm (AFSA), genetic algorithm (GA) and grid search algorithm, were used to optimize the SVM model parameters: penalty parameter c and kernel parameter g. After comparative analysis, AFSA, a new bionic optimization algorithm based on the foraging behavior of fish swarm, was proved to get the optimal model parameter (c=10. 659 1, g=0. 349 7), and the recognition accuracy of 10% were obtained for the AFSA

  9. EXTRACTING ROOF PARAMETERS AND HEAT BRIDGES OVER THE CITY OF OLDENBURG FROM HYPERSPECTRAL, THERMAL, AND AIRBORNE LASER SCANNING DATA

    Directory of Open Access Journals (Sweden)

    L. Bannehr

    2012-09-01

    Full Text Available Remote sensing methods are used to obtain different kinds of information about the state of the environment. Within the cooperative research project HiReSens, funded by the German BMBF, a hyperspectral scanner, an airborne laser scanner, a thermal camera, and a RGB-camera are employed on a small aircraft to determine roof material parameters and heat bridges of house tops over the city Oldenburg, Lower Saxony. HiReSens aims to combine various geometrical highly resolved data in order to achieve relevant evidence about the state of the city buildings. Thermal data are used to obtain the energy distribution of single buildings. The use of hyperspectral data yields information about material consistence of roofs. From airborne laser scanning data (ALS digital surface models are inferred. They build the basis to locate the best orientations for solar panels of the city buildings. The combination of the different data sets offers the opportunity to capitalize synergies between differently working systems. Central goals are the development of tools for the collection of heat bridges by means of thermal data, spectral collection of roofs parameters on basis of hyperspectral data as well as 3D-capture of buildings from airborne lasers scanner data. Collecting, analyzing and merging of the data are not trivial especially not when the resolution and accuracy is aimed in the domain of a few decimetre. The results achieved need to be regarded as preliminary. Further investigations are still required to prove the accuracy in detail.

  10. [Application of hyperspectral remote sensing in research on ecological boundary in north farming-pasturing transition in China].

    Science.gov (United States)

    Wang, Hong-Mei; Wang, Kun; Xie, Ying-Zhong

    2009-06-01

    Studies of ecological boundaries are important and have become a rapidly evolving part of contemporary ecology. The ecotones are dynamic and play several functional roles in ecosystem dynamics, and the changes in their locations can be used as an indicator of environment changes, and for these reasons, ecotones have recently become a focus of investigation of landscape ecology and global climate change. As the interest in ecotone increases, there is an increased need for formal techniques to detect it. Hence, to better study and understand the functional roles and dynamics of ecotones in ecosystem, we need quantitative methods to characterize them. In the semi-arid region of northern China, there exists a farming-pasturing transition resulting from grassland reclamation and deforestation. With the fragmentation of grassland landscape, the structure and function of the grassland ecosystem are changing. Given this perspective; new-image processing approaches are needed to focus on transition themselves. Hyperspectral remote sensing data, compared with wide-band remote sensing data, has the advantage of high spectral resolution. Hyperspectral remote sensing can be used to visualize transitional zones and to detect ecotone based on surface properties (e. g. vegetation, soil type, and soil moisture etc). In this paper, the methods of hyperspectral remote sensing information processing, spectral analysis and its application in detecting the vegetation classifications, vegetation growth state, estimating the canopy biochemical characteristics, soil moisture, soil organic matter etc are reviewed in detail. Finally the paper involves further application of hyperspectral remote sensing information in research on local climate in ecological boundary in north farming-pasturing transition in China.

  11. Identification of Mendelian inconsistencies between SNP and pedigree information of sibs

    Directory of Open Access Journals (Sweden)

    Calus Mario PL

    2011-10-01

    Full Text Available Abstract Background Using SNP genotypes to apply genomic selection in breeding programs is becoming common practice. Tools to edit and check the quality of genotype data are required. Checking for Mendelian inconsistencies makes it possible to identify animals for which pedigree information and genotype information are not in agreement. Methods Straightforward tests to detect Mendelian inconsistencies exist that count the number of opposing homozygous marker (e.g. SNP genotypes between parent and offspring (PAR-OFF. Here, we develop two tests to identify Mendelian inconsistencies between sibs. The first test counts SNP with opposing homozygous genotypes between sib pairs (SIBCOUNT. The second test compares pedigree and SNP-based relationships (SIBREL. All tests iteratively remove animals based on decreasing numbers of inconsistent parents and offspring or sibs. The PAR-OFF test, followed by either SIB test, was applied to a dataset comprising 2,078 genotyped cows and 211 genotyped sires. Theoretical expectations for distributions of test statistics of all three tests were calculated and compared to empirically derived values. Type I and II error rates were calculated after applying the tests to the edited data, while Mendelian inconsistencies were introduced by permuting pedigree against genotype data for various proportions of animals. Results Both SIB tests identified animal pairs for which pedigree and genomic relationships could be considered as inconsistent by visual inspection of a scatter plot of pairwise pedigree and SNP-based relationships. After removal of 235 animals with the PAR-OFF test, SIBCOUNT (SIBREL identified 18 (22 additional inconsistent animals. Seventeen animals were identified by both methods. The numbers of incorrectly deleted animals (Type I error, were equally low for both methods, while the numbers of incorrectly non-deleted animals (Type II error, were considerably higher for SIBREL compared to SIBCOUNT. Conclusions

  12. HELICoiD project: a new use of hyperspectral imaging for brain cancer detection in real-time during neurosurgical operations

    Science.gov (United States)

    Fabelo, Himar; Ortega, Samuel; Kabwama, Silvester; Callico, Gustavo M.; Bulters, Diederik; Szolna, Adam; Pineiro, Juan F.; Sarmiento, Roberto

    2016-05-01

    Hyperspectral images allow obtaining large amounts of information about the surface of the scene that is captured by the sensor. Using this information and a set of complex classification algorithms is possible to determine which material or substance is located in each pixel. The HELICoiD (HypErspectraL Imaging Cancer Detection) project is a European FET project that has the goal to develop a demonstrator capable to discriminate, with high precision, between normal and tumour tissues, operating in real-time, during neurosurgical operations. This demonstrator could help the neurosurgeons in the process of brain tumour resection, avoiding the excessive extraction of normal tissue and unintentionally leaving small remnants of tumour. Such precise delimitation of the tumour boundaries will improve the results of the surgery. The HELICoiD demonstrator is composed of two hyperspectral cameras obtained from Headwall. The first one in the spectral range from 400 to 1000 nm (visible and near infrared) and the second one in the spectral range from 900 to 1700 nm (near infrared). The demonstrator also includes an illumination system that covers the spectral range from 400 nm to 2200 nm. A data processing unit is in charge of managing all the parts of the demonstrator, and a high performance platform aims to accelerate the hyperspectral image classification process. Each one of these elements is installed in a customized structure specially designed for surgical environments. Preliminary results of the classification algorithms offer high accuracy (over 95%) in the discrimination between normal and tumour tissues.

  13. Two-dimensional hidden semantic information model for target saliency detection and eyetracking identification

    Science.gov (United States)

    Wan, Weibing; Yuan, Lingfeng; Zhao, Qunfei; Fang, Tao

    2018-01-01

    Saliency detection has been applied to the target acquisition case. This paper proposes a two-dimensional hidden Markov model (2D-HMM) that exploits the hidden semantic information of an image to detect its salient regions. A spatial pyramid histogram of oriented gradient descriptors is used to extract features. After encoding the image by a learned dictionary, the 2D-Viterbi algorithm is applied to infer the saliency map. This model can predict fixation of the targets and further creates robust and effective depictions of the targets' change in posture and viewpoint. To validate the model with a human visual search mechanism, two eyetrack experiments are employed to train our model directly from eye movement data. The results show that our model achieves better performance than visual attention. Moreover, it indicates the plausibility of utilizing visual track data to identify targets.

  14. Real-time progressive hyperspectral image processing endmember finding and anomaly detection

    CERN Document Server

    Chang, Chein-I

    2016-01-01

    The book covers the most crucial parts of real-time hyperspectral image processing: causality and real-time capability. Recently, two new concepts of real time hyperspectral image processing, Progressive Hyperspectral Imaging (PHSI) and Recursive Hyperspectral Imaging (RHSI). Both of these can be used to design algorithms and also form an integral part of real time hyperpsectral image processing. This book focuses on progressive nature in algorithms on their real-time and causal processing implementation in two major applications, endmember finding and anomaly detection, both of which are fundamental tasks in hyperspectral imaging but generally not encountered in multispectral imaging. This book is written to particularly address PHSI in real time processing, while a book, Recursive Hyperspectral Sample and Band Processing: Algorithm Architecture and Implementation (Springer 2016) can be considered as its companion book. Includes preliminary background which is essential to those who work in hyperspectral ima...

  15. Supervised Vicarious Calibration (SVC of Multi-Source Hyperspectral Remote-Sensing Data

    Directory of Open Access Journals (Sweden)

    Anna Brook

    2015-05-01

    Full Text Available Introduced in 2011, the supervised vicarious calibration (SVC approach is a promising approach to radiometric calibration and atmospheric correction of airborne hyperspectral (HRS data. This paper presents a comprehensive study by which the SVC method has been systematically examined and a complete protocol for its practical execution has been established—along with possible limitations encountered during the campaign. The technique was applied to multi-sourced HRS data in order to: (1 verify the at-sensor radiometric calibration and (2 obtain radiometric and atmospheric correction coefficients. Spanning two select study sites along the southeast coast of France, data were collected simultaneously by three airborne sensors (AisaDUAL, AHS and CASI-1500i aboard two aircrafts (CASA of National Institute for Aerospace Technology INTA ES and DORNIER 228 of NERC-ARSF Centre UK. The SVC ground calibration site was assembled along sand dunes near Montpellier and the thematic data were acquired from other areas in the south of France (Salon-de-Provence, Marseille, Avignon and Montpellier on 28 October 2010 between 12:00 and 16:00 UTC. The results of this study confirm that the SVC method enables reliable inspection and, if necessary, in-situ fine radiometric recalibration of airborne hyperspectral data. Independent of sensor or platform quality, the SVC approach allows users to improve at-sensor data to obtain more accurate physical units and subsequently improved reflectance information. Flight direction was found to be important, whereas the flight altitude posed very low impact. The numerous rules and major outcomes of this experiment enable a new standard of atmospherically corrected data based on better radiometric output. Future research should examine the potential of SVC to be applied to super-and-hyperspectral data obtained from on-orbit sensors.

