WorldWideScience

Sample records for hyperspectral information identification

  1. Overhead longwave infrared hyperspectral material identification using radiometric models

    Energy Technology Data Exchange (ETDEWEB)

    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

    Science.gov (United States)

    2015-10-06

    infrared hyperspectral remote sensing of chemical clouds: A focus on signal processing approaches,” IEEE Signal Process- ing Magazine 31(4), 120–141...Performance Metrics for the Evaluation of Hyperspectral Chemical Identification Systems Eric Truslowa, Steven Golowichb, Dimitris Manolakis b, and...Vinay Inglea aNortheastern University Department of Electrical and Computer Engineering 360 Huntington Avenue, Boston, MA 02115. bMIT Lincoln Laboratory

  3. Identification of unknown waste sites using MIVIS hyperspectral images

    Energy Technology Data Exchange (ETDEWEB)

    Gomarasca, M.A.; Strobelt, S. [National Research Council, Milano (Italy)

    1996-11-01

    This paper presents the results on the individuation of known and unknown (illegal) waste sites using Landsat TM satellite imagery and airborne MIVIS (Multispectral Infrared and Visible Imaging Spectrometer) data for detailed analysis in Italy. Previous results with Landsat TM imagery were partially positive for large waste site identification and negative for small sites. Information acquired by the MIVIS hyperspectral system presents three main characteristics: local scale study, possibility to plan the proper period based on the objectives of the study, high number of spectral bands with high spectral and geometrical resolution. MIVIS airborne shootings were carried out on 7 July 1994 at noon with 4x4 m pixel resolution. The MIVIS 102 bands` sensors can distinguish even objects with similar spectral behavior, thanks to its high spectral resolution. Identification of degraded sites is obtained using traditional spectral and statistical operators (NDVI, Principal Component Analysis, Maximum Likelihood classifier) and innovative combination of filtered band ratios realized to extract specific waste elements (acid slimes or contaminated soils). One of the aims that concerns with this study is the definition of an operative program for the characterization, identification and classification of defined categories of waste disposal sites. The best schedule for the data collection by airborne MIVIS oriented to this target is defined. The planning of the proper flight, based on the waste sites features, is important to optimize this technology. One of the most efficient methods for detecting hidden waste sites is the thermal inertia so two images are necessary: one during low sun load and one with high sun load. The results obtained are operationally useful and winning. This instrument, supported by correct analysis techniques, may offer new interesting prospects in territorial management and environmental monitoring. 5 refs., 5 figs., 1 tab.

  4. Identification of inflammation sites in arthritic joints using hyperspectral imaging

    Science.gov (United States)

    Paluchowski, Lukasz A.; Milanic, Matija; Bjorgan, Asgeir; Grandaunet, Berit; Dhainaut, Alvilde; Hoff, Mari; Randeberg, Lise L.

    2014-03-01

    Inflammatory arthritic diseases have prevalence between 2 and 3% and may lead to joint destruction and deformation resulting in a loss of function. Patient's quality of life is often severely affected as the disease attacks hands and finger joints. Pathology involved in arthritis includes angiogenesis, hyper-vascularization, hyper-metabolism and relative hypoxia. We have employed hyperspectral imaging to study the hemodynamics of affected- and non-affected joints and tissue. Two hyperspectral, push-broom cameras were used (VNIR-1600, SWIR-320i, Norsk Elektro Optikk AS, Norway). Optical spectra (400nm - 1700nm) of high spectral resolution were collected from 15 patients with visible symptoms of arthritic rheumatic diseases in at least one joint. The control group consisted of 10 healthy individuals. Concentrations of dominant chromophores were calculated based on analytical calculations of light transport in tissue. Image processing was used to analyze hyperspectral data and retrieve information, e.g. blood concentration and tissue oxygenation maps. The obtained results indicate that hyperspectral imaging can be used to quantify changes within affected joints and surrounding tissue. Further improvement of this method will have positive impact on diagnosis of arthritic joints at an early stage. Moreover it will enable development of fast, noninvasive and noncontact diagnostic tool of arthritic joints

  5. [Advances in researches on hyperspectral remote sensing forestry information-extracting technology].

    Science.gov (United States)

    Wu, Jian; Peng, Dao-Li

    2011-09-01

    The hyperspectral remote sensing technology has become one of the leading technologies in forestry remote sensing domain. In the present review paper, the advances in researches on hyperspectral remote sensing technology in forestry information extraction both at home and abroad were reviewed, and the five main research aspects including the hyperspectral classification and recognition of forest tree species, the hyperspectral inversion and extraction of forest ecological physical parameters, the hyperspectral monitoring and diagnosis of forest nutrient element, the forest crown density information extraction and the hyperspectral monitoring of forest disasters were summarized. The unresolved problems of hyperspectral technology in the forestry remote sensing applications were pointed out and the possible ways to solve these problems were expounded. Finally, the application prospect of hyperspectral remote sensing technology in forestry was analyzed.

  6. Pigment identification in pictorial layers by HyperSpectral Imaging

    Science.gov (United States)

    Capobianco, Giuseppe; Bonifazi, Giuseppe; Prestileo, Fernanda; Serranti, Silvia

    2014-05-01

    The use of Hyper-Spectral Imaging (HSI) as a diagnostic tool in the field of cultural heritage is of great interest presenting high potentialities. This analysis, in fact, is non-destructive, non-invasive and portable. Furthermore, the possibility to couple hyperspectral data with chemometric techniques allows getting qualitative and/or quantitative information on the nature and physical-chemical characteristics of the investigated materials. A study was carried out to explore the possibilities offered by this approach to identify pigments in paintings. More in detail, six pigments have been selected and they have been then mixed with four different binders and applied to a wood support. The resulting reference samples were acquired by HSI in the SWIR wavelength range (1000-2500 nm). Data were processed adopting a chemometric approach based on the PLS Toolbox (Eigenvector Research, Inc.) running inside Matlab® (The Mathworks, Inc.). The aim of the study was to verify, according to the information acquired in the investigated wavelength region, the correlation existing between collected spectral signatures and sample characteristics related to the different selected pigments and binders. Results were very good showing as correlations exist. New scenarios can thus be envisaged for analysis, characterization, conservation and restoration of paintings, considering that the developed approach allows to obtain, just "in one shot", information, not only on the type of pigment, but also on the utilized binder and support.

  7. The role of the continuous wavelet transform in mineral identification using hyperspectral imaging in the long-wave infrared by using SVM classifier

    Science.gov (United States)

    Sojasi, Saeed; Yousefi, Bardia; Liaigre, Kévin; Ibarra-Castanedo, Clemente; Beaudoin, Georges; Maldague, Xavier P. V.; Huot, François; Chamberland, Martin

    2017-05-01

    Hyperspectral imaging (HSI) in the long-wave infrared spectrum (LWIR) provides spectral and spatial information concerning the emissivity of the surface of materials, which can be used for mineral identification. For this, an endmember, which is the purest form of a mineral, is used as reference. All pure minerals have specific spectral profiles in the electromagnetic wavelength, which can be thought of as the mineral's fingerprint. The main goal of this paper is the identification of minerals by LWIR hyperspectral imaging using a machine learning scheme. The information of hyperspectral imaging has been recorded from the energy emitted from the mineral's surface. Solar energy is the source of energy in remote sensing, while a heating element is the energy source employed in laboratory experiments. Our work contains three main steps where the first step involves obtaining the spectral signatures of pure (single) minerals with a hyperspectral camera, in the long-wave infrared (7.7 to 11.8 μm), which measures the emitted radiance from the minerals' surface. The second step concerns feature extraction by applying the continuous wavelet transform (CWT) and finally we use support vector machine classifier with radial basis functions (SVM-RBF) for classification/identification of minerals. The overall accuracy of classification in our work is 90.23+/- 2.66%. In conclusion, based on CWT's ability to capture the information of signals can be used as a good marker for classification and identification the minerals substance.

  8. Information analysis of hyperspectral images from the hyperion satellite

    Science.gov (United States)

    Puzachenko, Yu. G.; Sandlersky, R. B.; Krenke, A. N.; Puzachenko, M. Yu.

    2017-07-01

    A new method of estimating the outgoing radiation spectra data obtained from the Hyperion EO-1 satellite is considered. In theoretical terms, this method is based on the nonequilibrium thermodynamics concept with corresponding estimates of the entropy and the Kullbak information. The obtained information estimates make it possible to assess the effective work of the landscape cover both in general and for its various types and to identify the spectrum ranges primarily responsible for the information increment and, accordingly, for the effective work. The information is measured in the frequency band intervals corresponding to the peaks of solar radiation absorption by different pigments, mesophyll, and water to evaluate the system operation by their synthesis and moisture accumulation. This method is assumed to be effective in investigation of ecosystem functioning by hyperspectral remote sensing.

  9. Identification of Metal Oxide Nanoparticles in Histological Samples by Enhanced Darkfield Microscopy and Hyperspectral Mapping.

    Science.gov (United States)

    Roth, Gary A; Sosa Peña, Maria del Pilar; Neu-Baker, Nicole M; Tahiliani, Sahil; Brenner, Sara A

    2015-12-08

    Nanomaterials are increasingly prevalent throughout industry, manufacturing, and biomedical research. The need for tools and techniques that aid in the identification, localization, and characterization of nanoscale materials in biological samples is on the rise. Currently available methods, such as electron microscopy, tend to be resource-intensive, making their use prohibitive for much of the research community. Enhanced darkfield microscopy complemented with a hyperspectral imaging system may provide a solution to this bottleneck by enabling rapid and less expensive characterization of nanoparticles in histological samples. This method allows for high-contrast nanoscale imaging as well as nanomaterial identification. For this technique, histological tissue samples are prepared as they would be for light-based microscopy. First, positive control samples are analyzed to generate the reference spectra that will enable the detection of a material of interest in the sample. Negative controls without the material of interest are also analyzed in order to improve specificity (reduce false positives). Samples can then be imaged and analyzed using methods and software for hyperspectral microscopy or matched against these reference spectra in order to provide maps of the location of materials of interest in a sample. The technique is particularly well-suited for materials with highly unique reflectance spectra, such as noble metals, but is also applicable to other materials, such as semi-metallic oxides. This technique provides information that is difficult to acquire from histological samples without the use of electron microscopy techniques, which may provide higher sensitivity and resolution, but are vastly more resource-intensive and time-consuming than light microscopy.

  10. Hyperspectral imaging of the crime scene for detection and identification of blood stains

    Science.gov (United States)

    Edelman, G. J.; van Leeuwen, T. G.; Aalders, M. C. G.

    2013-05-01

    Blood stains are an important source of information in forensic investigations. Extraction of DNA may lead to the identification of victims or suspects, while the blood stain pattern may reveal useful information for the reconstruction of a crime. Consequently, techniques for the detection and identification of blood stains are ideally non-destructive in order not to hamper both DNA and the blood stain pattern analysis. Currently, forensic investigators mainly detect and identify blood stains using chemical or optical methods, which are often either destructive or subject to human interpretation. We demonstrated the feasibility of hyperspectral imaging of the crime scene to detect and identify blood stains remotely. Blood stains outside the human body comprise the main chromophores oxy-hemoglobin, methemoglobin and hemichrome. Consequently, the reflectance spectra of blood stains are influenced by the composite of the optical properties of the individual chromophores and the substrate. Using the coefficient of determination between a non-linear least squares multi-component fit and the measured spectra blood stains were successfully distinguished from other substances visually resembling blood (e.g. ketchup, red wine and lip stick) with a sensitivity of 100 % and a specificity of 85 %. The practical applicability of this technique was demonstrated at a mock crime scene, where blood stains were successfully identified automatically.

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

    Science.gov (United States)

    Xia, Zhengyan; Zhang, Chu; Weng, Haiyong; Nie, Pengcheng; He, Yong

    2017-01-01

    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.

  12. Ensemble learning and model averaging for material identification in hyperspectral imagery

    Science.gov (United States)

    Basener, William F.

    2017-05-01

    In this paper we present a method for identifying the material contained in a pixel or region of pixels in a hyperspectral image. An identification process can be performed on a spectrum from an image from pixels that has been pre-determined to be of interest, generally comparing the spectrum from the image to spectra in an identification library. The metric for comparison used in this paper a Bayesian probability for each material. This probability can be computed either from Bayes' theorem applied to normal distributions for each library spectrum or using model averaging. Using probabilities has the advantage that the probabilities can be summed over spectra for any material class to obtain a class probability. For example, the probability that the spectrum of interest is a fabric is equal to the sum of all probabilities for fabric spectra in the library. We can do the same to determine the probability for a specific type of fabric, or any level of specificity contained in our library. Probabilities not only tell us which material is most likely, the tell us how confident we can be in the material presence; a probability close to 1 indicates near certainty of the presence of a material in the given class, and a probability close to 0.5 indicates that we cannot know if the material is present at the given level of specificity. This is much more informative than a detection score from a target detection algorithm or a label from a classification algorithm. In this paper we present results in the form of a hierarchical tree with probabilities for each node. We use Forest Radiance imagery with 159 bands.

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

    Science.gov (United States)

    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. Hyperspectral Foveated Imaging Sensor for Objects Identification and Tracking Project

    Data.gov (United States)

    National Aeronautics and Space Administration — Optical tracking and identification sensors have numerous NASA and non-NASA applications. For example, airborne or spaceborne imaging sensors are used to visualize...

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

    Science.gov (United States)

    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

  16. [Identification of varieties of black bean using ground based hyperspectral imaging].

    Science.gov (United States)

    Zhang, Chu; Liu, Fei; Zhang, Hai-Liang; Kong, Wen-Wen; He, Yong

    2014-03-01

    In the present study, hyperspectral imaging combined with chemometrics was successfully proposed to identify different varieties of black bean. The varieties of black bean were defined based on the three different colors of the bean core. The hy-perspectral images in the spectral range of 380-1,030 nm of black bean were acquired using the developed hyperspectral imaging system, and the reflectance spectra were extracted from the region of interest (ROD) in the images. The average spectrum of a ROI of the sample in the images was used to represent the spectrum of the sample and build classification models. In total, 180 spectra of 180 samples were extracted. The wavelengths from 440 to 943 nm were used for analysis after the removal of the spec- tral region with absolute noises, and 440-943 nm spectra were preprocessed by multiplicative scatter correction (MSC). Five classification methods, including partial least squares discriminant analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), K-nearest neighbor algorithm (KNN), support vector machine (SVM) and extreme learning machine (ELM), were used to build discriminant models using the preprocessed full spectra, the feature information extracted by principal component analysis (PCA) and the feature information extracted by wavelet transform (WT) from the preprocessed spectra, respectively. Among all the classification models using the preprocessed full spectra, ELM models obtained the best performance; among all the classification models using the feature information extracted from the preprocessed spectra by PCA, ELM model also obtained the best classification accuracy; and among all the classification models using the feature information extracted from the preprocessed spectra by WT, ELM models obtained the best classification performance with 100% accuracy in both the calibration set and the prediction set. Among all classification models, WT-ELM model obtained the best classification accuracy

  17. Oil spill detection using hyperspectral infrared camera

    Science.gov (United States)

    Yu, Hui; Wang, Qun; Zhang, Zhen; Zhang, Zhi-jie; Tang, Wei; Tang, Xin; Yue, Song; Wang, Chen-sheng

    2016-11-01

    Oil spill pollution is a severe environmental problem that persists in the marine environment and in inland water systems around the world. Remote sensing is an important part of oil spill response. The hyperspectral images can not only provide the space information but also the spectral information. Pixels of interests generally incorporate information from disparate component that requires quantitative decomposition of these pixels to extract desired information. Oil spill detection can be implemented by applying hyperspectral camera which can collect the hyperspectral data of the oil. By extracting desired spectral signature from hundreds of band information, one can detect and identify oil spill area in vast geographical regions. There are now numerous hyperspectral image processing algorithms developed for target detection. In this paper, we investigate several most widely used target detection algorithm for the identification of surface oil spills in ocean environment. In the experiments, we applied a hyperspectral camera to collect the real life oil spill. The experimental results shows the feasibility of oil spill detection using hyperspectral imaging and the performance of hyperspectral image processing algorithms were also validated.