  16. Visible and Near-Infrared Hyperspectral Imaging for Cooking Loss Classification of Fresh Broiler Breast Fillets

    Directory of Open Access Journals (Sweden)

    Hongzhe Jiang

    2018-02-01

    Full Text Available Cooking loss (CL is a critical quality attribute directly relating to meat juiciness. The potential of the hyperspectral imaging (HSI technique was investigated for non-invasively classifying and visualizing the CL of fresh broiler breast meat. Hyperspectral images of total 75 fresh broiler breast fillets were acquired by the system operating in the visible and near-infrared (VNIR, 400–1000 nm range. Mean spectra were extracted from regions of interest (ROIs determined by pure muscle tissue pixels. CL was firstly measured by calculating the weight loss in cooking, and then fillets were grouped into high-CL and low-CL according to the threshold of 20%. The classification methods partial least square-discriminant analysis (PLS-DA and radial basis function-support vector machine (RBF-SVM were applied, respectively, to determine the optimal spectral calibration strategy. Results showed that the PLS-DA model developed using the data, that is, first-order derivative (Der1 of VNIR full spectra, performed best with correct classification rates (CCRs of 0.90 and 0.79 for the calibration and prediction sets, respectively. Furthermore, to simplify the optimal PLS-DA model and make it practical, effective wavelengths were individually selected using uninformative variable elimination (UVE and competitive adaptive reweighted sampling (CARS. Through performance comparison, the CARS-PLS-DA combination was identified as the optimal method and the PLS-DA model built with 18 informative wavelengths selected by CARS resulted in good CCRs of 0.86 and 0.79. Finally, classification maps were created by predicting CL categories of each pixel in the VNIR hyperspectral images using the CARS-PLS-DA model, and the general CL categories of fillets were readily discernible. The overall results were encouraging and showed the promising potential of the VNIR HSI technique for classifying fresh broiler breast fillets into different CL categories.

  17. An assessment of independent component analysis for detection of military targets from hyperspectral images

    Science.gov (United States)

    Tiwari, K. C.; Arora, M. K.; Singh, D.

    2011-10-01

    Hyperspectral data acquired over hundreds of narrow contiguous wavelength bands are extremely suitable for target detection due to their high spectral resolution. Though spectral response of every material is expected to be unique, but in practice, it exhibits variations, which is known as spectral variability. Most target detection algorithms depend on spectral modelling using a priori available target spectra In practice, target spectra is, however, seldom available a priori. Independent component analysis (ICA) is a new evolving technique that aims at finding out components which are statistically independent or as independent as possible. The technique therefore has the potential of being used for target detection applications. A assessment of target detection from hyperspectral images using ICA and other algorithms based on spectral modelling may be of immense interest, since ICA does not require a priori target information. The aim of this paper is, thus, to assess the potential of ICA based algorithm vis a vis other prevailing algorithms for military target detection. Four spectral matching algorithms namely Orthogonal Subspace Projection (OSP), Constrained Energy Minimisation (CEM), Spectral Angle Mapper (SAM) and Spectral Correlation Mapper (SCM), and four anomaly detection algorithms namely OSP anomaly detector (OSPAD), Reed-Xiaoli anomaly detector (RXD), Uniform Target Detector (UTD) and a combination of Reed-Xiaoli anomaly detector and Uniform Target Detector (RXD-UTD) were considered. The experiments were conducted using a set of synthetic and AVIRIS hyperspectral images containing aircrafts as military targets. A comparison of true positive and false positive rates of target detections obtained from ICA and other algorithms plotted on a receiver operating curves (ROC) space indicates the superior performance of the ICA over other algorithms.

  18. A Novel Measurement Matrix Optimization Approach for Hyperspectral Unmixing

    Directory of Open Access Journals (Sweden)

    Su Xu

    2017-01-01

    Full Text Available Each pixel in the hyperspectral unmixing process is modeled as a linear combination of endmembers, which can be expressed in the form of linear combinations of a number of pure spectral signatures that are known in advance. However, the limitation of Gaussian random variables on its computational complexity or sparsity affects the efficiency and accuracy. This paper proposes a novel approach for the optimization of measurement matrix in compressive sensing (CS theory for hyperspectral unmixing. Firstly, a new Toeplitz-structured chaotic measurement matrix (TSCMM is formed by pseudo-random chaotic elements, which can be implemented by a simple hardware; secondly, rank revealing QR factorization with eigenvalue decomposition is presented to speed up the measurement time; finally, orthogonal gradient descent method for measurement matrix optimization is used to achieve optimal incoherence. Experimental results demonstrate that the proposed approach can lead to better CS reconstruction performance with low extra computational cost in hyperspectral unmixing.

  19. A system design for storing, archiving, and retrieving hyperspectral data

    Science.gov (United States)

    Dedecker, Ralph G.; Whittaker, Tom; Garcia, Raymond K.; Knuteson, Robert O.

    2004-10-01

    Hyperspectral data and products derived from instrumentation such as the Atmospheric Infrared Sounder (AIRS), the Cross-track Infrared Sounder (CrIS), Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS) and the GOES-R Hyperspectral Environmental Suite (HES) will impose storage and data retrieval requirements that far exceed the demands of earlier generation remote sensing instrumentation used for atmospheric science research. A new architecture designed to address projected real time and research needs is undergoing prototype design and development. The system is designed using proven aspects of distributed data storage networks, descriptive metadata associated with stored files, data cataloging and database search schemes, and a data delivery approach that obeys accepted standards. Preliminary implementation and testing of some components of this architecture indicate that the design approach shows promise of an improved method for storage and library functionality for the data volumes associated with operational hyperspectral instrumentation.

  20. Spatial and temporal variability of hyperspectral signatures of terrain

    Science.gov (United States)

    Jones, K. F.; Perovich, D. K.; Koenig, G. G.

    2008-04-01

    Electromagnetic signatures of terrain exhibit significant spatial heterogeneity on a range of scales as well as considerable temporal variability. A statistical characterization of the spatial heterogeneity and spatial scaling algorithms of terrain electromagnetic signatures are required to extrapolate measurements to larger scales. Basic terrain elements including bare soil, grass, deciduous, and coniferous trees were studied in a quasi-laboratory setting using instrumented test sites in Hanover, NH and Yuma, AZ. Observations were made using a visible and near infrared spectroradiometer (350 - 2500 nm) and hyperspectral camera (400 - 1100 nm). Results are reported illustrating: i) several difference scenes; ii) a terrain scene time series sampled over an annual cycle; and iii) the detection of artifacts in scenes. A principal component analysis indicated that the first three principal components typically explained between 90 and 99% of the variance of the 30 to 40-channel hyperspectral images. Higher order principal components of hyperspectral images are useful for detecting artifacts in scenes.

  1. Challenges of the information age: the impact of false discovery on pathway identification.

    Science.gov (United States)

    Rog, Colin J; Chekuri, Srinivasa C; Edgerton, Mary E

    2012-11-21

    Pathways with members that have known relevance to a disease are used to support hypotheses generated from analyses of gene expression and proteomic studies. Using cancer as an example, the pitfalls of searching pathways databases as support for genes and proteins that could represent false discoveries are explored. The frequency with which networks could be generated from 100 instances each of randomly selected five and ten genes sets as input to MetaCore, a commercial pathways database, was measured. A PubMed search enumerated cancer-related literature published for any gene in the networks. Using three, two, and one maximum intervening step between input genes to populate the network, networks were generated with frequencies of 97%, 77%, and 7% using ten gene sets and 73%, 27%, and 1% using five gene sets. PubMed reported an average of 4225 cancer-related articles per network gene. This can be attributed to the richly populated pathways databases and the interest in the molecular basis of cancer. As information sources become enriched, they are more likely to generate plausible mechanisms for false discoveries.

  2. Forensic Evidence Identification and Modeling for Attacks against a Simulated Online Business Information System

    Directory of Open Access Journals (Sweden)

    Manghui Tu

    2012-12-01

    Full Text Available Forensic readiness can support future forensics investigation or auditing on external/internal attacks, internal sabotage and espionage, and business frauds. To establish forensics readiness, it is essential for an organization to identify what evidences are relevant and where they can be found, to determine whether they are logged in a forensic sound way and whether all the needed evidences are available to reconstruct the events successfully.  Our goal of this research is to ensure evidence availability. First, both external and internal attacks are molded as augmented attack trees/graphs based on the system vulnerabilities. Second, modeled attacks are conducted against a honeynet simulating an online business information system, and each honeypot's hard drive is forensic sound imaged for each individual attack. Third, an evidence tree/graph will be built after forensics examination on the disk images for each attack. The evidence trees/graphs are expected to be used for automatic crime scene reconstruction and automatic attack/fraud detection in the future.

  3. Identification and classification of inland wetlands in Tamaulipas through remote sensing and geographic information systems

    Directory of Open Access Journals (Sweden)

    Wilver Enrique Salinas Castillo

    2012-03-01

    Full Text Available This work aimed to identify and classify artificial and natural inland wetlands in the state of Tamaulipas, Mexico, important for migratory aquatic birds. Historically, efforts nave been focused on natural coastal wetlands or specific water bodies located in highlands; however, these surveys have not reflected the dramatic changes in landscape due to farming development in northem Mexico in the Iatest decades. Agricultural fieids and dams associated to them provide food, water and shelterto many migratory birds and other species, a fact not well documented. Factors that may influence the use of wetlands were analyzed, including surface area, associated vegetation and proximity to agricultural fieids. The inventory of inland wetlands was based on the analysis of seven 2000 Landsat ETM satellite imagery and field data gathered from 261 sites surveyed in 2001. Baseline maps were created and GIS analyses were undertaken to classify these water bodies. More than 23 000 inland wetlands were identified, and the information derived from this study will be assist in the development of programs to manage and protect wetlands of importance for migratory aquatic birds in Tamaulipas.

  4. Hyperspectral Cubesat Constellation for Rapid Natural Hazard Response

    Science.gov (United States)

    Mandl, D.; Huemmrich, K. F.; Ly, V. T.; Handy, M.; Ong, L.; Crum, G.