  18. [Study on identification the crack feature of fresh jujube using hyperspectral imaging].

    Science.gov (United States)

    Yu, Ke-Qiang; Zhao, Yan-Ru; Li, Xiao-Li; Zhang, Shu-Juan; He, Yong

    2014-02-01

    Crack is one of the most important indicators to evaluate the quality of fresh jujube. Crack not only accelerates the decay of fresh jujube, but also diminishes the shelf life and reduces the economic value severely. In this study, the potential of hyperspectral imaging covered the range of 380 - 1030 nm was evaluated for discrimination crack feature (location and area) of fresh jujube. Regression coefficients of partial least squares regression (PLSR), successive projection analysis (SPA) and principal component analysis (PCA) based full-bands image were adopted to extract sensitive bands of crack of fresh jujube. Then least-squares support vector machine (LS-SVM) discriminant models using the selected sensitive bands for calibration set (132 samples)" were established for identification the prediction set (44 samples). ROC curve was used to judge the discriminant models of PLSR-LS-SVM, SPA-LS-SVM and PCA-LS-SVM which are established by sensitive bands of crack of fresh jujube. The results demonstrated that PLSR-LS-SVM model had an optimal effect (area=1, std=0) to discriminate crack feature of fresh jujube. Next, images corresponding to five sensitive bands (467, 544, 639, 673 and 682 nm) selected by PLSR were executed to PCA. Finally, the image of PC4 was employed to identify the location and area of crack feature through imaging processing. The results revealed that hyperspectral imaging technique combined with image processing could achieve the qualitative discrimination and quantitative identification of crack feature of fresh jujube, which provided a theoretical reference and basis for develop instrument of discrimination of crack of jujube in further work.

  19. Hyperspectral image processing methods

    Science.gov (United States)

    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...

  20. Hyperspectral imaging flow cytometer

    Energy Technology Data Exchange (ETDEWEB)

    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.

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

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

    2017-09-01

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

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

    Directory of Open Access Journals (Sweden)

    N. Jamshidpour

    2017-09-01

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

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

    Directory of Open Access Journals (Sweden)

    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

  5. On Endmember Identification in Hyperspectral Images Without Pure Pixels: A Comparison of Algorithms

    NARCIS (Netherlands)

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

    2012-01-01

    Hyperspectral imaging is an active area of research in Earth and planetary observation. One of the most important techniques for analyzing hyperspectral images is spectral unmixing, in which mixed pixels (resulting from insufficient spatial resolution of the imaging sensor) are decomposed into a

  6. Identification of staphylococcus species with hyperspectral microscope imaging and classification algrorithms

    Science.gov (United States)

    Hyperspectral microscope imaging is presented as a rapid and efficient tool to classify foodborne bacteria species. The spectral data were obtained from five different species of Staphylococcus spp. with a hyperspectral microscope imaging system that provided a maximum of 89 contiguous spectral imag...

  7. Improving Identification of Area Targets by Integrated Analysis of Hyperspectral Data and Extracted Texture Features

    Science.gov (United States)

    2012-09-01

    study area (healthy vegetation shown in red). ................................................................... 24 Figure 10. Study area showing the... multispectral and hyperspectral imaging data typically relies solely on the measured spectrum for that pixel, without reference to surrounding pixels. If...collections. Any one of the tens of multispectral bands could be used or one of the hundreds of hyperspectral bands could be used as well. In the case

  8. A mutual information-Dempster-Shafer based decision ensemble system for land cover classification of hyperspectral data

    Science.gov (United States)

    Pahlavani, Parham; Bigdeli, Behnaz

    2017-12-01

    Hyperspectral images contain extremely rich spectral information that offer great potential to discriminate between various land cover classes. However, these images are usually composed of tens or hundreds of spectrally close bands, which result in high redundancy and great amount of computation time in hyperspectral classification. Furthermore, in the presence of mixed coverage pixels, crisp classifiers produced errors, omission and commission. This paper presents a mutual information-Dempster-Shafer system through an ensemble classification approach for classification of hyperspectral data. First, mutual information is applied to split data into a few independent partitions to overcome high dimensionality. Then, a fuzzy maximum likelihood classifies each band subset. Finally, Dempster-Shafer is applied to fuse the results of the fuzzy classifiers. In order to assess the proposed method, a crisp ensemble system based on a support vector machine as the crisp classifier and weighted majority voting as the crisp fusion method are applied on hyperspectral data. Furthermore, a dimension reduction system is utilized to assess the effectiveness of mutual information band splitting of the proposed method. The proposed methodology provides interesting conclusions on the effectiveness and potentiality of mutual information-Dempster-Shafer based classification of hyperspectral data.

  9. A mutual information-Dempster-Shafer based decision ensemble system for land cover classification of hyperspectral data

    Science.gov (United States)

    Pahlavani, Parham; Bigdeli, Behnaz

    2016-12-01

    Hyperspectral images contain extremely rich spectral information that offer great potential to discriminate between various land cover classes. However, these images are usually composed of tens or hundreds of spectrally close bands, which result in high redundancy and great amount of computation time in hyperspectral classification. Furthermore, in the presence of mixed coverage pixels, crisp classifiers produced errors, omission and commission. This paper presents a mutual information-Dempster-Shafer system through an ensemble classification approach for classification of hyperspectral data. First, mutual information is applied to split data into a few independent partitions to overcome high dimensionality. Then, a fuzzy maximum likelihood classifies each band subset. Finally, Dempster-Shafer is applied to fuse the results of the fuzzy classifiers. In order to assess the proposed method, a crisp ensemble system based on a support vector machine as the crisp classifier and weighted majority voting as the crisp fusion method are applied on hyperspectral data. Furthermore, a dimension reduction system is utilized to assess the effectiveness of mutual information band splitting of the proposed method. The proposed methodology provides interesting conclusions on the effectiveness and potentiality of mutual information-Dempster-Shafer based classification of hyperspectral data.

  10. Hyperspectral Target Detection via Adaptive Joint Sparse Representation and Multi-Task Learning with Locality Information

    Directory of Open Access Journals (Sweden)

    Yuxiang Zhang

    2017-05-01

    Full Text Available Target detection from hyperspectral images is an important problem but encounters a critical challenge of simultaneously reducing spectral redundancy and preserving the discriminative information. Recently, the joint sparse representation and multi-task learning (JSR-MTL approach was proposed to address the challenge. However, it does not fully explore the prior class label information of the training samples and the difference between the target dictionary and background dictionary when constructing the model. Besides, there may exist estimation bias for the unknown coefficient matrix with the use of minimization which is usually inconsistent in variable selection. To address these problems, this paper proposes an adaptive joint sparse representation and multi-task learning detector with locality information (JSRMTL-ALI. The proposed method has the following capabilities: (1 it takes full advantage of the prior class label information to construct an adaptive joint sparse representation and multi-task learning model; (2 it explores the great difference between the target dictionary and background dictionary with different regularization strategies in order to better encode the task relatedness; (3 it applies locality information by imposing an iterative weight on the coefficient matrix in order to reduce the estimation bias. Extensive experiments were carried out on three hyperspectral images, and it was found that JSRMTL-ALI generally shows a better detection performance than the other target detection methods.

  11. Assessing reliability of classification in the most informative spectral regions of hyperspectral images

    Science.gov (United States)

    Aria, S. E. Hosseini; Menenti, M.; Gorte, B. G. H.

    2014-03-01

    Reliability analysis is usually applied to evaluate classification procedures with different classes. In this research, we have applied the analysis to two different band sets to find out which one is more reliable. These band sets provide the most informative spectral regions covered by hyperspectral images. The informative regions are identified by minimizing two dependency measures between bands: correlation coefficient and normalized mutual information. The implementations are done by a newly developed top-down method named Spectral Region Splitting (SRS) resulting in two sets of bands which are almost identical at critical spectral regions. A reliability analysis based on the thresholding technique of the two sets of bands was performed. A technique was applied to discard those pixels that are not correctly classified at the given confidence level. The results show that the informative spectral regions selected by normalized mutual information was more reliable.

  12. Upconversion based MIR hyperspectral imaging

    DEFF Research Database (Denmark)

    Junaid, Saher; Tidemand-Lichtenberg, Peter; Pedersen, Christian

    2017-01-01

    Midinfrared (MIR) hyperspectral imaging has a great potential to be used as a tool for medical diagnostics featuring a combination of imaging and spectroscopy. In hyperspectral imaging, the images of the (biomedical) samples contains both spectral and spatial information....

  13. Identification of moisture content in tobacco plant leaves using outlier sample eliminating algorithms and hyperspectral data.

    Science.gov (United States)

    Sun, Jun; Zhou, Xin; Wu, Xiaohong; Zhang, Xiaodong; Li, Qinglin

    2016-02-26

    Fast identification of moisture content in tobacco plant leaves plays a key role in the tobacco cultivation industry and benefits the management of tobacco plant in the farm. In order to identify moisture content of tobacco plant leaves in a fast and nondestructive way, a method involving Mahalanobis distance coupled with Monte Carlo cross validation(MD-MCCV) was proposed to eliminate outlier sample in this study. The hyperspectral data of 200 tobacco plant leaf samples of 20 moisture gradients were obtained using FieldSpc(®) 3 spectrometer. Savitzky-Golay smoothing(SG), roughness penalty smoothing(RPS), kernel smoothing(KS) and median smoothing(MS) were used to preprocess the raw spectra. In addition, Mahalanobis distance(MD), Monte Carlo cross validation(MCCV) and Mahalanobis distance coupled to Monte Carlo cross validation(MD-MCCV) were applied to select the outlier sample of the raw spectrum and four smoothing preprocessing spectra. Successive projections algorithm (SPA) was used to extract the most influential wavelengths. Multiple Linear Regression (MLR) was applied to build the prediction models based on preprocessed spectra feature in characteristic wavelengths. The results showed that the preferably four prediction model were MD-MCCV-SG (Rp(2) = 0.8401 and RMSEP = 0.1355), MD-MCCV-RPS (Rp(2) = 0.8030 and RMSEP = 0.1274), MD-MCCV-KS (Rp(2) = 0.8117 and RMSEP = 0.1433), MD-MCCV-MS (Rp(2) = 0.9132 and RMSEP = 0.1162). MD-MCCV algorithm performed best among MD algorithm, MCCV algorithm and the method without sample pretreatment algorithm in the eliminating outlier sample from 20 different moisture gradients of tobacco plant leaves and MD-MCCV can be used to eliminate outlier sample in the spectral preprocessing. Copyright © 2016 Elsevier Inc. All rights reserved.

  14. Rapid identification of Salmonella serotypes through hyperspectral microscopy with different lighting sources

    Directory of Open Access Journals (Sweden)

    Matthew Eady

    2016-11-01

    Full Text Available The rapid detection of food-borne pathogenic bacteria is critical to the food industry for preventing the introduction of contaminated product into the marketplace and limiting the spread of outbreaks. Hyperspectral microscope images (HMI are a form of optical detection, which classify bacteria by combining microscope images with a spectrophotometer. The objective of this study was to compare the spectra generated from dark-field HMIs of five live Salmonella serotypes from two lighting sources, metal halide (MH and tungsten halogen (TH, assessing classification accuracy and robustness, between 450 nm and 800 nm. It was found that the MH spectra could be reduced to as few as 10 optimal bands between 594 nm and 630 nm, but TH band reduction decreased accuracy, due to the inherent broader peak structure generated by the TH light source. Collection of HMIs from the two light sources comparing the same cells shows slight differences in scatter intensity patterns. Principal component linear discriminate analysis classified serotype subsets (n = 1800, reporting both MH and TH accuracies at 100%, while the reduced key MH bands achieved 99.4–100% accuracy. Principal component regression calculated the root mean squared error of cross-validation 0.948 for both full spectrum lamps. MH or TH lamps can be effectively used for discriminating bacteria HMIs on a cellular level by serotype, but reducing TH bands may lose crucial classification information.

  15. 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.

  16. 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

  17. 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.

  18. Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: From camera calibration to quality assurance

    Science.gov (United States)

    Aasen, Helge; Burkart, Andreas; Bolten, Andreas; Bareth, Georg

    2015-10-01

    This paper describes a novel method to derive 3D hyperspectral information from lightweight snapshot cameras for unmanned aerial vehicles for vegetation monitoring. Snapshot cameras record an image cube with one spectral and two spatial dimensions with every exposure. First, we describe and apply methods to radiometrically characterize and calibrate these cameras. Then, we introduce our processing chain to derive 3D hyperspectral information from the calibrated image cubes based on structure from motion. The approach includes a novel way for quality assurance of the data which is used to assess the quality of the hyperspectral data for every single pixel in the final data product. The result is a hyperspectral digital surface model as a representation of the surface in 3D space linked with the hyperspectral information emitted and reflected by the objects covered by the surface. In this study we use the hyperspectral camera Cubert UHD 185-Firefly, which collects 125 bands from 450 to 950 nm. The obtained data product has a spatial resolution of approximately 1 cm for the spatial and 21 cm for the hyperspectral information. The radiometric calibration yields good results with less than 1% offset in reflectance compared to an ASD FieldSpec 3 for most of the spectral range. The quality assurance information shows that the radiometric precision is better than 0.13% for the derived data product. We apply the approach to data from a flight campaign in a barley experiment with different varieties during the growth stage heading (BBCH 52 - 59) to demonstrate the feasibility for vegetation monitoring in the context of precision agriculture. The plant parameters retrieved from the data product correspond to in-field measurements of a single date field campaign for plant height (R2 = 0.7), chlorophyll (BGI2, R2 = 0.52), LAI (RDVI, R2 = 0.32) and biomass (RDVI, R2 = 0.29). Our approach can also be applied for other image-frame cameras as long as the individual bands of the

  19. Rapid identification of Salmonella serotypes through hyperspectral microscopy with different lighting sources

    Science.gov (United States)

    Hyperspectral microscope imaging (HMI) has the potential to classify foodborne pathogenic bacteria at cell level by combining microscope images with a spectrophotometer. In this study, the spectra generated from HMIs of five live Salmonella serovars from two light sources, metal halide (MH) and tun...

  20. 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...

  1. Rapid identification of heterogeneous mixture components with hyperspectral coherent anti-Stokes Raman scattering imaging

    NARCIS (Netherlands)

    Garbacik, E.T.; Herek, Jennifer Lynn; Otto, Cornelis; Offerhaus, Herman L.

    2012-01-01

    For the rapid analysis of complicated heterogeneous mixtures, we have developed a method to acquire and intuitively display hyperspectral coherent anti-Stokes Raman scattering (CARS) images. The imaging is performed with a conventional optical setup based around an optical parametric oscillator.

  2. [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

  3. Identification of pesticide varieties by testing microalgae using Visible/Near Infrared Hyperspectral Imaging technology.

    Science.gov (United States)

    Shao, Yongni; Jiang, Linjun; Zhou, Hong; Pan, Jian; He, Yong

    2016-04-13

    In our study, the feasibility of using visible/near infrared hyperspectral imaging technology to detect the changes of the internal components of Chlorella pyrenoidosa so as to determine the varieties of pesticides (such as butachlor, atrazine and glyphosate) at three concentrations (0.6 mg/L, 3 mg/L, 15 mg/L) was investigated. Three models (partial least squares discriminant analysis combined with full wavelengths, FW-PLSDA; partial least squares discriminant analysis combined with competitive adaptive reweighted sampling algorithm, CARS-PLSDA; linear discrimination analysis combined with regression coefficients, RC-LDA) were built by the hyperspectral data of Chlorella pyrenoidosa to find which model can produce the most optimal result. The RC-LDA model, which achieved an average correct classification rate of 97.0% was more superior than FW-PLSDA (72.2%) and CARS-PLSDA (84.0%), and it proved that visible/near infrared hyperspectral imaging could be a rapid and reliable technique to identify pesticide varieties. It also proved that microalgae can be a very promising medium to indicate characteristics of pesticides.