    2015-12-01

    With the advent of high performance space networks that provide total coverage for Cubesats, the paradigm for low cost, high temporal coverage with hyperspectral instruments becomes more feasible. The combination of ground cloud computing resources, high performance with low power consumption onboard processing, total coverage for the cubesats and social media provide an opprotunity for an architecture that provides cost-effective hyperspectral data products for natural hazard response and decision support. This paper provides a series of pathfinder efforts to create a scalable Intelligent Payload Module(IPM) that has flown on a variety of airborne vehicles including Cessna airplanes, Citation jets and a helicopter and will fly on an Unmanned Aerial System (UAS) hexacopter to monitor natural phenomena. The IPM's developed thus far were developed on platforms that emulate a satellite environment which use real satellite flight software, real ground software. In addition, science processing software has been developed that perform hyperspectral processing onboard using various parallel processing techniques to enable creation of onboard hyperspectral data products while consuming low power. A cubesat design was developed that is low cost and that is scalable to larger consteallations and thus can provide daily hyperspectral observations for any spot on earth. The design was based on the existing IPM prototypes and metrics that were developed over the past few years and a shrunken IPM that can perform up to 800 Mbps throughput. Thus this constellation of hyperspectral cubesats could be constantly monitoring spectra with spectral angle mappers after Level 0, Level 1 Radiometric Correction, Atmospheric Correction processing. This provides the opportunity daily monitoring of any spot on earth on a daily basis at 30 meter resolution which is not available today.

  5. Hyperspectral imaging for diagnosis and quality control in agri-food and industrial sectors

    Science.gov (United States)

    García-Allende, P. Beatriz; Conde, Olga M.; Mirapeix, Jesus; Cobo, Adolfo; Lopez-Higuera, Jose M.

    2010-04-01

    Optical spectroscopy has been utilized in various fields of science, industry and medicine, since each substance is discernible from all others by its spectral properties. However, optical spectroscopy traditionally generates information on the bulk properties of the whole sample, and mainly in the agri-food industry some product properties result from the heterogeneity in its composition. This monitoring is considerably more challenging and can be successfully achieved by the so-called hyperspectral imaging technology, which allows the simultaneous determination of the optical spectrum and the spatial location of an object in a surface. In addition, it is a nonintrusive and non-contact technique which gives rise to a great potential for industrial applications and it does not require any particular preparation of the samples, which is a primary concern in food monitoring. This work illustrates an overview of approaches based on this technology to address different problems in agri-food and industrial sectors. The hyperspectral system was originally designed and tested for raw material on-line discrimination, which is a key factor in the input stages of many industrial sectors. The combination of the acquisition of the spectral information across transversal lines while materials are being transported on a conveyor belt, and appropriate image analyses have been successfully validated in the tobacco industry. Lastly, the use of imaging spectroscopy applied to online welding quality monitoring is discussed and compared with traditional spectroscopic approaches in this regard.

  6. Algorithm for retrieving vegetative canopy and leaf parameters from multi- and hyperspectral imagery

    Science.gov (United States)

    Borel, Christoph

    2009-05-01

    In recent years hyper-spectral data has been used to retrieve information about vegetative canopies such as leaf area index and canopy water content. For the environmental scientist these two parameters are valuable, but there is potentially more information to be gained as high spatial resolution data becomes available. We developed an Amoeba (Nelder-Mead or Simplex) based program to invert a vegetative canopy radiosity model coupled with a leaf (PROSPECT5) reflectance model and modeled for the background reflectance (e.g. soil, water, leaf litter) to a measured reflectance spectrum. The PROSPECT5 leaf model has five parameters: leaf structure parameter Nstru, chlorophyll a+b concentration Cab, carotenoids content Car, equivalent water thickness Cw and dry matter content Cm. The canopy model has two parameters: total leaf area index (LAI) and number of layers. The background reflectance model is either a single reflectance spectrum from a spectral library() derived from a bare area pixel on an image or a linear mixture of soil spectra. We summarize the radiosity model of a layered canopy and give references to the leaf/needle models. The method is then tested on simulated and measured data. We investigate the uniqueness, limitations and accuracy of the retrieved parameters on canopy parameters (low, medium and high leaf area index) spectral resolution (32 to 211 band hyperspectral), sensor noise and initial conditions.

  7. Unmixing hyperspectral images using Markov random fields

    International Nuclear Information System (INIS)

    Eches, Olivier; Dobigeon, Nicolas; Tourneret, Jean-Yves

    2011-01-01

    This paper proposes a new spectral unmixing strategy based on the normal compositional model that exploits the spatial correlations between the image pixels. The pure materials (referred to as endmembers) contained in the image are assumed to be available (they can be obtained by using an appropriate endmember extraction algorithm), while the corresponding fractions (referred to as abundances) are estimated by the proposed algorithm. Due to physical constraints, the abundances have to satisfy positivity and sum-to-one constraints. The image is divided into homogeneous distinct regions having the same statistical properties for the abundance coefficients. The spatial dependencies within each class are modeled thanks to Potts-Markov random fields. Within a Bayesian framework, prior distributions for the abundances and the associated hyperparameters are introduced. A reparametrization of the abundance coefficients is proposed to handle the physical constraints (positivity and sum-to-one) inherent to hyperspectral imagery. The parameters (abundances), hyperparameters (abundance mean and variance for each class) and the classification map indicating the classes of all pixels in the image are inferred from the resulting joint posterior distribution. To overcome the complexity of the joint posterior distribution, Markov chain Monte Carlo methods are used to generate samples asymptotically distributed according to the joint posterior of interest. Simulations conducted on synthetic and real data are presented to illustrate the performance of the proposed algorithm.

  8. Evaluation of Algorithms for Compressing Hyperspectral Data

    Science.gov (United States)

    Cook, Sid; Harsanyi, Joseph; Faber, Vance

    2003-01-01

    With EO-1 Hyperion in orbit NASA is showing their continued commitment to hyperspectral imaging (HSI). As HSI sensor technology continues to mature, the ever-increasing amounts of sensor data generated will result in a need for more cost effective communication and data handling systems. Lockheed Martin, with considerable experience in spacecraft design and developing special purpose onboard processors, has teamed with Applied Signal & Image Technology (ASIT), who has an extensive heritage in HSI spectral compression and Mapping Science (MSI) for JPEG 2000 spatial compression expertise, to develop a real-time and intelligent onboard processing (OBP) system to reduce HSI sensor downlink requirements. Our goal is to reduce the downlink requirement by a factor > 100, while retaining the necessary spectral and spatial fidelity of the sensor data needed to satisfy the many science, military, and intelligence goals of these systems. Our compression algorithms leverage commercial-off-the-shelf (COTS) spectral and spatial exploitation algorithms. We are currently in the process of evaluating these compression algorithms using statistical analysis and NASA scientists. We are also developing special purpose processors for executing these algorithms onboard a spacecraft.

  9. Hyperspectral aerosol optical depths from TCAP flights

    Energy Technology Data Exchange (ETDEWEB)

    Shinozuka, Yohei [NASA Ames Research Center (ARC), Moffett Field, Mountain View, CA (United States); Bay Area Environmental REsearch Institute; Johnson, Roy R [NASA Ames Research Center (ARC), Moffett Field, Mountain View, CA (United States); Flynn, Connor J [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Russell, Philip B [NASA Ames Research Center (ARC), Moffett Field, Mountain View, CA (United States); Schmid, Beat [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

    2016-06-01

    4STAR (Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research), a hyperspectral airborne sunphotometer, acquired aerosol optical depths (AOD) at 1 Hz during all July 2012 flights of the Two Column Aerosol Project (TCAP). Root-mean-square differences from AERONET ground-based observations were 0.01 at wavelengths between 500-1020 nm, 0.02 at 380 and 1640 nm and 0.03 at 440 nm in four clear-sky fly-over events, and similar in ground side-by-side comparisons. Changes in the above-aircraft AOD across 3- km-deep spirals were typically consistent with integrals of coincident in situ (on DOE Gulfstream 1 with 4STAR) and lidar (on NASA B200) extinction measurements within 0.01, 0.03, 0.01, 0.02, 0.02, 0.02 at 355, 450, 532, 550, 700, 1064 nm, respectively, despite atmospheric variations and combined measurement uncertainties. Finer vertical differentials of the 4STAR measurements matched the in situ ambient extinction profile within 14% for one homogeneous column. For the AOD observed between 350-1660 nm, excluding strong

  10. Resolving Mixed Algal Species in Hyperspectral Images

    Directory of Open Access Journals (Sweden)

    Mehrube Mehrubeoglu

    2013-12-01

    Full Text Available We investigated a lab-based hyperspectral imaging system’s response from pure (single and mixed (two algal cultures containing known algae types and volumetric combinations to characterize the system’s performance. The spectral response to volumetric changes in single and combinations of algal mixtures with known ratios were tested. Constrained linear spectral unmixing was applied to extract the algal content of the mixtures based on abundances that produced the lowest root mean square error. Percent prediction error was computed as the difference between actual percent volumetric content and abundances at minimum RMS error. Best prediction errors were computed as 0.4%, 0.4% and 6.3% for the mixed spectra from three independent experiments. The worst prediction errors were found as 5.6%, 5.4% and 13.4% for the same order of experiments. Additionally, Beer-Lambert’s law was utilized to relate transmittance to different volumes of pure algal suspensions demonstrating linear logarithmic trends for optical property measurements.

  11. Identification of Evacuation Routes in Tacloban City using Geographic Information System

    Science.gov (United States)

    Mendoza, Jerico; Mahar Francisco Lagmay, Alfredo; Santiago, Joy; Suarez, John Kenneth

    2016-04-01

    The Philippines is the second most at risk to natural hazards according to the 2014 World Risk Report. On 8 November 2013, category 5 Typhoon Haiyan crossed the central region of the Philippines with maximum sustained wind reaching 315 kph. Considered as one of the strongest typhoons that made landfall in recorded history, Typhoon Haiyan caused USD 8 billion damage to properties, 6,293 deaths, 28,689 injured and 1,061 missing persons. Tacloban City, located in the north-eastern part of the island of Leyte in Eastern Visayas region, is one of the area most devastated by Typhoon Haiyan. The city is susceptible to other natural hazards given its geography, topography and geology. This condition emphasizes the need for preventive measures to avoid further loss of lives and destruction to properties. Evacuation is a mitigating strategy which involves the process of moving people from dangerous places to safer locations. Using Geographic Information System (GIS), a multi-hazard map of Tacloban City was created to determine safe areas for evacuation centers. The optimal route for evacuation was identified using ArcGIS Network Analyst's routing solver based on Dijkstra's algorithm. The medium of transportation used in the analysis is by foot with an average speed of 5.0 kph. Furthermore, the study assumes that all roads are passable and fully functional during the travel period and that there are no structures, trees and other debris that may act as road blockage. The study can be used as a reference in hazard assessment for disaster risk management and evacuation planning. This can be further improved by incorporating behaviour of the affected population and other socio-economic factors, different modes of transportation and detailed analysis of topography.

  12. Detecting brain tumor in pathological slides using hyperspectral imaging.

    Science.gov (United States)

    Ortega, Samuel; Fabelo, Himar; Camacho, Rafael; de la Luz Plaza, María; Callicó, Gustavo M; Sarmiento, Roberto

    2018-02-01

    Hyperspectral imaging (HSI) is an emerging technology for medical diagnosis. This research work presents a proof-of-concept on the use of HSI data to automatically detect human brain tumor tissue in pathological slides. The samples, consisting of hyperspectral cubes collected from 400 nm to 1000 nm, were acquired from ten different patients diagnosed with high-grade glioma. Based on the diagnosis provided by pathologists, a spectral library of normal and tumor tissues was created and processed using three different supervised classification algorithms. Results prove that HSI is a suitable technique to automatically detect high-grade tumors from pathological slides.

  13. Bread Water Content Measurement Based on Hyperspectral Imaging

    DEFF Research Database (Denmark)

    Liu, Zhi; Møller, Flemming

    2011-01-01

    Water content is one of the most important properties of the bread for tasting assesment or store monitoring. Traditional bread water content measurement methods mostly are processed manually, which is destructive and time consuming. This paper proposes an automated water content measurement...... for bread quality based on near-infrared hyperspectral imaging against the conventional manual loss-in-weight method. For this purpose, the hyperspectral components unmixing technology is used for measuring the water content quantitatively. And the definition on bread water content index is presented...

  14. Hyperspectral imaging using the single-pixel Fourier transform technique

    Science.gov (United States)

    Jin, Senlin; Hui, Wangwei; Wang, Yunlong; Huang, Kaicheng; Shi, Qiushuai; Ying, Cuifeng; Liu, Dongqi; Ye, Qing; Zhou, Wenyuan; Tian, Jianguo

    2017-03-01

    Hyperspectral imaging technology is playing an increasingly important role in the fields of food analysis, medicine and biotechnology. To improve the speed of operation and increase the light throughput in a compact equipment structure, a Fourier transform hyperspectral imaging system based on a single-pixel technique is proposed in this study. Compared with current imaging spectrometry approaches, the proposed system has a wider spectral range (400-1100 nm), a better spectral resolution (1 nm) and requires fewer measurement data (a sample rate of 6.25%). The performance of this system was verified by its application to the non-destructive testing of potatoes.

  15. Spectral mixture analyses of hyperspectral data acquired using a tethered balloon

    Science.gov (United States)

    Chen, Xuexia; Vierling, Lee

    2006-01-01

    short wave infrared (SWIR) region in the hyperspectral and multispectral endmember data significantly reduced the Pearson correlation coefficient values among endmember spectra. Therefore, combination of visible, NIR, and SWIR information is likely to further improve the utility of SMA in understanding ecosystem structure and function and may help narrow uncertainties when utilizing remotely sensed data to extrapolate trace glas flux measurements from the canopy scale to the landscape scale.

  16. 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

  17. A discrimination model in waste plastics sorting using NIR hyperspectral imaging system.

    Science.gov (United States)

    Zheng, Yan; Bai, Jiarui; Xu, Jingna; Li, Xiayang; Zhang, Yimin

    2018-02-01

    Classification of plastics is important in the recycling industry. A plastic identification model in the near infrared spectroscopy wavelength range 1000-2500 nm is proposed for the characterization and sorting of waste plastics using acrylonitrile butadiene styrene (ABS), polystyrene (PS), polypropylene (PP), polyethylene (PE), polyethylene terephthalate (PET), and polyvinyl chloride (PVC). The model is built by the feature wavelengths of standard samples applying the principle component analysis (PCA), and the accuracy, property and cross-validation of the model were analyzed. The model just contains a simple equation, center of mass coordinates, and radial distance, with which it is easy to develop classification and sorting software. A hyperspectral imaging system (HIS) with the identification model verified its practical application by using the unknown plastics. Results showed that the identification accuracy of unknown samples is 100%. All results suggested that the discrimination model was potential to an on-line characterization and sorting platform of waste plastics based on HIS. Copyright © 2017 Elsevier Ltd. All rights reserved.

  18. 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.

  19. Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context

    Directory of Open Access Journals (Sweden)

    Julie Transon

    2018-01-01

    Full Text Available In the last few decades, researchers have developed a plethora of hyperspectral Earth Observation (EO remote sensing techniques, analysis and applications. While hyperspectral exploratory sensors are demonstrating their potential, Sentinel-2 multispectral satellite remote sensing is now providing free, open, global and systematic high resolution visible and infrared imagery at a short revisit time. Its recent launch suggests potential synergies between multi- and hyper-spectral data. This study, therefore, reviews 20 years of research and applications in satellite hyperspectral remote sensing through the analysis of Earth observation hyperspectral sensors’ publications that cover the Sentinel-2 spectrum range: Hyperion, TianGong-1, PRISMA, HISUI, EnMAP, Shalom, HyspIRI and HypXIM. More specifically, this study (i brings face to face past and future hyperspectral sensors’ applications with Sentinel-2’s and (ii analyzes the applications’ requirements in terms of spatial and temporal resolutions. Eight main application topics were analyzed including vegetation, agriculture, soil, geology, urban, land use, water resources and disaster. Medium spatial resolution, long revisit time and low signal-to-noise ratio in the short-wave infrared of some hyperspectral sensors were highlighted as major limitations for some applications compared to the Sentinel-2 system. However, these constraints mainly concerned past hyperspectral sensors, while they will probably be overcome by forthcoming instruments. Therefore, this study is putting forward the compatibility of hyperspectral sensors and Sentinel-2 systems for resolution enhancement techniques in order to increase the panel of hyperspectral uses.

  20. Hyperspectral image reconstruction using RGB color for foodborne pathogen detection on agar plates

    Science.gov (United States)

    Yoon, Seung-Chul; Shin, Tae-Sung; Park, Bosoon; Lawrence, Kurt C.; Heitschmidt, Gerald W.

    2014-03-01

    This paper reports the latest development of a color vision technique for detecting colonies of foodborne pathogens grown on agar plates with a hyperspectral image classification model that was developed using full hyperspectral data. The hyperspectral classification model depended on reflectance spectra measured in the visible and near-infrared spectral range from 400 and 1,000 nm (473 narrow spectral bands). Multivariate regression methods were used to estimate and predict hyperspectral data from RGB color values. The six representative non-O157 Shiga-toxin producing Eschetichia coli (STEC) serogroups (O26, O45, O103, O111, O121, and O145) were grown on Rainbow agar plates. A line-scan pushbroom hyperspectral image sensor was used to scan 36 agar plates grown with pure STEC colonies at each plate. The 36 hyperspectral images of the agar plates were divided in half to create training and test sets. The mean Rsquared value for hyperspectral image estimation was about 0.98 in the spectral range between 400 and 700 nm for linear, quadratic and cubic polynomial regression models and the detection accuracy of the hyperspectral image classification model with the principal component analysis and k-nearest neighbors for the test set was up to 92% (99% with the original hyperspectral images). Thus, the results of the study suggested that color-based detection may be viable as a multispectral imaging solution without much loss of prediction accuracy compared to hyperspectral imaging.

  1. Crop stress detection and classification using hyperspectral remote sensing

    Science.gov (United States)

    Irby, Jon Trenton

    Agricultural production has observed many changes in technology over the last 20 years. Producers are able to utilize technologies such as site-specific applicators and remotely sensed data to assist with decision making for best management practices which can improve crop production and provide protection to the environment. It is known that plant stress can interfere with photosynthetic reactions within the plant and/or the physical structure of the plant. Common types of stress associated with agricultural crops include herbicide induced stress, nutrient stress, and drought stress from lack of water. Herbicide induced crop stress is not a new problem. However, with increased acreage being planting in varieties/hybrids that contain herbicide resistant traits, herbicide injury to non-target crops will continue to be problematic for producers. With rapid adoption of herbicide-tolerant cropping systems, it is likely that herbicide induced stress will continue to be a major concern. To date, commercially available herbicide-tolerant varieties/hybrids contain traits which allow herbicides like glyphosate and glufosinate-ammonium to be applied as a broadcast application during the growing season. Both glyphosate and glufosinate-ammonium are broad spectrum herbicides which have activity on a large number of plant species, including major crops like non-transgenic soybean, corn, and cotton. Therefore, it is possible for crop stress from herbicide applications to occur in neighboring fields that contain susceptible crop varieties/hybrids. Nutrient and moisture stress as well as stress caused by herbicide applications can interact to influence yields in agricultural fields. If remotely sensed data can be used to accurately identify specific levels of crop stress, it is possible that producers can use this information to better assist them in crop management to maximize yields and protect their investments. This research was conducted to evaluate classification of specific

  2. Soybean varieties discrimination using non-imaging hyperspectral sensor

    Science.gov (United States)

    da Silva Junior, Carlos Antonio; Nanni, Marcos Rafael; Shakir, Muhammad; Teodoro, Paulo Eduardo; de Oliveira-Júnior, José Francisco; Cezar, Everson; de Gois, Givanildo; Lima, Mendelson; Wojciechowski, Julio Cesar; Shiratsuchi, Luciano Shozo

    2018-03-01

    Infrared region of electromagnetic spectrum has remarkable applications in crop studies. Infrared along with Red band has been used to develop certain vegetation indices. These indices like NDVI, EVI provide important information on any crop physiological stages. The main objective of this research was to discriminate 4 different soybean varieties (BMX Potência, NA5909, FT Campo Mourão and Don Mario) using non-imaging hyperspectral sensor. The study was conducted in four agricultural areas in the municipality of Deodápolis (MS), Brazil. For spectral analysis, 2400 field samples were taken from soybean leaves by means of FieldSpec 3 JR spectroradiometer in the range from 350 to 2500 nm. The data were evaluated through multivariate analysis with the whole set of spectral curves isolated by blue, green, red and near infrared wavelengths along with the addition of vegetation indices like (Enhanced Vegetation Index - EVI, Normalized Difference Vegetation Index - NDVI, Green Normalized Difference Vegetation Index - GNDVI, Soil-adjusted Vegetation Index - SAVI, Transformed Vegetation Index - TVI and Optimized Soil-Adjusted Vegetation Index - OSAVI). A number of the analysis performed where, discriminant (60 and 80% of the data), simulated discriminant (40 and 20% of data), principal component (PC) and cluster analysis (CA). Discriminant and simulated discriminant analyze presented satisfactory results, with average global hit rates of 99.28 and 98.77%, respectively. The results obtained by PC and CA revealed considerable associations between the evaluated variables and the varieties, which indicated that each variety has a variable that discriminates it more effectively in relation to the others. There was great variation in the sample size (number of leaves) for estimating the mean of variables. However, it was possible to observe that 200 leaves allow to obtain a maximum error of 2% in relation to the mean.