  4. Multispectral, hyperspectral, and LiDAR remote sensing and geographic information fusion for improved earthquake response

    Science.gov (United States)

    Kruse, F. A.; Kim, A. M.; Runyon, S. C.; Carlisle, Sarah C.; Clasen, C. C.; Esterline, C. H.; Jalobeanu, A.; Metcalf, J. P.; Basgall, P. L.; Trask, D. M.; Olsen, R. C.

    2014-06-01

    The Naval Postgraduate School (NPS) Remote Sensing Center (RSC) and research partners have completed a remote sensing pilot project in support of California post-earthquake-event emergency response. The project goals were to dovetail emergency management requirements with remote sensing capabilities to develop prototype map products for improved earthquake response. NPS coordinated with emergency management services and first responders to compile information about essential elements of information (EEI) requirements. A wide variety of remote sensing datasets including multispectral imagery (MSI), hyperspectral imagery (HSI), and LiDAR were assembled by NPS for the purpose of building imagery baseline data; and to demonstrate the use of remote sensing to derive ground surface information for use in planning, conducting, and monitoring post-earthquake emergency response. Worldview-2 data were converted to reflectance, orthorectified, and mosaicked for most of Monterey County; CA. Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data acquired at two spatial resolutions were atmospherically corrected and analyzed in conjunction with the MSI data. LiDAR data at point densities from 1.4 pts/m2 to over 40 points/ m2 were analyzed to determine digital surface models. The multimodal data were then used to develop change detection approaches and products and other supporting information. Analysis results from these data along with other geographic information were used to identify and generate multi-tiered products tied to the level of post-event communications infrastructure (internet access + cell, cell only, no internet/cell). Technology transfer of these capabilities to local and state emergency response organizations gives emergency responders new tools in support of post-disaster operational scenarios.

  5. 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.

  6. Portable hyperspectral imager with continuous wave green laser for identification and detection of untreated latent fingerprints on walls.

    Science.gov (United States)

    Nakamura, Atsushi; Okuda, Hidekazu; Nagaoka, Takashi; Akiba, Norimitsu; Kurosawa, Kenji; Kuroki, Kenro; Ichikawa, Fumihiko; Torao, Akira; Sota, Takayuki

    2015-09-01

    Untreated latent fingerprints are known to exhibit fluorescence under UV laser excitation. Previously, the hyperspectral imager (HSI) has been primarily evaluated in terms of its potential to enhance the sensitivity of latent fingerprint detection following treatment by conventional chemical methods in the forensic science field. In this study however, the potential usability of the HSI for the visualization and detection of untreated latent fingerprints by measuring their inherent fluorescence under continuous wave (CW) visible laser excitation was examined. Its potential to undertake spectral separation of overlapped fingerprints was also evaluated. The excitation wavelength dependence of fluorescent images was examined using an untreated palm print on a steel based wall, and it was found that green laser excitation is superior to blue and yellow lasers' excitation for the production of high contrast fluorescence images. In addition, a spectral separation method for overlapped fingerprints/palm prints on a plaster wall was proposed using new images converted by the division and subtraction of two single wavelength images constructed based on measured hyperspectral data (HSD). In practical tests, the relative isolation of two overlapped fingerprints/palm prints was successful in twelve out of seventeen cases. Only one fingerprint/palm print was extracted for an additional three cases. These results revealed that the feasibility of overlapped fingerprint/palm print spectral separation depends on the difference in the temporal degeneration of each fluorescence spectrum. The present results demonstrate that a combination of a portable HSI and CW green laser has considerable potential for the identification and detection of untreated latent fingerprints/palm prints on the walls under study, while the use of HSD makes it practically possible for doubly overlapped fingerprints/palm prints to be separated spectrally. Copyright © 2015 Elsevier Ireland Ltd. All rights

  7. 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.

  8. Improved Feature Extraction, Feature Selection, and Identification Techniques That Create a Fast Unsupervised Hyperspectral Target Detection Algorithm

    Science.gov (United States)

    2008-03-01

    According to Stein, Beaven, Hoff, Winter, Schaum , and Stocker (2002:62), the local Gaussian model may not be a valid for hyperspectral data if relatively...David W.J., Scott G. Beaven, Lawrence E. Hoff, Edwin M. Winter, Alan P. Schaum and Alan D. Stocker. “Anomaly Detection for Hyperspectral Imagery

  9. Non-invasive identification of traditional red lake pigments in fourteenth to sixteenth centuries paintings through the use of hyperspectral imaging technique

    Science.gov (United States)

    Vitorino, T.; Casini, A.; Cucci, C.; Melo, M. J.; Picollo, M.; Stefani, L.

    2015-11-01

    The present paper, which focuses on the identification of red lake pigments, in particular madder, brazilwood, and cochineal, addresses the advantages and drawbacks of using reflectance hyperspectral imaging in the visible and near-infrared ranges as a non-invasive method of discrimination between different red organic pigments in cultural heritage objects. Based on reconstructions of paints used in the period extending from the fourteenth to the sixteenth century, prepared with as far as possible historical accuracy, the analyses by means of visible/near-infrared reflectance hyperspectral imaging were carried out with the objective of understanding the most significant differences between these vegetal- and animal-based red lake pigments. The paper discusses the results that were obtained on four original Italian and North European paintings and compared with those from the paint reconstructions, in order to demonstrate how the hyperspectral imaging technique can be usefully and effectively applied to the identification and mapping of red lake pigments in painted surfaces of interest in the conservation field.

  10. 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...... practical applications related to different issues: the separation among groups of homogeneous samples and the identification of outlier pixels in the spatial domain. For both case studies, guidance to the identification of the proper level of sparsity will be provided and, furthermore, we will show how...

  11. Hyperspectral-Augmented Target Tracking

    Science.gov (United States)

    2008-03-01

    camera is currently capable of taking a hyperspectral movie , the authors developed a simple algorithm that creates a hyperspectral movie based on a real...Nedeljkovic, I. “Image Classification based on Fuzzy Logic”. The International Archives of the Photogrammetry , Remote Sensing and Spatial Information

  12. 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

  13. Differentiation of bacterial colonies and temporal growth patterns using hyperspectral imaging

    Science.gov (United States)

    Mehrübeoglu, Mehrube; Buck, Gregory W.; Livingston, Daniel W.

    2014-09-01

    Detection and identification of bacteria are important for health and safety. Hyperspectral imaging offers the potential to capture unique spectral patterns and spatial information from bacteria which can then be used to detect and differentiate bacterial species. Here, hyperspectral imaging has been used to characterize different bacterial colonies and investigate their growth over time. Six bacterial species (Pseudomonas fluorescens, Escherichia coli, Serratia marcescens, Salmonella enterica, Staphylococcus aureus, Enterobacter aerogenes) were grown on tryptic soy agar plates. Hyperspectral data were acquired immediately after, 24 hours after, and 96 hours after incubation. Spectral signatures from bacterial colonies demonstrated repeatable measurements for five out of six species. Spatial variations as well as changes in spectral signatures were observed across temporal measurements within and among species at multiple wavelengths due to strengthening or weakening reflectance signals from growing bacterial colonies based on their pigmentation. Between-class differences and within-class similarities were the most prominent in hyperspectral data collected 96 hours after incubation.

  14. Meat quality evaluation by hyperspectral imaging technique: an overview.

    Science.gov (United States)

    Elmasry, Gamal; Barbin, Douglas F; Sun, Da-Wen; Allen, Paul

    2012-01-01

    During the last two decades, a number of methods have been developed to objectively measure meat quality attributes. Hyperspectral imaging technique as one of these methods has been regarded as a smart and promising analytical tool for analyses conducted in research and industries. Recently there has been a renewed interest in using hyperspectral imaging in quality evaluation of different food products. The main inducement for developing the hyperspectral imaging system is to integrate both spectroscopy and imaging techniques in one system to make direct identification of different components and their spatial distribution in the tested product. By combining spatial and spectral details together, hyperspectral imaging has proved to be a promising technology for objective meat quality evaluation. The literature presented in this paper clearly reveals that hyperspectral imaging approaches have a huge potential for gaining rapid information about the chemical structure and related physical properties of all types of meat. In addition to its ability for effectively quantifying and characterizing quality attributes of some important visual features of meat such as color, quality grade, marbling, maturity, and texture, it is able to measure multiple chemical constituents simultaneously without monotonous sample preparation. Although this technology has not yet been sufficiently exploited in meat process and quality assessment, its potential is promising. Developing a quality evaluation system based on hyperspectral imaging technology to assess the meat quality parameters and to ensure its authentication would bring economical benefits to the meat industry by increasing consumer confidence in the quality of the meat products. This paper provides a detailed overview of the recently developed approaches and latest research efforts exerted in hyperspectral imaging technology developed for evaluating the quality of different meat products and the possibility of its widespread

  15. 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.

  16. 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...

  17. 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...

  18. Separating Atmospheric and Surface Contributions in Hyperspectral Imager for the Coastal Ocean (HICO) Scenes using Informed Non-Negative Matrix Factorization

    Science.gov (United States)

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

    2016-12-01

    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. These new instruments require novel approaches for processing imagery and separating surface and atmospheric signals. One approach is numerical source separation, which allows the determination of the underlying physical causes of observed signals. Improved source separation will enable hyperspectral imagery to better address key science questions relevant to climate change, including land-use changes, trends in clouds and atmospheric water vapor, and aerosol characteristics. We developed an Informed Non-negative Matrix Factorization (INMF) method for separating atmospheric and surface 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. We also explore methods to produce an initial guess of the spatial separation patterns. 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 conditions, including high and low aerosol optical thickness and cloud cover, with only minor contributions from the ocean surfaces in order to isolate the contributions of the multiple atmospheric

  19. Transferability of multi- and hyperspectral optical biocrust indices

    Science.gov (United States)

    Rodríguez-Caballero, E.; Escribano, P.; Olehowski, C.; Chamizo, S.; Hill, J.; Cantón, Y.; Weber, B.

    2017-04-01

    Biological soil crusts (biocrusts) are communities of cyanobacteria, algae, microfungi, lichens and bryophytes in varying proportions, which live within or immediately on top of the uppermost millimeters of the soil in arid and semiarid regions. As biocrusts are highly relevant for ecosystem processes like carbon, nitrogen, and water cycling, a correct characterization of their spatial distribution is required. Following this objective, considerable efforts have been devoted to the identification and mapping of biocrusts using remote sensing data, and several mapping indices have been developed. However, their transferability to different regions has only rarely been tested. In this study we investigated the transferability of two multispectral indices, i.e. the Crust Index (CI) and the Biological Soil Crust Index (BSCI), and two hyperspectral indices, i.e. the Continuum Removal Crust Identification Algorithm (CRCIA) and the Crust Development Index (CDI), in three sites dominated by biocrusts, but with differences in soil and vegetation composition. Whereas multispectral indices have been important and valuable tools for first approaches to map and classify biological soil crusts, hyperspectral data and indices developed for these allowed to classify biocrusts at much higher accuracy. While multispectral indices showed Kappa (κ) values below 0.6, hyperspectral indices obtained good classification accuracy (κ ˜ 0.8) in both the study area where they had been developed and in the newly tested region. These results highlight the capability of hyperspectral sensors to identify specific absorption features related to photosynthetic pigments as chlorophyll and carotenoids, but also the limitation of multispectral information to discriminate between areas dominated by biocrusts, vegetation or bare soil. Based on these results we conclude that remote sensing offers an important and valid tool to map biocrusts. However, the spectral similarity between the main surface

  20. Evaluation of Hyperspectral Indices for Chlorophyll-a Concentration Estimation in Tangxun Lake (Wuhan, China

    Directory of Open Access Journals (Sweden)

    Yaohuan Huang

    2010-05-01

    Full Text Available Chlorophyll-a (Chl-a concentration is a major indicator of water quality which is harmful to human health. A growing number of studies have focused on the derivation of Chl-a concentration information from hyperspectral sensor data and the identification of best indices for Chl-a monitoring. The objective of this study is to assess the potential of hyperspectral indices to detect Chl-a concentrations in Tangxun Lake, which is the second largest lake in Wuhan, Central China. Hyperspectral reflectance and Chl-a concentration were measured at ten sample sites in Tangxun Lake. Three types of hyperspectral methods, including single-band reflectance, first derivative of reflectance, and reflectance ratio, were extracted from the spectral profiles of all bands of the hyperspectral sensor. The most appropriate bands for algorithms mentioned above were selected based on the correlation analysis. Evaluation results indicated that two methods, the first derivative of reflectance and reflectance ratio, were highly correlated (R2 > 0.8 with the measured Chl-a concentrations. Thus, the spatial and temporal variations of Chl-a concentration could be conveniently monitored with these hyperspectral methods.

  1. Combination of spectral and textural information of hyperspectral imaging for the prediction of the moisture content and storage time of cooked beef

    Science.gov (United States)

    Yang, Dong; He, Dandan; Lu, Anxiang; Ren, Dong; Wang, Jihua

    2017-06-01

    The feasibility of combining spectral and textural information from hyperspectral imaging to predict the moisture content and storage time of cooked beef was explored. A total of 10 optimal wavelengths were selected for the moisture content and storage time by conducting variable combination population analysis (VCPA). Principal component analysis was employed to reduce the number of dimensions of hyperspectral images, while a discrete cosine transform was applied to the first three principal component images to extract 30 textural features. A back-propagation artificial neural network (BP-ANN) model and partial least-squares regression model were developed to predict the moisture content and storage time from spectra, textural data, and their combination. The fused BP-ANN model provided satisfactory results with Rp2 of 0.977, and RMSEP of 0.9151 for the prediction of moisture content; these results were superior to those obtained with spectral or textual information alone. Combined with the storage time, the distribution map of the moisture content of cooked beef was visualized using the best fused BP-ANN model with imaging process method. The results reveal that the combination of spectral and textural information of hyperspectral imaging coupled with the BP-ANN algorithm has strong potential for the prediction and visualization of the moisture content of cooked beef at different storage times.

  2. From local spectral measurements to maps of vegetation cover and biomass on the Qinghai-Tibet-Plateau: Do we need hyperspectral information?

    Science.gov (United States)

    Meyer, Hanna; Lehnert, Lukas W.; Wang, Yun; Reudenbach, Christoph; Nauss, Thomas; Bendix, Jörg

    2017-03-01

    Though the relevance of pasture degradation on the Qinghai-Tibet Plateau (QTP) is widely postulated, its extent is still unknown. Due to the enormous spatial extent, remote sensing provides the only possibility to investigate pasture degradation via frequently used proxies such as vegetation cover and aboveground biomass (AGB). However, unified remote sensing approaches are still lacking. This study tests the applicability of hyper- and multispectral in situ measurements to map vegetation cover and AGB on regional scales. Using machine learning techniques, it is tested whether the full hyperspectral information is needed or if multispectral information is sufficient to accurately estimate pasture degradation proxies. To regionalize pasture degradation proxies, the transferability of the locally derived ML-models to high resolution multispectral satellite data is assessed. 1183 hyperspectral measurements and vegetation records were performed at 18 locations on the QTP. Random Forests models with recursive feature selection were trained to estimate vegetation cover and AGB using narrow-band indices (NBI) as predictors. Separate models were calculated using NBI from hyperspectral data as well as from the same data resampled to WorldView-2, QuickBird and RapidEye channels. The hyperspectral results were compared to the multispectral results. Finally, the models were applied to satellite data to map vegetation cover and AGB on a regional scale. Vegetation cover was accurately predicted by Random Forest if hyperspectral measurements were used (cross validated R2 = 0.89). In contrast, errors in AGB estimations were considerably higher (cross validated R2 = 0.32). Only small differences in accuracy were observed between the models based on hyperspectral compared to multispectral data. The application of the models to satellite images generally resulted in an increase of the estimation error. Though this reflects the challenge of applying in situ measurements to satellite

  3. Identification of pesticide varieties by detecting characteristics of Chlorella pyrenoidosa using Visible/Near infrared hyperspectral imaging and Raman microspectroscopy technology.