  3. Hyperspectral observation of anthropogenic and biogenic pollution in coastal zone

    Science.gov (United States)

    Lavrova, Olga; Loupian, Evgeny; Mityagina, Marina; Uvarov, Ivan

    The work presents results of anthropogenic and biogenic pollution detection in coastal zones of the Black and Caspian Seas based on satellite hyperspetral data provided by the Hyperion and HICO instruments. Techniques developed on the basis of the analysis of spectral characteristics calculated in special points were employed to address the following problems: (a) assessment of the blooming intensity of cyanobacteria and their distribution in bays of western Crimea and discrimination between anthropogenic pollutant discharge events and algae bloom; (b) detection of anthropogenic pollution in Crimean lakes utilized as industrial liquid discharge reservoirs; (c) detection of oil pollution in areas of shelf oil production in the Caspian Sea. Information values of different spectral bands and their composites were estimated in connection with the retrieval of the main sea water components: phytoplankton, suspended matter and colored organic matter, and also various anthropogenic pollutants, including oil. Software tools for thematic hyperspectral data processing in application to the investigation of sea coastal zones and internal water bodies were developed on the basis of the See the Sea geoportal created by the Space Research Institute RAS. The geoportal is focused on the study of processes in the world ocean with the emphasis on the advantages of satellite systems of observation. The tools that were introduced into the portal allow joint analysis of quasi-simultaneous satellite data, in particular data from the Hyperion, HICO, OLI Landsat-8, ETM Landsat-7 and TM Landsat-5 instruments. Results of analysis attempts combining data from different sensors are discussed. Their strong and weak points are highlighted. The study was completed with partial financial support from The Russian Foundation for Basic Research grants # 14-05-00520-a and 13-07-12017.

  4. Cocoa bean quality assessment by using hyperspectral images and fuzzy logic techniques

    Science.gov (United States)

    Soto, Juan; Granda, Guillermo; Prieto, Flavio; Ipanaque, William; Machacuay, Jorge

    2015-04-01

    Nowadays, cocoa bean exportation from Piura-Peru is having a positive international market response due to their inherent high quality. Nevertheless, when using subjective techniques for quality assessment, such as the cut test, a wastefulness of grains is generated, additional to a restriction in the selection as well as improvement approaches in earlier stages for optimizing the quality. Thus, in an attempt to standardize the internal features analyzed by the cut test, for instance, crack formation and internal color changes during the fermentation, this research is submitted as an approach which aims to make use of hyperspectral images, with the purpose of having a quick and accurate analysis. Hyperspectral cube size was reduced by using Principal Component Analysis (PCA). The image generated by principal component PC1 provides enough information to clearly distinguish the internal cracks of the cocoa bean, since the zones where these cracks are, have a negative correlation with PC1. The features taken were processed through a fuzzy block, which is able to describe the cocoa bean quality. Three membership functions were defined in the output: unfermented, partly fermented and well fermented, by using trapezoidal-shaped and triangular-shaped functions. A total of twelve rules were propounded. Furthermore, the bisector method was chosen for the defuzzification. Begin the abstract two lines below author names and addresses.

  5. Target oriented dimensionality reduction of hyperspectral data by Kernel Fukunaga-Koontz Transform

    Science.gov (United States)

    Binol, Hamidullah; Ochilov, Shuhrat; Alam, Mohammad S.; Bal, Abdullah

    2017-02-01

    Principal component analysis (PCA) is a popular technique in remote sensing for dimensionality reduction. While PCA is suitable for data compression, it is not necessarily an optimal technique for feature extraction, particularly when the features are exploited in supervised learning applications (Cheriyadat and Bruce, 2003) [1]. Preserving features belonging to the target is very crucial to the performance of target detection/recognition techniques. Fukunaga-Koontz Transform (FKT) based supervised band reduction technique can be used to provide this requirement. FKT achieves feature selection by transforming into a new space in where feature classes have complimentary eigenvectors. Analysis of these eigenvectors under two classes, target and background clutter, can be utilized for target oriented band reduction since each basis functions best represent target class while carrying least information of the background class. By selecting few eigenvectors which are the most relevant to the target class, dimension of hyperspectral data can be reduced and thus, it presents significant advantages for near real time target detection applications. The nonlinear properties of the data can be extracted by kernel approach which provides better target features. Thus, we propose constructing kernel FKT (KFKT) to present target oriented band reduction. The performance of the proposed KFKT based target oriented dimensionality reduction algorithm has been tested employing two real-world hyperspectral data and results have been reported consequently.

  6. Ship Detection Based on Multiple Features in Random Forest Model for Hyperspectral Images

    Science.gov (United States)

    Li, N.; Ding, L.; Zhao, H.; Shi, J.; Wang, D.; Gong, X.

    2018-04-01

    A novel method for detecting ships which aim to make full use of both the spatial and spectral information from hyperspectral images is proposed. Firstly, the band which is high signal-noise ratio in the range of near infrared or short-wave infrared spectrum, is used to segment land and sea on Otsu threshold segmentation method. Secondly, multiple features that include spectral and texture features are extracted from hyperspectral images. Principal components analysis (PCA) is used to extract spectral features, the Grey Level Co-occurrence Matrix (GLCM) is used to extract texture features. Finally, Random Forest (RF) model is introduced to detect ships based on the extracted features. To illustrate the effectiveness of the method, we carry out experiments over the EO-1 data by comparing single feature and different multiple features. Compared with the traditional single feature method and Support Vector Machine (SVM) model, the proposed method can stably achieve the target detection of ships under complex background and can effectively improve the detection accuracy of ships.

  7. Adaptive and automatic red blood cell counting method based on microscopic hyperspectral imaging technology

    Science.gov (United States)

    Liu, Xi; Zhou, Mei; Qiu, Song; Sun, Li; Liu, Hongying; Li, Qingli; Wang, Yiting

    2017-12-01

    Red blood cell counting, as a routine examination, plays an important role in medical diagnoses. Although automated hematology analyzers are widely used, manual microscopic examination by a hematologist or pathologist is still unavoidable, which is time-consuming and error-prone. This paper proposes a full-automatic red blood cell counting method which is based on microscopic hyperspectral imaging of blood smears and combines spatial and spectral information to achieve high precision. The acquired hyperspectral image data of the blood smear in the visible and near-infrared spectral range are firstly preprocessed, and then a quadratic blind linear unmixing algorithm is used to get endmember abundance images. Based on mathematical morphological operation and an adaptive Otsu’s method, a binaryzation process is performed on the abundance images. Finally, the connected component labeling algorithm with magnification-based parameter setting is applied to automatically select the binary images of red blood cell cytoplasm. Experimental results show that the proposed method can perform well and has potential for clinical applications.

  8. 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.

  9. Hardware Implementation of Lossless Adaptive and Scalable Hyperspectral Data Compression for Space

    Science.gov (United States)

    Aranki, Nazeeh; Keymeulen, Didier; Bakhshi, Alireza; Klimesh, Matthew

    2009-01-01

    On-board lossless hyperspectral data compression reduces data volume in order to meet NASA and DoD limited downlink capabilities. The technique also improves signature extraction, object recognition and feature classification capabilities by providing exact reconstructed data on constrained downlink resources. At JPL a novel, adaptive and predictive technique for lossless compression of hyperspectral data was recently developed. This technique uses an adaptive filtering method and achieves a combination of low complexity and compression effectiveness that far exceeds state-of-the-art techniques currently in use. The JPL-developed 'Fast Lossless' algorithm requires no training data or other specific information about the nature of the spectral bands for a fixed instrument dynamic range. It is of low computational complexity and thus well-suited for implementation in hardware. A modified form of the algorithm that is better suited for data from pushbroom instruments is generally appropriate for flight implementation. A scalable field programmable gate array (FPGA) hardware implementation was developed. The FPGA implementation achieves a throughput performance of 58 Msamples/sec, which can be increased to over 100 Msamples/sec in a parallel implementation that uses twice the hardware resources This paper describes the hardware implementation of the 'Modified Fast Lossless' compression algorithm on an FPGA. The FPGA implementation targets the current state-of-the-art FPGAs (Xilinx Virtex IV and V families) and compresses one sample every clock cycle to provide a fast and practical real-time solution for space applications.

  10. An interactive tool for semi-automatic feature extraction of hyperspectral data

    Science.gov (United States)

    Kovács, Zoltán; Szabó, Szilárd

    2016-09-01

    The spectral reflectance of the surface provides valuable information about the environment, which can be used to identify objects (e.g. land cover classification) or to estimate quantities of substances (e.g. biomass). We aimed to develop an MS Excel add-in - Hyperspectral Data Analyst (HypDA) - for a multipurpose quantitative analysis of spectral data in VBA programming language. HypDA was designed to calculate spectral indices from spectral data with user defined formulas (in all possible combinations involving a maximum of 4 bands) and to find the best correlations between the quantitative attribute data of the same object. Different types of regression models reveal the relationships, and the best results are saved in a worksheet. Qualitative variables can also be involved in the analysis carried out with separability and hypothesis testing; i.e. to find the wavelengths responsible for separating data into predefined groups. HypDA can be used both with hyperspectral imagery and spectrometer measurements. This bivariate approach requires significantly fewer observations than popular multivariate methods; it can therefore be applied to a wide range of research areas.

  11. Collection and corrections of oblique multiangle hyperspectral bidirectional reflectance imagery of the water surface

    Science.gov (United States)

    Bostater, Charles R.; Oney, Taylor S.

    2017-10-01

    Hyperspectral images of coastal waters in urbanized regions were collected from fixed platform locations. Surf zone imagery, images of shallow bays, lagoons and coastal waters are processed to produce bidirectional reflectance factor (BRF) signatures corrected for changing viewing angles. Angular changes as a function of pixel location within a scene are used to estimate changes in pixel size and ground sampling areas. Diffuse calibration targets collected simultaneously from within the image scene provides the necessary information for calculating BRF signatures of the water surface and shorelines. Automated scanning using a pushbroom hyperspectral sensor allows imagery to be collected on the order of one minute or less for different regions of interest. Imagery is then rectified and georeferenced using ground control points within nadir viewing multispectral imagery via image to image registration techniques. This paper demonstrates the above as well as presenting how spectra can be extracted along different directions in the imagery. The extraction of BRF spectra along track lines allows the application of derivative reflectance spectroscopy for estimating chlorophyll-a, dissolved organic matter and suspended matter concentrations at or near the water surface. Imagery is presented demonstrating the techniques to identify subsurface features and targets within the littoral and surf zones.