    Science.gov (United States)

    Shao, Yongni; Li, Yuan; Jiang, Linjun; Pan, Jian; He, Yong; Dou, Xiaoming

    2016-11-01

    The main goal of this research is to examine the feasibility of applying Visible/Near-infrared hyperspectral imaging (Vis/NIR-HSI) and Raman microspectroscopy technology for non-destructive identification of pesticide varieties (glyphosate and butachlor). Both mentioned technologies were explored to investigate how internal elements or characteristics of Chlorella pyrenoidosa change when pesticides are applied, and in the meantime, to identify varieties of the pesticides during this procedure. Successive projections algorithm (SPA) was introduced to our study to identify seven most effective wavelengths. With those wavelengths suggested by SPA, a model of the linear discriminant analysis (LDA) was established to classify the pesticide varieties, and the correct classification rate of the SPA-LDA model reached as high as 100%. For the Raman technique, a few partial least squares discriminant analysis models were established with different preprocessing methods from which we also identified one processing approach that achieved the most optimal result. The sensitive wavelengths (SWs) which are related to algae's pigment were chosen, and a model of LDA was established with the correct identification reached a high level of 90.0%. The results showed that both Vis/NIR-HSI and Raman microspectroscopy techniques are capable to identify pesticide varieties in an indirect but effective way, and SPA is an effective wavelength extracting method. The SWs corresponding to microalgae pigments, which were influenced by pesticides, could also help to characterize different pesticide varieties and benefit the variety identification. Copyright © 2016 Elsevier Ltd. All rights reserved.

  4. A comparison of visible wavelength reflectance hyperspectral imaging and Acid Black 1 for the detection and identification of blood stained fingerprints.

    Science.gov (United States)

    Cadd, Samuel; Li, Bo; Beveridge, Peter; O Hare, William T; Campbell, Andrew; Islam, Meez

    2016-07-01

    Bloodstains are often encountered at scenes of violent crime and have significant forensic value for criminal investigations. Blood is one of the most commonly encountered types of biological evidence and is the most commonly observed fingerprint contaminant. Presumptive tests are used to test blood stain and blood stained fingerprints are targeted with chemical enhancement methods, such as acid stains, including Acid Black 1, Acid Violet 17 or Acid Yellow 7. Although these techniques successfully visualise ridge detail, they are destructive, do not confirm the presence of blood and can have a negative impact on DNA sampling. A novel application of visible wavelength hyperspectral imaging (HSI) is used for the non-contact, non-destructive detection and identification of blood stained fingerprints on white tiles both before and after wet chemical enhancement using Acid Black 1. The identification was obtained in a non-contact and non-destructive manner, based on the unique visible absorption spectrum of haemoglobin between 400 and 500nm. Results from the exploration of the selectivity of the setup to detect blood against ten other non-blood protein contaminants are also presented. A direct comparison of the effectiveness of HSI with chemical enhancement using Acid Black 1 on white tiles is also shown. Copyright © 2016 The Chartered Society of Forensic Sciences. Published by Elsevier Ireland Ltd. All rights reserved.

  5. Compact hyperspectral image sensor based on a novel hyperspectral encoder

    Science.gov (United States)

    Hegyi, Alex N.; Martini, Joerg

    2015-06-01

    A novel hyperspectral imaging sensor is demonstrated that can enable breakthrough applications of hyperspectral imaging in domains not previously accessible. Our technology consists of a planar hyperspectral encoder combined with a traditional monochrome image sensor. The encoder adds negligibly to the sensor's overall size, weight, power requirement, and cost (SWaP-C); therefore, the new imager can be incorporated wherever image sensors are currently used, such as in cell phones and other consumer electronics. In analogy to Fourier spectroscopy, the technique maintains a high optical throughput because narrow-band spectral filters are unnecessary. Unlike conventional Fourier techniques that rely on Michelson interferometry, our hyperspectral encoder is robust to vibration and amenable to planar integration. The device can be viewed within a computational optics paradigm: the hardware is uncomplicated and serves to increase the information content of the acquired data, and the complexity of the system, that is, the decoding of the spectral information, is shifted to computation. Consequently, system tradeoffs, for example, between spectral resolution and imaging speed or spatial resolution, are selectable in software. Our prototype demonstration of the hyperspectral imager is based on a commercially-available silicon CCD. The prototype encoder was inserted within the camera's ~1 cu. in. housing. The prototype can image about 49 independent spectral bands distributed from 350 nm to 1250 nm, but the technology may be extendable over a wavelength range from ~300 nm to ~10 microns, with suitable choice of detector.

  6. Monitoring Deformation in Graphene Through Hyperspectral Synchrotron Spectroscopy to Inform Fabrication (Postprint)

    Science.gov (United States)

    2017-06-30

    promise from graphene to produce devices with high mobilities and detectors with fast response times is truncated in practice by strain and...Supporting Information ABSTRACT: The promise from graphene to produce devices with high mobilities and detectors with fast response times is truncated in... importance , in particular for electronic applications, as strain fluctuations limit carrier mobility in detriment to operational Figure 1. Acquisition

  7. Hyperspectral Data: Efficient and Secure Transmission

    Directory of Open Access Journals (Sweden)

    Raffaele Pizzolante

    2017-11-01

    Full Text Available Airborne and spaceborne hyperspectral sensors collect information which is derived from the electromagnetic spectrum of an observed area. Hyperspectral data are used in several studies and they are an important aid in different real-life applications (e.g., mining and geology applications, ecology, surveillance, etc.. A hyperspectral image has a three-dimensional structure (a sort of datacube: it can be considered as a sequence of narrow and contiguous spectral channels (bands. The objective of this paper is to present a framework permits the efficient storage/transmission of an input hyperspectral image, and its protection. The proposed framework relies on a reversible invisible watermarking scheme and an efficient lossless compression algorithm. The reversible watermarking scheme is used in conjunction with digital signature techniques in order to permit the verification of the integrity of a hyperspectral image by the receiver.

  8. Water Hyacinth Identification Using CART Modeling With Hyperspectral Data in the Sacramento-San Joaquin River Delta of California

    Science.gov (United States)

    Khanna, S.; Hestir, E. L.; Santos, M. J.; Greenberg, J. A.; Ustin, S. L.

    2007-12-01

    Water hyacinth (Eichhornia crassipes) is an invasive aquatic weed that is causing severe economic and ecological impacts in the Sacramento-San Joaquin River Delta (California, USA). Monitoring its distribution using remote sensing is the crucial first step in modeling its predicted spread and implementing control and eradication efforts. However, accurately mapping this species is confounded by its several phenological forms, namely a healthy vegetative canopy, flowering canopy with dense conspicuous terminal flowers above the foliage, and floating dead and senescent forms. The full range of these phenologies may be simultaneously present at any time, given the heterogeneity of environmental and ecological conditions in the Delta. There is greater spectral variation within water hyacinth than between any of the co-occurring species (pennywort and water primrose), so classification approaches must take these different phenological stages into consideration. We present an approach to differentiating water hyacinth from co-occurring species based on knowledge of relevant variation in leaf chlorophyll, floral pigments, foliage water content, and variation in leaf structure using a classification and regression tree (CART) applied to airborne hyperspectral remote sensing imagery.

  9. 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.

  10. Pixel-Based Land Cover Classification by Fusing Hyperspectral and LIDAR Data

    Science.gov (United States)

    Jahan, F.; Awrangjeb, M.

    2017-09-01

    Land cover classification has many applications like forest management, urban planning, land use change identification and environment change analysis. The passive sensing of hyperspectral systems can be effective in describing the phenomenology of the observed area over hundreds of (narrow) spectral bands. On the other hand, the active sensing of LiDAR (Light Detection and Ranging) systems can be exploited for characterising topographical information of the area. As a result, the joint use of hyperspectral and LiDAR data provides a source of complementary information, which can greatly assist in the classification of complex classes. In this study, we fuse hyperspectral and LiDAR data for land cover classification. We do a pixel-wise classification on a disjoint set of training and testing samples for five different classes. We propose a new feature combination by fusing features from both hyperspectral and LiDAR, which achieves competent classification accuracy with low feature dimension, while the existing method requires high dimensional feature vector to achieve similar classification result. Also, for the reduction of the dimension of the feature vector, Principal Component Analysis (PCA) is used as it captures the variance of the samples with a limited number of Principal Components (PCs). We tested our classification method using PCA applied on hyperspectral bands only and combined hyperspectral and LiDAR features. Classification with support vector machine (SVM) and decision tree shows that our feature combination achieves better classification accuracy compared to the existing feature combination, while keeping the similar number of PCs. The experimental results also show that decision tree performs better than SVM and requires less execution time.

  11. PIXEL-BASED LAND COVER CLASSIFICATION BY FUSING HYPERSPECTRAL AND LIDAR DATA

    Directory of Open Access Journals (Sweden)

    F. Jahan

    2017-09-01

    Full Text Available Land cover classification has many applications like forest management, urban planning, land use change identification and environment change analysis. The passive sensing of hyperspectral systems can be effective in describing the phenomenology of the observed area over hundreds of (narrow spectral bands. On the other hand, the active sensing of LiDAR (Light Detection and Ranging systems can be exploited for characterising topographical information of the area. As a result, the joint use of hyperspectral and LiDAR data provides a source of complementary information, which can greatly assist in the classification of complex classes. In this study, we fuse hyperspectral and LiDAR data for land cover classification. We do a pixel-wise classification on a disjoint set of training and testing samples for five different classes. We propose a new feature combination by fusing features from both hyperspectral and LiDAR, which achieves competent classification accuracy with low feature dimension, while the existing method requires high dimensional feature vector to achieve similar classification result. Also, for the reduction of the dimension of the feature vector, Principal Component Analysis (PCA is used as it captures the variance of the samples with a limited number of Principal Components (PCs. We tested our classification method using PCA applied on hyperspectral bands only and combined hyperspectral and LiDAR features. Classification with support vector machine (SVM and decision tree shows that our feature combination achieves better classification accuracy compared to the existing feature combination, while keeping the similar number of PCs. The experimental results also show that decision tree performs better than SVM and requires less execution time.

  12. Quantitative Hyperspectral Reflectance Imaging

    Directory of Open Access Journals (Sweden)

    Ted A.G. Steemers

    2008-09-01

    Full Text Available Hyperspectral imaging is a non-destructive optical analysis technique that can for instance be used to obtain information from cultural heritage objects unavailable with conventional colour or multi-spectral photography. This technique can be used to distinguish and recognize materials, to enhance the visibility of faint or obscured features, to detect signs of degradation and study the effect of environmental conditions on the object. We describe the basic concept, working principles, construction and performance of a laboratory instrument specifically developed for the analysis of historical documents. The instrument measures calibrated spectral reflectance images at 70 wavelengths ranging from 365 to 1100 nm (near-ultraviolet, visible and near-infrared. By using a wavelength tunable narrow-bandwidth light-source, the light energy used to illuminate the measured object is minimal, so that any light-induced degradation can be excluded. Basic analysis of the hyperspectral data includes a qualitative comparison of the spectral images and the extraction of quantitative data such as mean spectral reflectance curves and statistical information from user-defined regions-of-interest. More sophisticated mathematical feature extraction and classification techniques can be used to map areas on the document, where different types of ink had been applied or where one ink shows various degrees of degradation. The developed quantitative hyperspectral imager is currently in use by the Nationaal Archief (National Archives of The Netherlands to study degradation effects of artificial samples and original documents, exposed in their permanent exhibition area or stored in their deposit rooms.

  13. Arizona law enforcement biometrics identification and information sharing technology framework

    OpenAIRE

    Kalaf, William M.

    2010-01-01

    CHDS State/Local Approved for public release; distribution is unlimited Since 9/11, Arizona and federal law enforcement agencies understand the need to improve subject identification capabilities and integrate criminal information across jurisdictions. Agencies still collect information based on a subject's name and demographics for identification. Using a subject's name and demographics as keys to identifying information is a weakness. In 2012, Arizona will upgrade the state's strat...

  14. 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

  15. Hyperspectral image representation and processing with binary partition trees

    OpenAIRE

    Valero Valbuena, Silvia

    2012-01-01

    Premi extraordinari doctorat curs 2011-2012, àmbit Enginyeria de les TIC The optimal exploitation of the information provided by hyperspectral images requires the development of advanced image processing tools. Therefore, under the title Hyperspectral image representation and Processing with Binary Partition Trees, this PhD thesis proposes the construction and the processing of a new region-based hierarchical hyperspectral image representation: the Binary Partition Tree (BPT). This hierarc...

  16. Hyperspectral sensors and the conservation of monumental buildings

    Science.gov (United States)

    Camaiti, Mara; Benvenuti, Marco; Chiarantini, Leandro; Costagliola, Pilar; Moretti, Sandro; Paba, Francesca; Pecchioni, Elena; Vettori, Silvia

    2010-05-01

    The continuous control of the conservation state of outdoor materials is a good practice for timely planning conservative interventions and therefore to preserve historical buildings. The monitoring of surfaces composition, in order to characterize compounds of neo-formation and deposition, by traditional diagnostic campaigns, although gives accurate results, is a long and expensive method, and often micro-destructive analyses are required. On the other hand, hyperspectral analysis in the visible and near infrared (VNIR) region is a very common technique for determining the characteristics and properties of soils, air, and water in consideration of its capability to give information in a rapid, simultaneous and not-destructive way. VNIR Hypespectral analysis, which discriminate materials on the basis of their different patterns of absorption at specific wavelengths, are in fact successfully used for identifying minerals and rocks (1), as well as for detecting soil properties including moisture, organic content and salinity (2). Among the existing VNIR techniques (Laboratory Spectroscopy - LS, Portable Spectroscopy - PS and Imaging Spectroscopy - IS), PS and IS can play a crucial role in the characterization of components of exposed stone surfaces. In particular, the Imaging Spectroscopic (remote sensing), which uses sensors placed both on land or airborne, may contribute to the monitoring of large areas in consideration of its ability to produce large areal maps at relatively low costs. In this presentation the application of hyperspectral instruments (mainly PS and IS, not applied before in the field of monumental building diagnostic) to quantify the degradation of carbonate surfaces will be discussed. In particular, considering gypsum as the precursor symptom of damage, many factors which may affect the estimation of gypsum content on the surface will be taken into consideration. Two hyperspectral sensors will be considered: 1) A portable radiometer (ASD

  17. 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...

  18. 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...

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

    National Research Council Canada - National Science Library

    Qing-Jun Xu; Fa-Wang Ye; Shao-Feng Liu; Zhi-Xin Zhang; Chuan Zhang

    2017-01-01

    .... 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...

  20. Hyperspectral light field image denoising

    Science.gov (United States)

    Liu, Yun; Qi, Na; Cheng, Zhen; Liu, Dong; Xiong, Zhiwei

    2018-01-01

    Hyperspectral light field (HSLF) images with enriched spectral and angular information provide better representation of real scenes than conventional 2D images. In this paper, we propose a novel denoising method for HSLF images. The proposed method consists of two main steps. First, we generalize the intrinsic tensor sparsity (ITS) measure previously used for 3D hyperspectral image denoising to the 5D HSLF, by using the global correlation along the spectral dimension and the nonlocal similarity across the spatial and angular dimensions. Second, we further exploit the spatial-angular correlation by integrating light field super-resolution (SR) into the denoising process. In this way, the 5D HSLF can be better recovered. Experimental results validate the superior performance of the proposed method in terms of both objective and subjective quality on a self-collected HSLF dataset, in comparison with directly applying the state-of-the-art denoising methods.

  1. Food quality assessment by NIR hyperspectral imaging

    Science.gov (United States)

    Whitworth, Martin B.; Millar, Samuel J.; Chau, Astor

    2010-04-01

    Near infrared reflectance (NIR) spectroscopy is well established in the food industry for rapid compositional analysis of bulk samples. NIR hyperspectral imaging provides new opportunities to measure the spatial distribution of components such as moisture and fat, and to identify and measure specific regions of composite samples. An NIR hyperspectral imaging system has been constructed for food research applications, incorporating a SWIR camera with a cooled 14 bit HgCdTe detector and N25E spectrograph (Specim Ltd, Finland). Samples are scanned in a pushbroom mode using a motorised stage. The system has a spectral resolution of 256 pixels covering a range of 970-2500 nm and a spatial resolution of 320 pixels covering a swathe adjustable from 8 to 300 mm. Images are acquired at a rate of up to 100 lines s-1, enabling samples to be scanned within a few seconds. Data are captured using SpectralCube software (Specim) and analysed using ENVI and IDL (ITT Visual Information Solutions). Several food applications are presented. The strength of individual absorbance bands enables the distribution of particular components to be assessed. Examples are shown for detection of added gluten in wheat flour and to study the effect of processing conditions on fat distribution in chips/French fries. More detailed quantitative calibrations have been developed to study evolution of the moisture distribution in baguettes during storage at different humidities, to assess freshness of fish using measurements of whole cod and fillets, and for prediction of beef quality by identification and separate measurement of lean and fat regions.

  2. Hyperspectral imaging of bruised skin

    Science.gov (United States)

    Randeberg, Lise L.; Baarstad, Ivar; Løke, Trond; Kaspersen, Peter; Svaasand, Lars O.

    2006-02-01

    Bruises can be important evidence in legal medicine, for example in cases of child abuse. Optical techniques can be used to discriminate and quantify the chromophores present in bruised skin, and thereby aid dating of an injury. However, spectroscopic techniques provide only average chromophore concentrations for the sampled volume, and contain little information about the spatial chromophore distribution in the bruise. Hyperspectral imaging combines the power of imaging and spectroscopy, and can provide both spectroscopic and spatial information. In this study a hyperspectral imaging system developed by Norsk Elektro Optikk AS was used to measure the temporal development of bruised skin in a human volunteer. The bruises were inflicted by paintball bullets. The wavelength ranges used were 400 - 1000 nm (VNIR) and 900 - 1700 nm (SWIR), and the spectral sampling intervals were 3.7 and 5 nm, respectively. Preliminary results show good spatial discrimination of the bruised areas compared to normal skin. Development of a white spot can be seen in the central zone of the bruises. This central white zone was found to resemble the shape of the object hitting the skin, and is believed to develop in areas where the impact caused vessel damage. These results show that hyperspectral imaging is a promising technique to evaluate the temporal and spatial development of bruises on human skin.

  3. Identification of informative simple sequence repeat (SSR) markers ...

    African Journals Online (AJOL)

    Phi080) to 0.79 (UMC2359), with a mean PIC of 0.53. The analysis also led to identification of informative SSR markers, namely UMC1862 (bin 1.11), UMC1719 (bin 4.10-4.11), UMC1447 (bin 5.03), UMC2359 (bin 9.07) and UMC1432 (bin 10.02), ...

  4. 22 CFR 1411.5 - Identification of information requested.

    Science.gov (United States)

    2010-04-01

    ... 22 Foreign Relations 2 2010-04-01 2010-04-01 true Identification of information requested. 1411.5 Section 1411.5 Foreign Relations FOREIGN SERVICE LABOR RELATIONS BOARD; FEDERAL LABOR RELATIONS AUTHORITY; GENERAL COUNSEL OF THE FEDERAL LABOR RELATIONS AUTHORITY; AND THE FOREIGN SERVICE IMPASSE DISPUTES PANEL...

  5. MULTIPLE REFLECTION EFFECTS IN NONLINEAR MIXTURE MODEL FOR HYPERSPECTRAL IMAGE ANALYSIS

    Directory of Open Access Journals (Sweden)

    C. Y. Liu

    2016-06-01

    Full Text Available Hyperspectral spectrometers can record electromagnetic energy with hundreds or thousands of spectral channels. With such high spectral resolution, the spectral information has better capability for material identification. Because of the spatial resolution, one pixel in hyperspectral images usually covers several meters, and it may contain more than one material. Therefore, the mixture model must be considered. Linear mixture model (LMM has been widely used for remote sensing target classifications, because of its simplicity and yields reasonable results for smooth surfaces. For rough surfaces, the physical interactions of the light scattered between multiple materials in the scene must be considered. Recently, Generalized Bilinear Model (GBM is proposed and it includes the double reflection between different materials into a nonlinear model, but it ignores the interactions within the same material. In this study, we propose a modified version of GBM to further consider this effect in our model, called Modified Generalized Bilinear Model (MGBM.

  6. Comparing methods for analysis of biomedical hyperspectral image data

    Science.gov (United States)

    Leavesley, Silas J.; Sweat, Brenner; Abbott, Caitlyn; Favreau, Peter F.; Annamdevula, Naga S.; Rich, Thomas C.

    2017-02-01

    Over the past 2 decades, hyperspectral imaging technologies have been adapted to address the need for molecule-specific identification in the biomedical imaging field. Applications have ranged from single-cell microscopy to whole-animal in vivo imaging and from basic research to clinical systems. Enabling this growth has been the availability of faster, more effective hyperspectral filtering technologies and more sensitive detectors. Hence, the potential for growth of biomedical hyperspectral imaging is high, and many hyperspectral imaging options are already commercially available. However, despite the growth in hyperspectral technologies for biomedical imaging, little work has been done to aid users of hyperspectral imaging instruments in selecting appropriate analysis algorithms. Here, we present an approach for comparing the effectiveness of spectral analysis algorithms by combining experimental image data with a theoretical "what if" scenario. This approach allows us to quantify several key outcomes that characterize a hyperspectral imaging study: linearity of sensitivity, positive detection cut-off slope, dynamic range, and false positive events. We present results of using this approach for comparing the effectiveness of several common spectral analysis algorithms for detecting weak fluorescent protein emission in the midst of strong tissue autofluorescence. Results indicate that this approach should be applicable to a very wide range of applications, allowing a quantitative assessment of the effectiveness of the combined biology, hardware, and computational analysis for detecting a specific molecular signature.

  7. Automated Ortho-Rectification of UAV-Based Hyperspectral Data over an Agricultural Field Using Frame RGB Imagery

    Directory of Open Access Journals (Sweden)

    Ayman Habib

    2016-09-01

    Full Text Available Low-cost Unmanned Airborne Vehicles (UAVs equipped with consumer-grade imaging systems have emerged as a potential remote sensing platform that could satisfy the needs of a wide range of civilian applications. Among these applications, UAV-based agricultural mapping and monitoring have attracted significant attention from both the research and professional communities. The interest in UAV-based remote sensing for agricultural management is motivated by the need to maximize crop yield. Remote sensing-based crop yield prediction and estimation are primarily based on imaging systems with different spectral coverage and resolution (e.g., RGB and hyperspectral imaging systems. Due to the data volume, RGB imaging is based on frame cameras, while hyperspectral sensors are primarily push-broom scanners. To cope with the limited endurance and payload constraints of low-cost UAVs, the agricultural research and professional communities have to rely on consumer-grade and light-weight sensors. However, the geometric fidelity of derived information from push-broom hyperspectral scanners is quite sensitive to the available position and orientation established through a direct geo-referencing unit onboard the imaging platform (i.e., an integrated Global Navigation Satellite System (GNSS and Inertial Navigation System (INS. This paper presents an automated framework for the integration of frame RGB images, push-broom hyperspectral scanner data and consumer-grade GNSS/INS navigation data for accurate geometric rectification of the hyperspectral scenes. The approach relies on utilizing the navigation data, together with a modified Speeded-Up Robust Feature (SURF detector and descriptor, for automating the identification of conjugate features in the RGB and hyperspectral imagery. The SURF modification takes into consideration the available direct geo-referencing information to improve the reliability of the matching procedure in the presence of repetitive texture

  8. Feature Selection on Hyperspectral Data for Dismount Skin Analysis

    Science.gov (United States)

    2014-03-27

    to make an identification. This is not the case with other human identification systems, such as fingerprint, voice, hand and iris [42], which...challenge. For decades, technology has been evolving to expand the capabilities of human identification systems. The most popular biometric ...technologies include fingerprint, voice, hand, iris , and face analysis [42]. A new approach to human identification could use hyperspectral imaging (HSI) to

  9. 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...

  10. Multimodal hyperspectral optical microscopy

    Science.gov (United States)

    Novikova, Irina V.; Smallwood, Chuck R.; Gong, Yu; Hu, Dehong; Hendricks, Leif; Evans, James E.; Bhattarai, Ashish; Hess, Wayne P.; El-Khoury, Patrick Z.

    2017-11-01

    We describe a unique approach to hyperspectral optical microscopy, herein achieved by coupling a hyperspectral imager to various optical microscopes. Hyperspectral fluorescence micrographs of isolated fluorescent beads are first employed to ensure spectral calibration of our detector and to gauge the attainable spatial resolution of our measurements. Different science applications of our instrument are then described. Spatially over-sampled absorption spectroscopy of a single lipid (18:1 Liss Rhod PE) layer reveals that optical densities on the order of 10-3 can be resolved by spatially averaging the recorded optical signatures. This is followed by three applications in the general areas of plasmonics and bioimaging. Notably, we deploy hyperspectral absorption microscopy to identify and image pigments within a simple biological system, namely, a single live Tisochrysis lutea cell. Overall, this work paves the way for multimodal spectral imaging measurements spanning the realms of several scientific disciplines.

  11. Human Identification at a Distance Using Body Shape Information

    Science.gov (United States)

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

    2013-12-01

    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.

  12. 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.

  13. Information Theoretic Studies and Assessment of Space Object Identification

    Science.gov (United States)

    2014-03-24

    AFRL-OSR-VA-TR-2014-0119 INFORMATION THEORETIC STUDIES AND ASSESMENTS OF SPACE-OBJECT IDENTIFICATION Sudhakar Prasad UNIVERSITY OF NEW MEXICO Final... computational -imaging approach to encoding the field depth of a target using the rotation of a point-spread function based on the orbital angular momentum (OAM... Computing the statistical entropy and associated MI requires the evaluation of statistical averages of logarithms of PDs and their ratios, as we have

  14. 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

  15. 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 processing...... 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....

  16. 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.

  17. 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.

  18. Post-processing for improving hyperspectral anomaly detection accuracy

    Science.gov (United States)

    Wu, Jee-Cheng; Jiang, Chi-Ming; Huang, Chen-Liang

    2015-10-01

    Anomaly detection is an important topic in the exploitation of hyperspectral data. Based on the Reed-Xiaoli (RX) detector and a morphology operator, this research proposes a novel technique for improving the accuracy of hyperspectral anomaly detection. Firstly, the RX-based detector is used to process a given input scene. Then, a post-processing scheme using morphology operator is employed to detect those pixels around high-scoring anomaly pixels. Tests were conducted using two real hyperspectral images with ground truth information and the results based on receiver operating characteristic curves, illustrated that the proposed method reduced the false alarm rates of the RXbased detector.

  19. Identifying 1 Method of Meat Containing Excessive Moisture Based on hyperspectral and SVM Multi-Information Fusion

    Directory of Open Access Journals (Sweden)

    Guo Peiyuan

    2016-01-01

    Full Text Available In this paper, a quick and accurate detection method which can identify whether the meat contain excessive moisture is mentioned. By using near-infrared spectroscopy measurement and SVM Multi-Information Fusion, the meat moisture content model has been established. In order to improve the accuracy of NIR measurement predicted model and to reduce the measurement sensitivity, utilizing image information and the PH value data as the parameters of the meat moisture content model. The study concluded that the theory and method can be further extended to the detection of other related meat agricultural products.

  20. 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.

  1. 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.

  2. Identification and separation of DNA mixtures using peak area information

    DEFF Research Database (Denmark)

    Cowell, R.G.; Lauritzen, Steffen Lilholt; Mortera, J.

    We show how probabilistic expert systems can be used to analyse forensic identification problems involving DNA mixture traces using quantitative peak area information. Peak area is modelled with conditional Gaussian distributions. The expert system can be used for scertaining whether individuals......, whose profiles have been measured, have contributed to the mixture, but also to predict DNA profiles of unknown contributors by separating the mixture into its individual components. The potential of our methodology is illustrated on case data examples and compared with alternative approaces...

  3. 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.

  4. 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.

  5. 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.

  6. 21 CFR 880.6300 - Implantable radiofrequency transponder system for patient identification and health information.

    Science.gov (United States)

    2010-04-01

    ... 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... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Implantable radiofrequency transponder system for...

  7. Random projection and SVD methods in hyperspectral imaging

    Science.gov (United States)

    Zhang, Jiani

    Hyperspectral imaging provides researchers with abundant information with which to study the characteristics of objects in a scene. Processing the massive hyperspectral imagery datasets in a way that efficiently provides useful information becomes an important issue. In this thesis, we consider methods which reduce the dimension of hyperspectral data while retaining as much useful information as possible. Traditional deterministic methods for low-rank approximation are not always adaptable to process huge datasets in an effective way, and therefore probabilistic methods are useful in dimension reduction of hyperspectral images. In this thesis, we begin by generally introducing the background and motivations of this work. Next, we summarize the preliminary knowledge and the applications of SVD and PCA. After these descriptions, we present a probabilistic method, randomized Singular Value Decomposition (rSVD), for the purposes of dimension reduction, compression, reconstruction, and classification of hyperspectral data. We discuss some variations of this method. These variations offer the opportunity to obtain a more accurate reconstruction of the matrix whose singular values decay gradually, to process matrices without target rank, and to obtain the rSVD with only one single pass over the original data. Moreover, we compare the method with Compressive-Projection Principle Component Analysis (CPPCA). From the numerical results, we can see that rSVD has better performance in compression and reconstruction than truncated SVD and CPPCA. We also apply rSVD to classification methods for the hyperspectral data provided by the National Geospatial-Intelligence Agency (NGA).

  8. Hyperspectral Fluorescence and Reflectance Imaging Instrument

    Science.gov (United States)

    Ryan, Robert E.; O'Neal, S. Duane; Lanoue, Mark; Russell, Jeffrey

    2008-01-01

    The system is a single hyperspectral imaging instrument that has the unique capability to acquire both fluorescence and reflectance high-spatial-resolution data that is inherently spatially and spectrally registered. Potential uses of this instrument include plant stress monitoring, counterfeit document detection, biomedical imaging, forensic imaging, and general materials identification. Until now, reflectance and fluorescence spectral imaging have been performed by separate instruments. Neither a reflectance spectral image nor a fluorescence spectral image alone yields as much information about a target surface as does a combination of the two modalities. Before this system was developed, to benefit from this combination, analysts needed to perform time-consuming post-processing efforts to co-register the reflective and fluorescence information. With this instrument, the inherent spatial and spectral registration of the reflectance and fluorescence images minimizes the need for this post-processing step. The main challenge for this technology is to detect the fluorescence signal in the presence of a much stronger reflectance signal. To meet this challenge, the instrument modulates artificial light sources from ultraviolet through the visible to the near-infrared part of the spectrum; in this way, both the reflective and fluorescence signals can be measured through differencing processes to optimize fluorescence and reflectance spectra as needed. The main functional components of the instrument are a hyperspectral imager, an illumination system, and an image-plane scanner. The hyperspectral imager is a one-dimensional (line) imaging spectrometer that includes a spectrally dispersive element and a two-dimensional focal plane detector array. The spectral range of the current imaging spectrometer is between 400 to 1,000 nm, and the wavelength resolution is approximately 3 nm. The illumination system consists of narrowband blue, ultraviolet, and other discrete

  9. CSP - Hyperspectral Imaging and Sounding of the Environment Meeting Scholarship Fund

    Science.gov (United States)

    2017-05-09

    completing and reviewing the ooll~ion of information . Send comments regarding lnis burden estimate or any Olher asp~ of this collection of information ...hyperspectral instrumentation and data analysis methods , to study geophysical and atmospheric phenomena, and to advance capabilities for anomaly- and...innovative researchers in hyperspectral instrumentation and data analysis methods , to study geophysical and atmospheric phenomena, and to advance

  10. Hyperspectral Image Recovery via Hybrid Regularization.

    Science.gov (United States)

    Arablouei, Reza; de Hoog, Frank

    2016-09-27

    Natural images tend to mostly consist of smooth regions with individual pixels having highly correlated spectra. This information can be exploited to recover hyperspectral images of natural scenes from their incomplete and noisy measurements. To perform the recovery while taking full advantage of the prior knowledge, we formulate a composite cost function containing a square-error data-fitting term and two distinct regularization terms pertaining to spatial and spectral domains. The regularization for the spatial domain is the sum of total-variation of the image frames corresponding to all spectral bands. The regularization for the spectral domain is the ��������-norm of the coefficient matrix obtained by applying a suitable sparsifying transform to the spectra of the pixels. We use an accelerated proximal-subgradient method to minimize the formulated cost function. We analyse the performance of the proposed algorithm and prove its convergence. Numerical simulations using real hyperspectral images exhibit that the proposed algorithm offers an excellent recovery performance with a number of measurements that is only a small fraction of the hyperspectral image data size. Simulation results also show that the proposed algorithm significantly outperforms an accelerated proximal-gradient algorithm that solves the classical basis-pursuit denoising problem to recover the hyperspectral image.