  12. Unsupervised domain adaptation for early detection of drought stress in hyperspectral images

    Science.gov (United States)

    Schmitter, P.; Steinrücken, J.; Römer, C.; Ballvora, A.; Léon, J.; Rascher, U.; Plümer, L.

    2017-09-01

    Hyperspectral images can be used to uncover physiological processes in plants if interpreted properly. Machine Learning methods such as Support Vector Machines (SVM) and Random Forests have been applied to estimate development of biomass and detect and predict plant diseases and drought stress. One basic requirement of machine learning implies, that training and testing is done in the same domain and the same distribution. Different genotypes, environmental conditions, illumination and sensors violate this requirement in most practical circumstances. Here, we present an approach, which enables the detection of physiological processes by transferring the prior knowledge within an existing model into a related target domain, where no label information is available. We propose a two-step transformation of the target features, which enables a direct application of an existing model. The transformation is evaluated by an objective function including additional prior knowledge about classification and physiological processes in plants. We have applied the approach to three sets of hyperspectral images, which were acquired with different plant species in different environments observed with different sensors. It is shown, that a classification model, derived on one of the sets, delivers satisfying classification results on the transformed features of the other data sets. Furthermore, in all cases early non-invasive detection of drought stress was possible.

  13. Improved Scanners for Microscopic Hyperspectral Imaging

    Science.gov (United States)

    Mao, Chengye

    2009-01-01

    Improved scanners to be incorporated into hyperspectral microscope-based imaging systems have been invented. Heretofore, in microscopic imaging, including spectral imaging, it has been customary to either move the specimen relative to the optical assembly that includes the microscope or else move the entire assembly relative to the specimen. It becomes extremely difficult to control such scanning when submicron translation increments are required, because the high magnification of the microscope enlarges all movements in the specimen image on the focal plane. To overcome this difficulty, in a system based on this invention, no attempt would be made to move either the specimen or the optical assembly. Instead, an objective lens would be moved within the assembly so as to cause translation of the image at the focal plane: the effect would be equivalent to scanning in the focal plane. The upper part of the figure depicts a generic proposed microscope-based hyperspectral imaging system incorporating the invention. The optical assembly of this system would include an objective lens (normally, a microscope objective lens) and a charge-coupled-device (CCD) camera. The objective lens would be mounted on a servomotor-driven translation stage, which would be capable of moving the lens in precisely controlled increments, relative to the camera, parallel to the focal-plane scan axis. The output of the CCD camera would be digitized and fed to a frame grabber in a computer. The computer would store the frame-grabber output for subsequent viewing and/or processing of images. The computer would contain a position-control interface board, through which it would control the servomotor. There are several versions of the invention. An essential feature common to all versions is that the stationary optical subassembly containing the camera would also contain a spatial window, at the focal plane of the objective lens, that would pass only a selected portion of the image. In one version

  14. Hyperspectral remote sensing of wild oyster reefs

    Science.gov (United States)

    Le Bris, Anthony; Rosa, Philippe; Lerouxel, Astrid; Cognie, Bruno; Gernez, Pierre; Launeau, Patrick; Robin, Marc; Barillé, Laurent

    2016-04-01

    The invasion of the wild oyster Crassostrea gigas along the western European Atlantic coast has generated changes in the structure and functioning of intertidal ecosystems. Considered as an invasive species and a trophic competitor of the cultivated conspecific oyster, it is now seen as a resource by oyster farmers following recurrent mass summer mortalities of oyster spat since 2008. Spatial distribution maps of wild oyster reefs are required by local authorities to help define management strategies. In this work, visible-near infrared (VNIR) hyperspectral and multispectral remote sensing was investigated to map two contrasted intertidal reef structures: clusters of vertical oysters building three-dimensional dense reefs in muddy areas and oysters growing horizontally creating large flat reefs in rocky areas. A spectral library, collected in situ for various conditions with an ASD spectroradiometer, was used to run Spectral Angle Mapper classifications on airborne data obtained with an HySpex sensor (160 spectral bands) and SPOT satellite HRG multispectral data (3 spectral bands). With HySpex spectral/spatial resolution, horizontal oysters in the rocky area were correctly classified but the detection was less efficient for vertical oysters in muddy areas. Poor results were obtained with the multispectral image and from spatially or spectrally degraded HySpex data, it was clear that the spectral resolution was more important than the spatial resolution. In fact, there was a systematic mud deposition on shells of vertical oyster reefs explaining the misclassification of 30% of pixels recognized as mud or microphytobenthos. Spatial distribution maps of oyster reefs were coupled with in situ biomass measurements to illustrate the interest of a remote sensing product to provide stock estimations of wild oyster reefs to be exploited by oyster producers. This work highlights the interest of developing remote sensing techniques for aquaculture applications in coastal

  15. ALGORITMA ESTIMASI KANDUNGAN KLOROFIL TANAMAN PADI DENGAN DATA AIRBORNE HYPERSPECTRAL

    Directory of Open Access Journals (Sweden)

    Abdi Sukmono

    2015-02-01

    Full Text Available Klorofil merupakan pigmen yang paling penting dalam proses fotosintesis. Tanaman sehat yang mampu tumbuh maksimum umumnya  memiliki jumlah klorofil yang lebih besar daripada tanaman yang tidak sehat. Dalam Estimasi kandungan klorofil tanaman padi dengan airborne hyperspectral dibutuhkan algoritma khusus untuk mendaaptkan akurasi yang baik. Objek dari penelitian ini mengembangkan reflektan in situ menjadi model algoritma   estimasi kandungan klorofil tanaman padi untuk airborne hyperspectral.  Dalam penelitian ini beberapa indeks vegetasi seperti normalized difference vegetation index (NDVI, modified simple ratio (MSR  , modified/transformed chlorophyll absorption ratio index (MCARI, TCARI dan bentuk integrasi (MCARI/OSAVI and TCARI/OSAVI digunakan untuk membentuk model estimasi dengan metode regresi linear. Selain itu juga digunakan  Blue/Green/Yellow/Red Edge Absorption Clhorophyll Index. Dari proses regresi di dapatkan tiga ground model yang mempunyai korelasi kuat (R2≥0.5 terhadap klorofil tanaman padi. Ketiga model tersebut yaitu MSR (705,750 dengan R2 sebesar 0.51, TCARI/OSAVI (705, 750 dengan R2 sebesar 0.52 dan REACL 2 dengan R2 sebesar 0.57. Dari ketiga tersebut dipilih groun model terbaik REACL 2 untuk di upscalling ke model algoritma airborne hyperspectral.  Pembentukan algoritma dengan data airborne hyperspectral sensor Hymap dan REACL 2 menghasilkan model algoritma ( Klorofil (SPAD unit = 3.031((B22-B18/(B18-B13 + 31.596 dengan R2 sebesar 0.78

  16. Characterisation of a Tunisian coastal lagoon through hyperspectral ...

    African Journals Online (AJOL)

    In 2008 an optical procedure was developed and applied in Ghar El Melh, a Tunisian lagoon which has been increasingly impacted by pollutant loading, especially from agriculture. In situ hyperspectral irradiance was measured at several stations, from which the apparent optical properties (AOPs), namely the irradiance ...

  17. The challenges of analysing blood stains with hyperspectral imaging

    Science.gov (United States)

    Kuula, J.; Puupponen, H.-H.; Rinta, H.; Pölönen, I.

    2014-06-01

    Hyperspectral imaging is a potential noninvasive technology for detecting, separating and identifying various substances. In the forensic and military medicine and other CBRNE related use it could be a potential method for analyzing blood and for scanning other human based fluids. For example, it would be valuable to easily detect whether some traces of blood are from one or more persons or if there are some irrelevant substances or anomalies in the blood. This article represents an experiment of separating four persons' blood stains on a white cotton fabric with a SWIR hyperspectral camera and FT-NIR spectrometer. Each tested sample includes standardized 75 _l of 100 % blood. The results suggest that on the basis of the amount of erythrocytes in the blood, different people's blood might be separable by hyperspectral analysis. And, referring to the indication given by erythrocytes, there might be a possibility to find some other traces in the blood as well. However, these assumptions need to be verified with wider tests, as the number of samples in the study was small. According to the study there also seems to be several biological, chemical and physical factors which affect alone and together on the hyperspectral analyzing results of blood on fabric textures, and these factors need to be considered before making any further conclusions on the analysis of blood on various materials.

  18. Hyperspectral remote sensing of canopy biodiversity in Hawaiian lowland rainforests

    Science.gov (United States)

    Kimberly M. Carlson; Gregory P. Asner; R. Flint Hughes; Rebecca Ostertag; Roberta E. Martin

    2007-01-01

    Mapping biological diversity is a high priority for conservation research, management and policy development, but few studies have provided diversity data at high spatial resolution from remote sensing. We used airborne imaging spectroscopy to map woody vascular plant species richness in lowland tropical forest ecosystems in Hawaii. Hyperspectral signatures spanning...

  19. Bathymetry from fusion of airborne hyperspectral and laser data

    Science.gov (United States)

    Kappus, Mary E.; Davis, Curtiss O.; Rhea, W. Joseph

    1998-10-01

    Airborne hyperspectral and nadir-viewing laser data can be combined to ascertain shallow water bathymetry. The combination emphasizes the advances and overcomes the disadvantages of each method used alone. For laser systems, both the hardware and software for obtaining off-nadir measurement are complicated and expensive, while for the nadir view the conversion of laser pulse travel time to depth is straightforward. The hyperspectral systems can easily collect data in a full swath, but interpretation for water depth requires careful calibration and correction for transmittance through the atmosphere and water. Relative depths are apparent in displays of several subsets of hyperspectral data, for example, single blue-green wavelengths, endmembers that represent the pure water component of the data, or ratios of deep to shallow water endmembers. A relationship between one of these values and the depth measured by the aligned nadir laser can be determined, and then applied to the rest of the swath to obtain depth in physical units for the entire area covered. We demonstrate this technique using bathymetric charts as a proxy for laser data, and hyperspectral data taken by AVIRIS over Lake Tahoe and Key West.

  20. Identifying saltcedar with hyperspectral data and support vector machines

    Science.gov (United States)

    Saltcedar (Tamarix spp.) are a group of dense phreatophytic shrubs and trees that are invasive to riparian areas throughout the United States. This study determined the feasibility of using hyperspectral data and a support vector machine (SVM) classifier to discriminate saltcedar from other cover t...