  11. Using Climate Information for Disaster Risk Identification in Sri Lanka

    Science.gov (United States)

    Zubair, L.

    2004-12-01

    We have engaged in a concerted attempt to undertake research and apply earth science information for development in Sri Lanka, with a focus on climate sciences. Here, we provide details of an ongoing attempt to harness science for disaster identification as a prelude to informed disaster management. Natural disasters not only result in death and destruction but also undermine decades of development gains as highlighted by recent examples from Sri Lanka. First, in May 2003, flooding and landslides in the South-West led to 260 deaths, damage to 120,000 homes and destruction of schools, infrastructure and agricultural land. Second, on December 26, 2000, a cyclone in the North-Central region left 8 dead, 55,000 displaced, with severe damage to fishing, agriculture, infrastructure and cultural sites. Third, an extended island-wide drought in 2001 and 2002 resulted in a 2% drop in GDP. In the aftermath of these disasters, improved disaster management has been deemed to be urgent by the Government of Sri Lanka. In the past the primary policy response to disasters was to provide emergency relief. It is increasingly recognized that appropriate disaster risk management, including risk assessment, preventive measures to reduce losses and improved preparedness, can help reduce death, destruction and socio-economic disruption. The overwhelming majority of hazards in Sri Lanka - droughts, floods, cyclones and landslides -have hydro-meteorological antecedents. Little systematic advantage has, however, been taken of hydro-meteorological information and advances in climate prediction for disaster management. Disaster risks are created by the interaction between hazard events and vulnerabilities of communities, infrastructure and economically important activities. A comprehensive disaster risk management system encompasses risk identification, risk reduction and risk transfer. We undertook an identification of risks for Sri Lanka at fine scale with the support of the Global Disaster

  12. Multimodal hyperspectral optical microscopy

    Energy Technology Data Exchange (ETDEWEB)

    Novikova, Irina V.; Smallwood, Chuck R.; Gong, Yu; Hu, Dehong; Hendricks, Leif; Evans, James E.; Bhattarai, Ashish; Hess, Wayne P.; El-Khoury, Patrick Z.

    2017-09-01

    We describe a unique and convenient approach to multimodal hyperspectral optical microscopy, herein achieved by coupling a portable and transferable hyperspectral imager to various optical microscopes. The experimental and data analysis schemes involved in recording spectrally and spatially resolved fluorescence, dark field, and optical absorption micrographs are illustrated through prototypical measurements targeting selected model systems. Namely, hyperspectral fluorescence micrographs of isolated fluorescent beads are employed to ensure spectral calibration of our detector and to gauge the attainable spatial resolution of our measurements; the recorded images are diffraction-limited. Moreover, spatially over-sampled absorption spectroscopy of a single lipid (18:1 Liss Rhod PE) layer reveals that optical densities on the order of 10-3 may be resolved by spatially averaging the recorded optical signatures. We also briefly illustrate two applications of our setup in the general areas of plasmonics and cell biology. Most notably, we deploy hyperspectral optical absorption microscopy to identify and image algal pigments within a single live Tisochrysis lutea cell. Overall, this work paves the way for multimodal multidimensional spectral imaging measurements spanning the realms of several scientific disciples.

  13. Hyperspectral data compression

    CERN Document Server

    Motta, Giovanni; Storer, James A

    2006-01-01

    Provides a survey of results in the field of compression of remote sensed 3D data, with a particular interest in hyperspectral imagery. This work covers topics such as compression architecture, lossless compression, lossy techniques, and more. It also describes a lossless algorithm based on vector quantization.

  14. Recent Developments in Hyperspectral Imaging for Assessment of Food Quality and Safety

    Directory of Open Access Journals (Sweden)

    Hui Huang

    2014-04-01

    Full Text Available Hyperspectral imaging which combines imaging and spectroscopic technology is rapidly gaining ground as a non-destructive, real-time detection tool for food quality and safety assessment. Hyperspectral imaging could be used to simultaneously obtain large amounts of spatial and spectral information on the objects being studied. This paper provides a comprehensive review on the recent development of hyperspectral imaging applications in food and food products. The potential and future work of hyperspectral imaging for food quality and safety control is also discussed.

  15. Improved hyperspectral imaging technologies Project

    Data.gov (United States)

    National Aeronautics and Space Administration — Improved hyperspectral imaging technologies could enable lower-cost analysis for planetary science including atmospheric studies, mineralogical investigations, and...

  16. Hyperspectral remote sensing: A new approach for oil spill detection and analysis

    Science.gov (United States)

    Salem, Foudan Mohamed Fathy

    Remote sensing technology is an important tool for monitoring, detecting, and analyzing oil spills. Researchers have explored the use of digital imagery acquired from airborne and spaceborne platforms for monitoring oil spills and for analyzing changes to oil spill thickness and contaminated areas. However, traditional digital imagery from multispectral scanners is subject to limitations of spatial and spectral resolution. A new type of remote sensing, known as "hyperspectral sensors," promises to revolutionize the use of remotely sensed data for a variety of applications including mapping and monitoring oil spills by eliminating the limitations of multispectral scanners. With hyperspectral sensors, it is possible to map oil spills of different types and thicknesses, as well as to detect subtle changes in oil-contaminated wetlands such as complex contaminated wet soil and vegetation. However, despite the great promise they offer, these sensors introduce a host of problems which must be addressed before they can be routinely used in oil spill applications. For example, statistical analysis techniques commonly used to process multispectral data are not suited to the amount and dimensionality of data present in a hyperspectral image. The large volume of data, along with the CPU-intensive algorithms required to derive information from hyperspectral data, make it difficult to extract useful information. This dissertation describes the spectral and spatial characteristics of hyperspectral data and the potential work of these data for oil spill detection and environmental applications. The advantages and disadvantages of these data for oil spills in fresh and sea water and contaminated wetlands are discussed. Furthermore, application of the Airborne Imaging Spectrometer for Applications (AISA) and the Airborne Visible/Infrared Imagery Spectrometer (AVIRIS) data for oil spill image classification is used. Two case studies are considered, the first focusing on the

  17. Multispectral and hyperspectral images invariant to illumination

    OpenAIRE

    Yazdani Salekdeh, Amin

    2011-01-01

    In this thesis a novel method is proposed that makes use of multispectral and hyperspectral image data to generate a novel photometric-invariant spectral image. For RGB colour image, an illuminant-invariant image was constructed independent of the illuminant and shading. To generate this image either a set of calibration images was required, or entropy information from a single image was used. For spectral images we show that photometric-invariant image formation is in essence greatly simplif...

  18. 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.

  19. Compact infrared hyperspectral imaging polarimeter

    Science.gov (United States)

    Craven, Julia; Kudenov, Michael W.; Stapelbroek, Maryn G.; Dereniak, Eustace L.

    2010-04-01

    A compact SWIR/MWIR infrared hyperspectral imaging polarimeter (IHIP) is currently under development at the Optical Detection Lab at the University of Arizona. The sensor uses a pair of sapphire Wollaston prisms and high order retarders to form an imaging birefringent Fourier transform spectropolarimeter. Polarimetric data are acquired through the use of channeled spectropolarimetry to modulate the spectrum with the Stokes parameter information. The two dimensional interferogram is Fourier filtered and reconstructed to recover the complete Stokes vector data across the image. The IHIP operates over a +/-5° field of view and will use a dual-scan false signature reduction technique to suppress polarimetric aliasing artifacts. We present current instrument development progress, initial laboratory results, and our plan for future work.

  20. HYPERSPECTRAL ANOMALY DETECTION IN URBAN SCENARIOS

    Directory of Open Access Journals (Sweden)

    J. G. Rejas Ayuga

    2016-06-01

    Full Text Available We have studied the spectral features of reflectance and emissivity in the pattern recognition of urban materials in several single hyperspectral scenes through a comparative analysis of anomaly detection methods and their relationship with city surfaces with the aim to improve information extraction processes. Spectral ranges of the visible-near infrared (VNIR, shortwave infrared (SWIR and thermal infrared (TIR from hyperspectral data cubes of AHS sensor and HyMAP and MASTER of two cities, Alcalá de Henares (Spain and San José (Costa Rica respectively, have been used. In this research it is assumed no prior knowledge of the targets, thus, the pixels are automatically separated according to their spectral information, significantly differentiated with respect to a background, either globally for the full scene, or locally by image segmentation. Several experiments on urban scenarios and semi-urban have been designed, analyzing the behaviour of the standard RX anomaly detector and different methods based on subspace, image projection and segmentation-based anomaly detection methods. A new technique for anomaly detection in hyperspectral data called DATB (Detector of Anomalies from Thermal Background based on dimensionality reduction by projecting targets with unknown spectral signatures to a background calculated from thermal spectrum wavelengths is presented. First results and their consequences in non-supervised classification and extraction information processes are discussed.

  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. 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.

  3. Albedo recovery for hyperspectral image classification

    Science.gov (United States)

    Zhan, Kun; Wang, Haibo; Xie, Yuange; Zhang, Chutong; Min, Yufang

    2017-07-01

    Image intensity value is determined by both the albedo component and the shading component. The albedo component describes the physical nature of different objects at the surface of the earth, and land-cover classes are different from each other because of their intrinsic physical materials. We, therefore, recover the intrinsic albedo feature of the hyperspectral image to exploit the spatial semantic information. Then, we use the support vector machine (SVM) to classify the recovered intrinsic albedo hyperspectral image. The SVM tries to maximize the minimum margin to achieve good generalization performance. Experimental results show that the SVM with the intrinsic albedo feature method achieves a better classification performance than the state-of-the-art methods in terms of visual quality and three quantitative metrics.

  4. 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.

  5. A DIVERSIFIED DEEP BELIEF NETWORK FOR HYPERSPECTRAL IMAGE CLASSIFICATION

    Directory of Open Access Journals (Sweden)

    P. Zhong

    2016-06-01

    Full Text Available In recent years, researches in remote sensing demonstrated that deep architectures with multiple layers can potentially extract abstract and invariant features for better hyperspectral image classification. Since the usual real-world hyperspectral image classification task cannot provide enough training samples for a supervised deep model, such as convolutional neural networks (CNNs, this work turns to investigate the deep belief networks (DBNs, which allow unsupervised training. The DBN trained over limited training samples usually has many “dead” (never responding or “potential over-tolerant” (always responding latent factors (neurons, which decrease the DBN’s description ability and thus finally decrease the hyperspectral image classification performance. This work proposes a new diversified DBN through introducing a diversity promoting prior over the latent factors during the DBN pre-training and fine-tuning procedures. The diversity promoting prior in the training procedures will encourage the latent factors to be uncorrelated, such that each latent factor focuses on modelling unique information, and all factors will be summed up to capture a large proportion of information and thus increase description ability and classification performance of the diversified DBNs. The proposed method was evaluated over the well-known real-world hyperspectral image dataset. The experiments demonstrate that the diversified DBNs can obtain much better results than original DBNs and comparable or even better performances compared with other recent hyperspectral image classification methods.

  6. Synergetics Framework for Hyperspectral Image Classification

    Science.gov (United States)

    Müller, R.; Cerra, D.; Reinartz, P.

    2013-05-01

    In this paper a new classification technique for hyperspectral data based on synergetics theory is presented. Synergetics - originally introduced by the physicist H. Haken - is an interdisciplinary theory to find general rules for pattern formation through selforganization and has been successfully applied in fields ranging from biology to ecology, chemistry, cosmology, and thermodynamics up to sociology. Although this theory describes general rules for pattern formation it was linked also to pattern recognition. Pattern recognition algorithms based on synergetics theory have been applied to images in the spatial domain with limited success in the past, given their dependence on the rotation, shifting, and scaling of the images. These drawbacks can be discarded if such methods are applied to data acquired by a hyperspectral sensor in the spectral domain, as each single spectrum, related to an image element in the hyperspectral scene, can be analysed independently. The classification scheme based on synergetics introduces also methods for spatial regularization to get rid of "salt and pepper" classification results and for iterative parameter tuning to optimize class weights. The paper reports an experiment on a benchmark data set frequently used for method comparisons. This data set consists of a hyperspectral scene acquired by the Airborne Visible Infrared Imaging Spectrometer AVIRIS sensor of the Jet Propulsion Laboratory acquired over the Salinas Valley in CA, USA, with 15 vegetation classes. The results are compared to state-of-the-art methodologies like Support Vector Machines (SVM), Spectral Information Divergence (SID), Neural Networks, Logistic Regression, Factor Graphs or Spectral Angle Mapper (SAM). The outcomes are promising and often outperform state-of-the-art classification methodologies.

  7. 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.

  8. Hyperspectral image classification based on filtering: a comparative study

    Science.gov (United States)

    Cao, Xianghai; Ji, Beibei; Ji, Yamei; Wang, Lin; Jiao, Licheng

    2017-07-01

    The classification of hyperspectral images benefits greatly from integration of spectral information and spatial context. There have been many means to incorporate spatial information into the classification, such as the Markov random field, extended morphological profiles, and segmentation-based methods. Recently, spatial filtering was introduced to improve the classification accuracy of hyperspectral images. Compared with other spectral-spatial algorithms, spatial filtering is simple and easy to implement. This advantage makes it suitable for practical applications. However, spatial filtering has not been given enough attention. A comprehensive comparative study of spatial filtering is conducted. Specifically, 10 kinds of filters are used to smooth the hyperspectral images and the classified maps, respectively. The experimental results show that most filtering-based classification methods perform well with high efficiency.

  9. Hyperspectral optical diffraction tomography

    CERN Document Server

    Jung, JaeHwang; Yoon, Jonghee; Park, YongKeun

    2015-01-01

    Here, we present a novel microscopic technique for measuring wavelength-dependent three-dimensional (3-D) distributions of the refractive indices (RIs) of microscopic samples in the visible wavelengths. Employing 3-D quantitative phase microscopy techniques with a wavelength-swept source, 3-D RI tomograms were obtained in the range of 450 - 700 nm with a spectral resolution of a few nanometers. The capability of the technique was demonstrated by measuring the hyperspectral 3-D RI tomograms of polystyrene beads, human red blood cells, and hepatocytes. The results demonstrate the potential for label-free molecular specific 3-D tomography of biological samples.

  10. Using Visible Spectral Information to Predict Long-Wave Infrared Spectral Emissivity: A Case Study over the Sokolov Area of the Czech Republic with an Airborne Hyperspectral Scanner Sensor

    Directory of Open Access Journals (Sweden)

    Gila Notesco

    2013-11-01

    Full Text Available Remote-sensing platforms are often comprised of a cluster of different spectral range detectors or sensors to benefit from the spectral identification capabilities of each range. Missing data from these platforms, caused by problematic weather conditions, such as clouds, sensor failure, low temporal coverage or a narrow field of view (FOV, is one of the problems preventing proper monitoring of the Earth. One of the possible solutions is predicting a detector or sensor’s missing data using another detector/sensor. In this paper, we propose a new method of predicting spectral emissivity in the long-wave infrared (LWIR spectral region using the visible (VIS spectral region. The proposed method is suitable for two main scenarios of missing data: sensor malfunctions and narrow FOV. We demonstrate the usefulness and limitations of this prediction scheme using the airborne hyperspectral scanner (AHS sensor, which consists of both VIS and LWIR spectral regions, in a case study over the Sokolov area, Czech Republic.