  1. Classification of Salmonella serotypes with hyperspectral microscope imagery

    Science.gov (United States)

    Previous research has demonstrated an optical method with acousto-optic tunable filter (AOTF) based hyperspectral microscope imaging (HMI) had potential for classifying gram-negative from gram-positive foodborne pathogenic bacteria rapidly and nondestructively with a minimum sample preparation. In t...

  2. Hyperspectral Imaging of Forest Resources: The Malaysian Experience

    Science.gov (United States)

    Mohd Hasmadi, I.; Kamaruzaman, J.

    2008-08-01

    Remote sensing using satellite and aircraft images are well established technology. Remote sensing application of hyperspectral imaging, however, is relatively new to Malaysian forestry. Through a wide range of wavelengths hyperspectral data are precisely capable to capture narrow bands of spectra. Airborne sensors typically offer greatly enhanced spatial and spectral resolution over their satellite counterparts, and able to control experimental design closely during image acquisition. The first study using hyperspectral imaging for forest inventory in Malaysia were conducted by Professor Hj. Kamaruzaman from the Faculty of Forestry, Universiti Putra Malaysia in 2002 using the AISA sensor manufactured by Specim Ltd, Finland. The main objective has been to develop methods that are directly suited for practical tropical forestry application at the high level of accuracy. Forest inventory and tree classification including development of single spectral signatures have been the most important interest at the current practices. Experiences from the studies showed that retrieval of timber volume and tree discrimination using this system is well and some or rather is better than other remote sensing methods. This article reviews the research and application of airborne hyperspectral remote sensing for forest survey and assessment in Malaysia.

  3. Remote sensing of soil moisture using airborne hyperspectral data

    Science.gov (United States)

    The Institute for Technology Development (ITD) has developed an airborne hyperspectral sensor system that collects electromagnetic reflectance data of the terrain. The system consists of sensors for three different sections of the electromagnetic spectrum; the Ultra-Violet (UV), Visible/Near Infrare...

  4. Use of Aerial Hyperspectral Imaging For Monitoring Forest Health

    Science.gov (United States)

    Milton O. Smith; Nolan J. Hess; Stephen Gulick; Lori G. Eckhardt; Roger D. Menard

    2004-01-01

    This project evaluates the effectiveness of aerial hyperspectral digital imagery in the assessment of forest health of loblolly stands in central Alabama. The imagery covers 50 square miles, in Bibb and Hale Counties, south of Tuscaloosa, AL, which includes intensive managed forest industry sites and National Forest lands with multiple use objectives. Loblolly stands...

  5. Infrared hyperspectral upconversion imaging using spatial object translation

    DEFF Research Database (Denmark)

    Kehlet, Louis Martinus; Sanders, Nicolai Højer; Tidemand-Lichtenberg, Peter

    2015-01-01

    In this paper hyperspectral imaging in the mid-infrared wavelength region is realised using nonlinear frequency upconversion. The infrared light is converted to the near-infrared region for detection with a Si-based CCD camera. The object is translated in a predefined grid by motorized actuators...

  6. Manifold regularization for sparse unmixing of hyperspectral images.

    Science.gov (United States)

    Liu, Junmin; Zhang, Chunxia; Zhang, Jiangshe; Li, Huirong; Gao, Yuelin

    2016-01-01

    Recently, sparse unmixing has been successfully applied to spectral mixture analysis of remotely sensed hyperspectral images. Based on the assumption that the observed image signatures can be expressed in the form of linear combinations of a number of pure spectral signatures known in advance, unmixing of each mixed pixel in the scene is to find an optimal subset of signatures in a very large spectral library, which is cast into the framework of sparse regression. However, traditional sparse regression models, such as collaborative sparse regression , ignore the intrinsic geometric structure in the hyperspectral data. In this paper, we propose a novel model, called manifold regularized collaborative sparse regression , by introducing a manifold regularization to the collaborative sparse regression model. The manifold regularization utilizes a graph Laplacian to incorporate the locally geometrical structure of the hyperspectral data. An algorithm based on alternating direction method of multipliers has been developed for the manifold regularized collaborative sparse regression model. Experimental results on both the simulated and real hyperspectral data sets have demonstrated the effectiveness of our proposed model.

  7. Testing a potential alternative to traditional identification procedures: Reaction time-based concealed information test does not work for lineups with cooperative witnesses.

    Science.gov (United States)

    Sauerland, Melanie; Wolfs, Andrea C F; Crans, Samantha; Verschuere, Bruno

    2017-11-27

    Direct eyewitness identification is widely used, but prone to error. We tested the validity of indirect eyewitness identification decisions using the reaction time-based concealed information test (CIT) for assessing cooperative eyewitnesses' face memory as an alternative to traditional lineup procedures. In a series of five experiments, a total of 401 mock eyewitnesses watched one of 11 different stimulus events that depicted a breach of law. Eyewitness identifications in the CIT were derived from longer reaction times as compared to well-matched foil faces not encountered before. Across the five experiments, the weighted mean effect size d was 0.14 (95% CI 0.08-0.19). The reaction time-based CIT seems unsuited for testing cooperative eyewitnesses' memory for faces. The careful matching of the faces required for a fair lineup or the lack of intent to deceive may have hampered the diagnosticity of the reaction time-based CIT.

  8. Tunable thin-film optical filters for hyperspectral microscopy

    Science.gov (United States)

    Favreau, Peter F.; Rich, Thomas C.; Prabhat, Prashant; Leavesley, Silas J.

    2013-02-01

    Hyperspectral imaging was originally developed for use in remote sensing applications. More recently, it has been applied to biological imaging systems, such as fluorescence microscopes. The ability to distinguish molecules based on spectral differences has been especially advantageous for identifying fluorophores in highly autofluorescent tissues. A key component of hyperspectral imaging systems is wavelength filtering. Each filtering technology used for hyperspectral imaging has corresponding advantages and disadvantages. Recently, a new optical filtering technology has been developed that uses multi-layered thin-film optical filters that can be rotated, with respect to incident light, to control the center wavelength of the pass-band. Compared to the majority of tunable filter technologies, these filters have superior optical performance including greater than 90% transmission, steep spectral edges and high out-of-band blocking. Hence, tunable thin-film optical filters present optical characteristics that may make them well-suited for many biological spectral imaging applications. An array of tunable thin-film filters was implemented on an inverted fluorescence microscope (TE 2000, Nikon Instruments) to cover the full visible wavelength range. Images of a previously published model, GFP-expressing endothelial cells in the lung, were acquired using a charge-coupled device camera (Rolera EM-C2, Q-Imaging). This model sample presents fluorescently-labeled cells in a highly autofluorescent environment. Linear unmixing of hyperspectral images indicates that thin-film tunable filters provide equivalent spectral discrimination to our previous acousto-optic tunable filter-based approach, with increased signal-to-noise characteristics. Hence, tunable multi-layered thin film optical filters may provide greatly improved spectral filtering characteristics and therefore enable wider acceptance of hyperspectral widefield microscopy.

  9. Colorectal cancer detection by hyperspectral imaging using fluorescence excitation scanning

    Science.gov (United States)

    Leavesley, Silas J.; Deal, Joshua; Hill, Shante; Martin, Will A.; Lall, Malvika; Lopez, Carmen; Rider, Paul F.; Rich, Thomas C.; Boudreaux, Carole W.

    2018-02-01

    Hyperspectral imaging technologies have shown great promise for biomedical applications. These techniques have been especially useful for detection of molecular events and characterization of cell, tissue, and biomaterial composition. Unfortunately, hyperspectral imaging technologies have been slow to translate to clinical devices - likely due to increased cost and complexity of the technology as well as long acquisition times often required to sample a spectral image. We have demonstrated that hyperspectral imaging approaches which scan the fluorescence excitation spectrum can provide increased signal strength and faster imaging, compared to traditional emission-scanning approaches. We have also demonstrated that excitation-scanning approaches may be able to detect spectral differences between colonic adenomas and adenocarcinomas and normal mucosa in flash-frozen tissues. Here, we report feasibility results from using excitation-scanning hyperspectral imaging to screen pairs of fresh tumoral and nontumoral colorectal tissues. Tissues were imaged using a novel hyperspectral imaging fluorescence excitation scanning microscope, sampling a wavelength range of 360-550 nm, at 5 nm increments. Image data were corrected to achieve a NIST-traceable flat spectral response. Image data were then analyzed using a range of supervised and unsupervised classification approaches within ENVI software (Harris Geospatial Solutions). Supervised classification resulted in >99% accuracy for single-patient image data, but only 64% accuracy for multi-patient classification (n=9 to date), with the drop in accuracy due to increased false-positive detection rates. Hence, initial data indicate that this approach may be a viable detection approach, but that larger patient sample sizes need to be evaluated and the effects of inter-patient variability studied.

  10. Assessing the biophysical naturalness of grassland in eastern North Dakota with hyperspectral imagery

    Science.gov (United States)

    Zhou, Qiang

    Over the past two decades, non-native species within grassland communities have quickly developed due to human migration and commerce. Invasive species like Smooth Brome grass (Bromus inermis) and Kentucky Blue Grass (Poa pratensis), seriously threaten conservation of native grasslands. This study aims to discriminate between native grasslands and planted hayfields and conservation areas dominated by introduced grasses using hyperspectral imagery. Hyperspectral imageries from the Hyperion sensor on EO-1 were acquired in late spring and late summer on 2009 and 2010. Field spectra for widely distributed species as well as smooth brome grass and Kentucky blue grass were collected from the study sites throughout the growing season. Imagery was processed with an unmixing algorithm to estimate fractional cover of green and dry vegetation and bare soil. As the spectrum is significantly different through growing season, spectral libraries for the most common species are then built for both the early growing season and late growing season. After testing multiple methods, the Adaptive Coherence Estimator (ACE) was used for spectral matching analysis between the imagery and spectral libraries. Due in part to spectral similarity among key species, the results of spectral matching analysis were not definitive. Additional indexes, "Level of Dominance" and "Band variance", were calculated to measure the predominance of spectral signatures in any area. A Texture co-occurrence analysis was also performed on both "Level of Dominance" and "Band variance" indexes to extract spatial characteristics. The results suggest that compared with disturbed area, native prairie tend to have generally lower "Level of Dominance" and "Band variance" as well as lower spatial dissimilarity. A final decision tree model was created to predict presence of native or introduced grassland. The model was more effective for identification of Mixed Native Grassland than for grassland dominated by a single

  11. Spatial Field Variability Mapping of Rice Crop using Clustering Technique from Space Borne Hyperspectral Data

    Science.gov (United States)

    Moharana, S.; Dutta, S.