  11. 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

  12. 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.

  13. Mineral mapping based on independent component analysis for spaceborne hyperspectral data

    Science.gov (United States)

    Li, Na; Zhao, Huijie; Jia, Guorui; Dong, Chao; Wang, Runsheng; Yan, Bokun

    2007-11-01

    Independent component analysis (ICA) model is proposed to extract alteration minerals using spaceborne hyperspectral data, because the result of present methods for alteration minerals identification are affected easily by the factors including excursion and variation of spectral signatures, interference of environment conditions and insufficient spectral library. In our work, the proposed method can realize extraction of alteration minerals under condition that the prior information of mineral spectra is unknown and the background model is not built. Therefore, this method is successfully applied to spaceborne Hyperion data at Qulong district of Tibet, and the application result in our work is approximately in accord with the geological map. Four kinds of minerals have been identified, which include kaolinite, chlorite, rich-aluminium sericite, and poor-aluminium sericite. And then the result that is obtained by ICA model can illuminate the validity and practicability of the proposed method and can provide some useful information and direction for the prognostication of mineral resource.

  14. Identification and Management of Information Problems by Emergency Department Staff

    Science.gov (United States)

    Murphy, Alison R.; Reddy, Madhu C.

    2014-01-01

    Patient-care teams frequently encounter information problems during their daily activities. These information problems include wrong, outdated, conflicting, incomplete, or missing information. Information problems can negatively impact the patient-care workflow, lead to misunderstandings about patient information, and potentially lead to medical errors. Existing research focuses on understanding the cause of these information problems and the impact that they can have on the hospital’s workflow. However, there is limited research on how patient-care teams currently identify and manage information problems that they encounter during their work. Through qualitative observations and interviews in an emergency department (ED), we identified the types of information problems encountered by ED staff, and examined how they identified and managed the information problems. We also discuss the impact that these information problems can have on the patient-care teams, including the cascading effects of information problems on workflow and the ambiguous accountability for fixing information problems within collaborative teams. PMID:25954457

  15. 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.

  16. 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.

  17. 76 FR 59660 - Proposed Information Collection; Comment Request; Permitting, Vessel Identification, and Vessel...

    Science.gov (United States)

    2011-09-27

    ...; Permitting, Vessel Identification, and Vessel Monitoring System Requirements for the Commercial Bottomfish... compliance with federal identification requirements and carry and maintain a satellite- based vessel monitoring system (VMS). This collection of information is needed for permit issuance, to identify actual or...

  18. Total variation regularization via continuation to recover compressed hyperspectral images.

    Science.gov (United States)

    Eason, Duncan T; Andrews, Mark

    2015-01-01

    In this paper, we investigate a low-complexity scheme for decoding compressed hyperspectral image data. We have exploited the simplicity of the subgradient method by modifying a total variation-based regularization problem to include a residual constraint, employing convex optimality conditions to provide equivalency between the original and reformed problem statements. A scheme that utilizes spectral smoothness by calculating informed starting points to improve the rate of convergence is introduced. We conduct numerical experiments, using both synthetic and real hyperspectral data, to demonstrate the effectiveness of the reconstruction algorithm and the validity of our method for exploiting spectral smoothness. Evidence from these experiments suggests that the proposed methods have the potential to improve the quality and run times of the future compressed hyperspectral image reconstructions.

  19. 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), ВТН (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

  20. Hyperspectral image analysis. A tutorial

    Energy Technology Data Exchange (ETDEWEB)

    Amigo, José Manuel, E-mail: jmar@food.ku.dk [Spectroscopy and Chemometrics Group, Department of Food Sciences, Faculty of Science, University of Copenhagen, Rolighedsvej 30, Frederiksberg C DK–1958 (Denmark); Babamoradi, Hamid [Spectroscopy and Chemometrics Group, Department of Food Sciences, Faculty of Science, University of Copenhagen, Rolighedsvej 30, Frederiksberg C DK–1958 (Denmark); Elcoroaristizabal, Saioa [Spectroscopy and Chemometrics Group, Department of Food Sciences, Faculty of Science, University of Copenhagen, Rolighedsvej 30, Frederiksberg C DK–1958 (Denmark); Chemical and Environmental Engineering Department, School of Engineering, University of the Basque Country, Alameda de Urquijo s/n, E-48013 Bilbao (Spain)

    2015-10-08

    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.

  1. Anomaly Detection from Hyperspectral Remote Sensing Imagery

    Directory of Open Access Journals (Sweden)

    Qiandong Guo

    2016-12-01

    Full Text Available Hyperspectral remote sensing imagery contains much more information in the spectral domain than does multispectral imagery. The consecutive and abundant spectral signals provide a great potential for classification and anomaly detection. In this study, two real hyperspectral data sets were used for anomaly detection. One data set was an Airborne Visible/Infrared Imaging Spectrometer (AVIRIS data covering the post-attack World Trade Center (WTC and anomalies are fire spots. The other data set called SpecTIR contained fabric panels as anomalies compared to their background. Existing anomaly detection algorithms including the Reed–Xiaoli detector (RXD, the blocked adaptive computation efficient outlier nominator (BACON, the random selection based anomaly detector (RSAD, the weighted-RXD (W-RXD, and the probabilistic anomaly detector (PAD are reviewed here. The RXD generally sets strict assumptions to the background, which cannot be met in many scenarios, while BACON, RSAD, and W-RXD employ strategies to optimize the estimation of background information. The PAD firstly estimates both background information and anomaly information and then uses the information to conduct anomaly detection. Here, the BACON, RSAD, W-RXD, and PAD outperformed the RXD in terms of detection accuracy, and W-RXD and PAD required less time than BACON and RSAD.

  2. 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

  3. 77 FR 20889 - Proposed Information Collection (Request One-VA Identification Verification Card) Activity...

    Science.gov (United States)

    2012-04-06

    ... AFFAIRS Proposed Information Collection (Request One-VA Identification Verification Card) Activity... certain information by the agency. Under the Paperwork Reduction Act (PRA) of 1995, Federal agencies are required to publish notice in the Federal Register concerning each proposed collection of information...

  4. 77 FR 38396 - Agency Information Collection (One-VA Identification Verification Card) Activities Under OMB Review

    Science.gov (United States)

    2012-06-27

    ... AFFAIRS Agency Information Collection (One-VA Identification Verification Card) Activities Under OMB... Affairs, will submit the collection of information abstracted below to the Office of Management and Budget (OMB) for review and comment. The PRA submission describes the nature of the information collection and...

  5. 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

  6. Estimating canopy water content using hyperspectral remote sensing data

    NARCIS (Netherlands)

    Clevers, J.G.P.W.; Kooistra, L.; Schaepman, M.E.

    2010-01-01

    Hyperspectral remote sensing has demonstrated great potential for accurate retrieval of canopy water content (CWC). This CWC is defined by the product of the leaf equivalent water thickness (EWT) and the leaf area index (LAI). In this paper, in particular the spectral information provided by the

  7. Tree Species Classification Using Hyperspectral Imagery: A Comparison of Two Classifiers

    Directory of Open Access Journals (Sweden)

    Laurel Ballanti

    2016-05-01

    Full Text Available The identification of tree species can provide a useful and efficient tool for forest managers for planning and monitoring purposes. Hyperspectral data provide sufficient spectral information to classify individual tree species. Two non-parametric classifiers, support vector machines (SVM and random forest (RF, have resulted in high accuracies in previous classification studies. This research takes a comparative classification approach to examine the SVM and RF classifiers in the complex and heterogeneous forests of Muir Woods National Monument and Kent Creek Canyon in Marin County, California. The influence of object- or pixel-based training samples and segmentation size on the object-oriented classification is also explored. To reduce the data dimensionality, a minimum noise fraction transform was applied to the mosaicked hyperspectral image, resulting in the selection of 27 bands for the final classification. Each classifier was also assessed individually to identify any advantage related to an increase in training sample size or an increase in object segmentation size. All classifications resulted in overall accuracies above 90%. No difference was found between classifiers when using object-based training samples. SVM outperformed RF when additional training samples were used. An increase in training samples was also found to improve the individual performance of the SVM classifier.

  8. Detection of Honey Adulteration using Hyperspectral Imaging

    NARCIS (Netherlands)

    Shafiee, Sahameh; Polder, Gerrit; Minaei, Saeid; Moghadam-charkari, Nasrolah; Ruth, Van Saskia; Kuś, Piotr M.

    2016-01-01

    This study investigates the application of hyperspectral imaging system and data mining based classifiers for honey adulteration detection. Hyperspectral images from pure and adulterated samples were captured in using a VIS-NIR hyperspectral camera (400 – 1000 nm). After preprocessing the images,

  9. Hyperspectral Image Analysis of Food Quality

    DEFF Research Database (Denmark)

    Arngren, Morten

    , the visualisation and interpretation of hyperspectral images are discussed.A Bayesian based unmixing method is presented as a novel approachto decompose a hyperspectral image into interpretable components. Secondly,hyperspectral imaging is applied to a dedicated application of predicting the degreeof pre-germination...

  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 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.

  12. Investigation of Spectral Characteristics of Urban Surface Materials Using Field Measurements and Hyperspectral HyMap Data

    Science.gov (United States)

    Heiden, U.; Roessner, S.; Segl, K.; Kaufmann, H.

    2002-12-01

    Recent developments of remote sensing technologies allow the acquisition of data with a high spectral and spatial resolution. The high information content of hyperspectral data enables the detailed analysis urban surface materials. This yields the potential for an automated identification of urban surface materials based on their spectral shape. The base for such an analysis is a detailed knowledge about spectral characteristics of urban surface materials. This study aims at investigating the spectral characteristics of urban surface materials throughout the reflective wavelength range. For this purpose urban surface materials have been systematically measured with a field spectrometer. Furthermore the measurements have been analyzed and stored in a spectral library. For the systematic assessment of materials the urban surface is categorized in regard to its degree of surface sealing resulting in urban surface cover types. The resulting categories form the thematic frame for the assessment of urban surface materials. Sealed surfaces are analyzed in terms of variations in material and color. Non-sealed surfaces, such as soil and vegetation are investigated in regard to their special urban properties. The obtained spectral library is used to explore the spectral information content of airborne hyperspectral HyMap data, which have been aquired for the study area of Dresden, Germany. In the result 79 spectral classes could be distinguished based on these data. They represent spectrally different materials (e.g. zinc and asphalt). They also contain spectral variations of the same material, which are caused by different roof geometries, color and other coating, age and intensity of use. The results of this investigation show the high spectral variability of urban surface materials in the field measurements and in the hyperspectral data. This high information content yields the potential for area-wide automated assessment of urban surface materials.

  13. 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

  14. Evaluation of the effectiveness of laser crust removal on granites by means of hyperspectral imaging techniques

    Energy Technology Data Exchange (ETDEWEB)

    Pozo-Antonio, J.S., E-mail: santiago.pozo@udc.es [Laboratorio de Aplicacións Industriais do Láser, Centro de Investigacións Tecnolóxicas (CIT), Departamento de Enxeñaría Industrial II, Escola Politécnica Superior, Universidade de Coruña (UDC), Campus Ferrol, 15403 Ferrol (Spain); Fiorucci, M.P.; Ramil, A.; López, A.J. [Laboratorio de Aplicacións Industriais do Láser, Centro de Investigacións Tecnolóxicas (CIT), Departamento de Enxeñaría Industrial II, Escola Politécnica Superior, Universidade de Coruña (UDC), Campus Ferrol, 15403 Ferrol (Spain); Rivas, T. [Departamento de Enxeñaría dos Recursos Naturais e Medioambiente, Escola Superior de Minas, Universidade de Vigo, 36310 Vigo (Spain)

    2015-08-30

    Highlights: • Hyperspectral imaging techniques for determining the degree of crust removal on granites used in Cultural Heritage. • Hyperspectral imaging techniques allow to in situ evaluate of the effectiveness of the laser cleaning. • Hyperspectral imaging data are consistent with the information obtained by conventional techniques about the cleaning effectiveness. - Abstract: In this paper, we present a study of the application of the hyperspectral imaging technique in order to non-destructively evaluate the laser cleaning of the biogenic patina and the sulphated black crust developed on a fine-grained granite used in the construction of Cultural Heritage in NW Spain. The grained polymineral texture of the granite hinders the adjustment of laser irradiation parameters during the cleaning, and therefore the in situ process control. The cleaning was performed with a nanosecond pulsed Nd:YVO{sub 4} laser at 355 nm. A hyperspectral camera was used to in situ assess the effectiveness of cleaning by recording images of the rock surfaces before and during the laser treatment. Different analytical techniques were used to test the ability of the hyperspectral imaging technique to evaluate the cleaning process of the granite samples: optical microscopy, scanning electron microscopy with energy dispersive X-ray spectrometry (SEM - EDX), Fourier transform infrared spectroscopy (FTIR) and spectrophotometer colour measurements. The results indicated that hyperspectral imaging technique is a reliable and more affordable technique to in situ evaluate the process of laser cleaning of the biogenic patina and the sulphated black crust in fine-grained granites.

  15. Icon Based Information Retrieval and Disease Identification in Agriculture

    OpenAIRE

    Mittal, Namita; Agarwal, Basant; Gupta, Ajay; Madhur, Hemant

    2014-01-01

    Recent developments in the ICT industry in past few decades has enabled the quick and easy access to the information available on the internet. But, digital literacy is the pre-requisite for its use. The main purpose of this paper is to provide an interface for digitally illiterate users, especially farmers to efficiently and effectively retrieve information through Internet. In addition, to enable the farmers to identify the disease in their crop, its cause and symptoms using digital image p...

  16. Sparsely-sampled hyperspectral stimulated Raman scattering microscopy: a theoretical investigation

    Science.gov (United States)

    Lin, Haonan; Liao, Chien-Sheng; Wang, Pu; Huang, Kai-Chih; Bouman, Charles A.; Kong, Nan; Cheng, Ji-Xin

    2017-02-01

    A hyperspectral image corresponds to a data cube with two spatial dimensions and one spectral dimension. Through linear un-mixing, hyperspectral images can be decomposed into spectral signatures of pure components as well as their concentration maps. Due to this distinct advantage on component identification, hyperspectral imaging becomes a rapidly emerging platform for engineering better medicine and expediting scientific discovery. Among various hyperspectral imaging techniques, hyperspectral stimulated Raman scattering (HSRS) microscopy acquires data in a pixel-by-pixel scanning manner. Nevertheless, current image acquisition speed for HSRS is insufficient to capture the dynamics of freely moving subjects. Instead of reducing the pixel dwell time to achieve speed-up, which would inevitably decrease signal-to-noise ratio (SNR), we propose to reduce the total number of sampled pixels. Location of sampled pixels are carefully engineered with triangular wave Lissajous trajectory. Followed by a model-based image in-painting algorithm, the complete data is recovered for linear unmixing. Simulation results show that by careful selection of trajectory, a fill rate as low as 10% is sufficient to generate accurate linear unmixing results. The proposed framework applies to any hyperspectral beam-scanning imaging platform which demands high acquisition speed.