    2015-12-01

    Precision farming refers to field-specific management of an agricultural crop at a spatial scale with an aim to get the highest achievable yield and to achieve this spatial information on field variability is essential. The difficulty in mapping of spatial variability occurring within an agriculture field can be revealed by employing spectral techniques in hyperspectral imagery rather than multispectral imagery. However an advanced algorithm needs to be developed to fully make use of the rich information content in hyperspectral data. In the present study, potential of hyperspectral data acquired from space platform was examined to map the field variation of paddy crop and its species discrimination. This high dimensional data comprising 242 spectral narrow bands with 30m ground resolution Hyperion L1R product acquired for Assam, India (30th Sept and 3rd Oct, 2014) were allowed for necessary pre-processing steps followed by geometric correction using Hyperion L1GST product. Finally an atmospherically corrected and spatially deduced image consisting of 112 band was obtained. By employing an advanced clustering algorithm, 12 different clusters of spectral waveforms of the crop were generated from six paddy fields for each images. The findings showed that, some clusters were well discriminated representing specific rice genotypes and some clusters were mixed treating as a single rice genotype. As vegetation index (VI) is the best indicator of vegetation mapping, three ratio based VI maps were also generated and unsupervised classification was performed for it. The so obtained 12 clusters of paddy crop were mapped spatially to the derived VI maps. From these findings, the existence of heterogeneity was clearly captured in one of the 6 rice plots (rice plot no. 1) while heterogeneity was observed in rest of the 5 rice plots. The degree of heterogeneous was found more in rice plot no.6 as compared to other plots. Subsequently, spatial variability of paddy field was

  12. Real-time recursive hyperspectral sample and band processing algorithm architecture and implementation

    CERN Document Server

    Chang, Chein-I

    2017-01-01

    This book explores recursive architectures in designing progressive hyperspectral imaging algorithms. In particular, it makes progressive imaging algorithms recursive by introducing the concept of Kalman filtering in algorithm design so that hyperspectral imagery can be processed not only progressively sample by sample or band by band but also recursively via recursive equations. This book can be considered a companion book of author’s books, Real-Time Progressive Hyperspectral Image Processing, published by Springer in 2016. Explores recursive structures in algorithm architecture Implements algorithmic recursive architecture in conjunction with progressive sample and band processing Derives Recursive Hyperspectral Sample Processing (RHSP) techniques according to Band-Interleaved Sample/Pixel (BIS/BIP) acquisition format Develops Recursive Hyperspectral Band Processing (RHBP) techniques according to Band SeQuential (BSQ) acquisition format for hyperspectral data.

  13. Spatial calibration and image processing requirements of an image fiber bundle based snapshot hyperspectral imaging probe: from raw data to datacube

    Science.gov (United States)

    Lim, Hoong-Ta; Murukeshan, Vadakke Matham

    2017-06-01

    Hyperspectral imaging was first used in remote sensing and since then, it has been used in many other applications such as cancer diagnosis, precision farming and assessment of the level of flaking in ancient murals. In order to make hyperspectral imaging available for a wide variety of applications, its imagers can be made to operate using different methods and developed into different configurations. This leads to each variant having a set of specifications suitable for certain applications. The many variants of hyperspectral imager produce a set of three-dimensional spatial-spatialspectral datacube, which is made up of hundreds of spectral images of one scene. A snapshot hyperspectral imaging probe has recently been developed by integrating a fiber bundle, which is made up of specially-arranged optical fibers, with a spectrograph-based hyperspectral imager. The snapshot method is able to produce a datacube using the information from each scan. The fiber bundle has 100 fiberlets which are arranged in a row in the one-dimensional proximal end, and are rearranged into a 10×10 hexagonal array in the two-dimensional distal end. The image captured by the two-dimensional end of the fiber bundle is reduced from two to one spatial dimension at the one-dimensional end. The raw data acquired from each scan has to be remapped into a datacube with the correct representation of the spectral and spatial features of the captured scene. This paper reports the spatial calibrations of both ends of the fiber bundle and image processing that have to be performed for such a remapping.

  14. High-emulation mask recognition with high-resolution hyperspectral video capture system

    Science.gov (United States)

    Feng, Jiao; Fang, Xiaojing; Li, Shoufeng; Wang, Yongjin

    2014-11-01

    We present a method for distinguishing human face from high-emulation mask, which is increasingly used by criminals for activities such as stealing card numbers and passwords on ATM. Traditional facial recognition technique is difficult to detect such camouflaged criminals. In this paper, we use the high-resolution hyperspectral video capture system to detect high-emulation mask. A RGB camera is used for traditional facial recognition. A prism and a gray scale camera are used to capture spectral information of the observed face. Experiments show that mask made of silica gel has different spectral reflectance compared with the human skin. As multispectral image offers additional spectral information about physical characteristics, high-emulation mask can be easily recognized.

  15. Impact of patient questionnaires on completeness of clinical information and identification of causes of pain during outpatient abdominopelvic CT interpretation.

    Science.gov (United States)

    Doshi, Ankur M; Huang, Chenchan; Ginocchio, Luke; Shanbhogue, Krishna; Rosenkrantz, Andrew B

    2017-12-01

    To evaluate the impact of questionnaires completed by patients at the time of abdominopelvic CT performed for abdominal pain on the completeness of clinical information and the identification of potential causes of pain, compared with order requisitions alone. 100 outpatient CT examinations performed for the evaluation of abdominal pain were retrospectively reviewed. The specificity of the location of pain was compared between the order requisition and patient questionnaire. An abdominal imaging fellow (Reader 1) and abdominal radiologist (Reader 2) reviewed the examinations independently in two sessions 6 weeks apart (one with only the order requisition and one also with the questionnaire). Readers recorded identified causes of pain and rated their confidence in interpretation (1-5 scale; least to greatest confidence). In 30% of patients, the questionnaire provided a more specific location for pain. Among these, the pain was localized to a specific quadrant in 40%. With having access to the questionnaire, both readers identified additional causes for pain not identified in session 1 (Reader 1, 8.6% [7/81]; Reader 2 5.3% [4/75]). Additional identified causes of pain included diverticulitis, cystitis, peritoneal implants, epiploic appendagitis, osseous metastatic disease, umbilical hernia, gastritis, and SMA syndrome. Confidence in interpretation was significantly greater using the questionnaire for both readers (Reader 1: 4.8 ± 0.6 vs. 4.0 ± 0.5; Reader 2: 4.9 ± 0.3 vs. 4.7 ± 0.5, p questionnaires provide additional relevant clinical history, increased diagnostic yield, and improve radiologists' confidence. Radiology practices are encouraged to implement questionnaires and make these readily available to radiologists at the time of interpretation.

  16. A stakeholder-informed approach to the identification of criteria for the prioritization of zoonoses in Canada.

    Directory of Open Access Journals (Sweden)

    Victoria Ng

    Full Text Available Zoonotic diseases account for over 60% of all communicable diseases causing illness in humans and 75% of recently emerging infectious diseases. As limited resources are available for the control and prevention of zoonotic diseases, it is necessary to prioritize diseases in order to direct resources into those with the greatest needs. The selection of criteria for prioritization has traditionally been on the basis of expert opinion; however, details of the methods used to identify criteria from expert opinion often are not published and a full range of criteria may not be captured by expert opinion.This study used six focus groups to identify criteria for the prioritization of zoonotic diseases in Canada. Focus groups included people from the public, animal health professionals and human health professionals. A total of 59 criteria were identified for prioritizing zoonotic diseases. Human-related criteria accounted for the highest proportion of criteria identified (55%, followed by animal-related criteria (26% then pathogen/disease-related criteria (19%. Similarities and differences were observed in the identification and scoring of criteria for disease prioritization between groups; the public groups were strongly influenced by the individual-level of disease burden, the responsibility of the scientific community in disease prioritization and the experiences of recent events while the professional groups were influenced by the societal- and population-level of disease burden and political and public pressure.This was the first study to describe a mixed semi-quantitative and qualitative approach to deriving criteria for disease prioritization. This was also the first study to involve the opinion of the general public regarding disease prioritization. The number of criteria identified highlights the difficulty in prioritizing zoonotic diseases. The method presented in this paper has formulated a comprehensive list of criteria that can be used to

  17. AN EMPIRICAL STUDY FOR RADIO FREQUENCY IDENTIFICATION (RFID ADOPTION BY SMEs IN THE TAIWANESE INFORMATION TECHNOLOGY (IT INDUSTRY

    Directory of Open Access Journals (Sweden)

    Hsin Chen

    2012-07-01

    Full Text Available Radio Frequency Identification (RFID technology represents a common standard for data storage and retrieval that could improve collaboration and data sharing between non-competing organisations. With the advent of RFID, organisations have the opportunity to rethink how their organisation will operate and integrate in the supply chain. Especially for Small to Medium Sized Enterprises (SMEs, that they have limited resources adopting such an innovative technology (i.e. RFID the adoption decision can be daunting. Literature indicates that SMEs that decide to go on with implementation have so far only a few guidelines from either private companies or public authorities regarding awareness on specific opportunities and risks. This research is therefore trying to explore in detail the factors that affect SMEs' RFID adoption in the Taiwan Information Technology (IT manufacturing industry. We are employing Exploratory Factor Analysis (EFA techniques and utilising a questionnaire survey in order to collect and analyse our data. After classifying the responding SMEs into three different adopters categories named ready adopter, initiator adopter and unprepared adopter using EFA technique our results show that each category has some specific adoption factors related to their unique situation. These are for ready adopters: cost and management, for initiator adopters: competitiveness and process efficiency and unprepared adopters: IT management difficulties, IT implementation difficulties and cost of implementation. A SMEs RFID adoption model is then proposed. It is anticipated that the findings of this research will not only enhance the research in RFID adoption in SMEs, but can also act as a reference for practitioners in the industry and researchers in the academic field.

  18. Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context

    OpenAIRE

    Julie Transon; Raphaël d’Andrimont; Alexandre Maugnard; Pierre Defourny

    2018-01-01

    In the last few decades, researchers have developed a plethora of hyperspectral Earth Observation (EO) remote sensing techniques, analysis and applications. While hyperspectral exploratory sensors are demonstrating their potential, Sentinel-2 multispectral satellite remote sensing is now providing free, open, global and systematic high resolution visible and infrared imagery at a short revisit time. Its recent launch suggests potential synergies between multi- and hyper-spectral data. This st...

  19. 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.

  20. 36 CFR 1254.8 - What information do I need to provide when applying for a researcher identification card?

    Science.gov (United States)

    2010-07-01

    ... other identification is not current. Students who consider the home of their parents as their permanent address, but who do not live there during the academic session, must provide their current student address...