  17. Superpixel sparse representation for target detection in hyperspectral imagery

    Science.gov (United States)

    Dong, Chunhua; Naghedolfeizi, Masoud; Aberra, Dawit; Qiu, Hao; Zeng, Xiangyan

    2017-05-01

    Sparse Representation (SR) is an effective classification method. Given a set of data vectors, SR aims at finding the sparsest representation of each data vector among the linear combinations of the bases in a given dictionary. In order to further improve the classification performance, the joint SR that incorporates interpixel correlation information of neighborhoods has been proposed for image pixel classification. However, SR and joint SR demand significant amount of computational time and memory, especially when classifying a large number of pixels. To address this issue, we propose a superpixel sparse representation (SSR) algorithm for target detection in hyperspectral imagery. We firstly cluster hyperspectral pixels into nearly uniform hyperspectral superpixels using our proposed patch-based SLIC approach based on their spectral and spatial information. The sparse representations of these superpixels are then obtained by simultaneously decomposing superpixels over a given dictionary consisting of both target and background pixels. The class of a hyperspectral pixel is determined by a competition between its projections on target and background subdictionaries. One key advantage of the proposed superpixel representation algorithm with respect to pixelwise and joint sparse representation algorithms is that it reduces computational cost while still maintaining competitive classification performance. We demonstrate the effectiveness of the proposed SSR algorithm through experiments on target detection in the in-door and out-door scene data under daylight illumination as well as the remote sensing data. Experimental results show that SSR generally outperforms state of the art algorithms both quantitatively and qualitatively.

  18. 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

  19. DETERMINATION OF OPTIMUM CLASSIFICATION SYSTEM FOR HYPERSPECTRAL IMAGERY AND LIDAR DATA BASED ON BEES ALGORITHM

    Directory of Open Access Journals (Sweden)

    F. Samadzadega

    2015-12-01

    Full Text Available Hyperspectral imagery is a rich source of spectral information and plays very important role in discrimination of similar land-cover classes. In the past, several efforts have been investigated for improvement of hyperspectral imagery classification. Recently the interest in the joint use of LiDAR data and hyperspectral imagery has been remarkably increased. Because LiDAR can provide structural information of scene while hyperspectral imagery provide spectral and spatial information. The complementary information of LiDAR and hyperspectral data may greatly improve the classification performance especially in the complex urban area. In this paper feature level fusion of hyperspectral and LiDAR data is proposed where spectral and structural features are extract from both dataset, then hybrid feature space is generated by feature stacking. Support Vector Machine (SVM classifier is applied on hybrid feature space to classify the urban area. In order to optimize the classification performance, two issues should be considered: SVM parameters values determination and feature subset selection. Bees Algorithm (BA is powerful meta-heuristic optimization algorithm which is applied to determine the optimum SVM parameters and select the optimum feature subset simultaneously. The obtained results show the proposed method can improve the classification accuracy in addition to reducing significantly the dimension of feature space.

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

    OpenAIRE

    Nikica Budimir; Jill Slay

    2007-01-01

    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 w...

  1. 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

  2. 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 ...

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

    African Journals Online (AJOL)

    Jane

    2011-08-17

    Aug 17, 2011 ... Key words: Information and communication technologies (ICT), tools, food security, rural households, agricultural ... New ICTs. This group consists of computers, satellites, one-on-one connections, wireless phones (mobile), the internet, e- mail, the web, internet services, video conferences, CD-. ROMs ...

  4. 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...

  5. Recent advances and applications of hyperspectral imaging for fruit and vegetable quality assessment

    OpenAIRE

    Lorente, D.; Aleixos Borrás, María Nuria; GÓMEZ SANCHÍS, JUAN; Cubero, S.; GARCÍA NAVARRETE, OSCAR LEONARDO; BLASCO IVARS, JOSE

    2011-01-01

    Hyperspectral imaging systems are starting to be used as a scientific tool for food quality assessment. A typical hyperspectral image is composed of a set of a relatively wide range of monochromatic images corresponding to continuous wavelengths that normally contain redundant information or may exhibit a high degree of correlation. In addition, computation of the classifiers used to deal with the data obtained from the images can become excessively complex and time-consuming for such high-di...

  6. Emotion identification method using RGB information of human face

    Science.gov (United States)

    Kita, Shinya; Mita, Akira

    2015-03-01

    Recently, the number of single households is drastically increased due to the growth of the aging society and the diversity of lifestyle. Therefore, the evolution of building spaces is demanded. Biofied Building we propose can help to avoid this situation. It helps interaction between the building and residents' conscious and unconscious information using robots. The unconscious information includes emotion, condition, and behavior. One of the important information is thermal comfort. We assume we can estimate it from human face. There are many researchs about face color analysis, but a few of them are conducted in real situations. In other words, the existing methods were not used with disturbance such as room lumps. In this study, Kinect was used with face-tracking. Room lumps and task lumps were used to verify that our method could be applicable to real situation. In this research, two rooms at 22 and 28 degrees C were prepared. We showed that the transition of thermal comfort by changing temperature can be observed from human face. Thus, distinction between the data of 22 and 28 degrees C condition from face color was proved to be possible.

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

    Directory of Open Access Journals (Sweden)

    Omotola Osunrinde

    2017-01-01

    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.

  8. 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.

  9. 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

  10. 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.

  11. [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

  12. 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.

  13. Hyperspectral imaging from space: Warfighter-1

    Science.gov (United States)

    Cooley, Thomas; Seigel, Gary; Thorsos, Ivan

    1999-01-01

    The Air Force Research Laboratory Integrated Space Technology Demonstrations (ISTD) Program Office has partnered with Orbital Sciences Corporation (OSC) to complement the commercial satellite's high-resolution panchromatic imaging and Multispectral imaging (MSI) systems with a moderate resolution Hyperspectral imaging (HSI) spectrometer camera. The program is an advanced technology demonstration utilizing a commercially based space capability to provide unique functionality in remote sensing technology. This leveraging of commercial industry to enhance the value of the Warfighter-1 program utilizes the precepts of acquisition reform and is a significant departure from the old-school method of contracting for government managed large demonstration satellites with long development times and technology obsolescence concerns. The HSI system will be able to detect targets from the spectral signature measured by the hyperspectral camera. The Warfighter-1 program will also demonstrate the utility of the spectral information to theater military commanders and intelligence analysts by transmitting HSI data directly to a mobile ground station that receives and processes the data. After a brief history of the project origins, this paper will present the details of the Warfighter-1 system and expected results from exploitation of HSI data as well as the benefits realized by this collaboration between the Air Force and commercial industry.

  14. 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.

  15. [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.

  16. Novel feature extraction method for hyperspectral remote sensing image

    Science.gov (United States)

    Liu, Chunhong; Zhao, Huijie

    2007-11-01

    In order to reduce high dimensions of hyperspectral remote sensing image and concentrate optimal information to reduced bands, this paper proposed a new method of feature extraction. The new method has two steps. The first step is to reduce the high dimensions by selecting high informative and low correlative bands according to the indexes calculated by a smart band selection method. The criterions that SBS method complied are: (1) The selected bands have the most information; (2) The selected bands have the smallest correlation with other bands. The second step is to decompose the selected bands by a novel second generation wavelet, predicting and updating subimages on rectangle and quincunx grids by Neville filters, finally using variance weighting as fusion weight. A 126-band HYMAP hyperspectral data was experimented in order to test the effect of the new method. The results showed classification accuracy is increased by using the novel feature extraction method.

  17. High-sensitivity hyperspectral imager for biomedical video diagnostic applications

    Science.gov (United States)

    Leitner, Raimund; Arnold, Thomas; De Biasio, Martin

    2010-04-01

    Video endoscopy allows physicians to visually inspect inner regions of the human body using a camera and only minimal invasive optical instruments. It has become an every-day routine in clinics all over the world. Recently a technological shift was done to increase the resolution from PAL/NTSC to HDTV. But, despite a vast literature on invivo and in-vitro experiments with multi-spectral point and imaging instruments that suggest that a wealth of information for diagnostic overlays is available in the visible spectrum, the technological evolution from colour to hyper-spectral video endoscopy is overdue. There were two approaches (NBI, OBI) that tried to increase the contrast for a better visualisation by using more than three wavelengths. But controversial discussions about the real benefit of a contrast enhancement alone, motivated a more comprehensive approach using the entire spectrum and pattern recognition algorithms. Up to now the hyper-spectral equipment was too slow to acquire a multi-spectral image stack at reasonable video rates rendering video endoscopy applications impossible. Recently, the availability of fast and versatile tunable filters with switching times below 50 microseconds made an instrumentation for hyper-spectral video endoscopes feasible. This paper describes a demonstrator for hyper-spectral video endoscopy and the results of clinical measurements using this demonstrator for measurements after otolaryngoscopic investigations and thorax surgeries. The application investigated here is the detection of dysplastic tissue, although hyper-spectral video endoscopy is of course not limited to cancer detection. Other applications are the detection of dysplastic tissue or polyps in the colon or the gastrointestinal tract.

  18. 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.

  19. Hyperspectral Technique for Detecting Soil Parameters

    Science.gov (United States)

    Garfagnoli, F.; Ciampalini, A.; Moretti, S.; Chiarantini, L.

    2011-12-01

    -georeferred VNIR/SWIR DN values. Then, airborne imagery needed to be corrected for the influence of the atmosphere, solar illumination, sensor viewing geometry and terrain geometry information, for the retrieval of inherent surface reflectance properties. The geocoded products were obtained for each flight line by using a procedure developed in IDL Language and PARGE (PARametric Geocoding) software. When all compensation parameters were applied to hyperspectral data or to the final thematic map, orthorectified, georeferred and coregistered VNIR to SWIR images or maps were available for GIS application and 3D view as well as for the retrieval of different geophysical parameters by means of inversion algorithms. The experimental fitting of laboratory data on mineral content is used for airborne data inversion, whose results are in agreement with laboratory records, demonstrating the possibility to use this methodology for digital mapping of soil properties. In this study, we established a complete procedure for mapping clay content areal variations in agricultural soils starting form airborne hyperspectral imagery.

  20. 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.

  1. 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).

  2. Objective color classification of ecstasy tablets by hyperspectral imaging.

    Science.gov (United States)

    Edelman, Gerda; Lopatka, Martin; Aalders, Maurice

    2013-07-01

    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 and illicit tablets, created with different amounts of known colorants were analyzed. We derived reflectance spectra from hyperspectral images of these tablets, and successfully determined the most likely colorant used in the production of all self-manufactured tablets and four of five illicit tablets studied. Upon classification, the concentration of the colorant was estimated using a photon propagation model and a single reference measurement of a tablet of known concentration. The estimated concentrations showed a high correlation with the actual values (R(2) = 0.9374). The achieved color information, combined with other physical and chemical characteristics, can provide a powerful tool for the comparison of tablet seizures, which may reveal their origin. © 2013 American Academy of Forensic Sciences.

  3. Hyperspectral imaging for detection of cholesterol in human skin

    Science.gov (United States)

    Milanič, Matija; Bjorgan, Asgeir; Larsson, Marcus; Marraccini, Paolo; Strömberg, Tomas; Randeberg, Lise L.

    2015-03-01

    Hypercholesterolemia is characterized by high levels of cholesterol in the blood and is associated with an increased risk of atherosclerosis and coronary heart disease. Early detection of hypercholesterolemia is necessary to prevent onset and progress of cardiovascular disease. Optical imaging techniques might have a potential for early diagnosis and monitoring of hypercholesterolemia. In this study, hyperspectral imaging was investigated for this application. The main aim of the study was to identify spectral and spatial characteristics that can aid identification of hypercholesterolemia in facial skin. The first part of the study involved a numerical simulation of human skin affected by hypercholesterolemia. A literature survey was performed to identify characteristic morphological and physiological parameters. Realistic models were prepared and Monte Carlo simulations were performed to obtain hyperspectral images. Based on the simulations optimal wavelength regions for differentiation between normal and cholesterol rich skin were identified. Minimum Noise Fraction transformation (MNF) was used for analysis. In the second part of the study, the simulations were verified by a clinical study involving volunteers with elevated and normal levels of cholesterol. The faces of the volunteers were scanned by a hyperspectral camera covering the spectral range between 400 nm and 720 nm, and characteristic spectral features of the affected skin were identified. Processing of the images was done after conversion to reflectance and masking of the images. The identified features were compared to the known cholesterol levels of the subjects. The results of this study demonstrate that hyperspectral imaging of facial skin can be a promising, rapid modality for detection of hypercholesterolemia.

  4. Hyperspectral remote sensing image retrieval system using spectral and texture features.

    Science.gov (United States)

    Zhang, Jing; Geng, Wenhao; Liang, Xi; Li, Jiafeng; Zhuo, Li; Zhou, Qianlan

    2017-06-01

    Although many content-based image retrieval systems have been developed, few studies have focused on hyperspectral remote sensing images. In this paper, a hyperspectral remote sensing image retrieval system based on spectral and texture features is proposed. The main contributions are fourfold: (1) considering the "mixed pixel" in the hyperspectral image, endmembers as spectral features are extracted by an improved automatic pixel purity index algorithm, then the texture features are extracted with the gray level co-occurrence matrix; (2) similarity measurement is designed for the hyperspectral remote sensing image retrieval system, in which the similarity of spectral features is measured with the spectral information divergence and spectral angle match mixed measurement and in which the similarity of textural features is measured with Euclidean distance; (3) considering the limited ability of the human visual system, the retrieval results are returned after synthesizing true color images based on the hyperspectral image characteristics; (4) the retrieval results are optimized by adjusting the feature weights of similarity measurements according to the user's relevance feedback. The experimental results on NASA data sets can show that our system can achieve comparable superior retrieval performance to existing hyperspectral analysis schemes.

  5. Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry

    Directory of Open Access Journals (Sweden)

    Telmo Adão

    2017-10-01

    Full Text Available Traditional imagery—provided, for example, by RGB and/or NIR sensors—has proven to be useful in many agroforestry applications. However, it lacks the spectral range and precision to profile materials and organisms that only hyperspectral sensors can provide. This kind of high-resolution spectroscopy was firstly used in satellites and later in manned aircraft, which are significantly expensive platforms and extremely restrictive due to availability limitations and/or complex logistics. More recently, UAS have emerged as a very popular and cost-effective remote sensing technology, composed of aerial platforms capable of carrying small-sized and lightweight sensors. Meanwhile, hyperspectral technology developments have been consistently resulting in smaller and lighter sensors that can currently be integrated in UAS for either scientific or commercial purposes. The hyperspectral sensors’ ability for measuring hundreds of bands raises complexity when considering the sheer quantity of acquired data, whose usefulness depends on both calibration and corrective tasks occurring in pre- and post-flight stages. Further steps regarding hyperspectral data processing must be performed towards the retrieval of relevant information, which provides the true benefits for assertive interventions in agricultural crops and forested areas. Considering the aforementioned topics and the goal of providing a global view focused on hyperspectral-based remote sensing supported by UAV platforms, a survey including hyperspectral sensors, inherent data processing and applications focusing both on agriculture and forestry—wherein the combination of UAV and hyperspectral sensors plays a center role—is presented in this paper. Firstly, the advantages of hyperspectral data over RGB imagery and multispectral data are highlighted. Then, hyperspectral acquisition devices are addressed, including sensor types, acquisition modes and UAV-compatible sensors that can be used

  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. Topic Identification and Categorization of Public Information in Community-Based Social Media

    Science.gov (United States)

    Kusumawardani, RP; Basri, MH

    2017-01-01

    This paper presents a work on a semi-supervised method for topic identification and classification of short texts in the social media, and its application on tweets containing dialogues in a large community of dwellers in a city, written mostly in Indonesian. These dialogues comprise a wealth of information about the city, shared in real-time. We found that despite the high irregularity of the language used, and the scarcity of suitable linguistic resources, a meaningful identification of topics could be performed by clustering the tweets using the K-Means algorithm. The resulting clusters are found to be robust enough to be the basis of a classification. On three grouping schemes derived from the clusters, we get accuracy of 95.52%, 95.51%, and 96.7 using linear SVMs, reflecting the applicability of applying this method for generating topic identification and classification on such data.

  8. Active contour segmentation for hyperspectral oil spill remote sensing

    Science.gov (United States)

    Song, Mei-ping; Chang, Ming; An, Ju-bai; Huang, Jian; Lin, Bin

    2013-08-01

    Oil spills could occur in many conditions, which results in pollution of the natural resources, marine environment and economic health of the area. Whenever we need to identify oil spill, confirm the location or get the shape and acreage of oil spill, we have to get the edge information of oil slick images firstly. Hyperspectral remote sensing imaging is now widely used to detect oil spill. Active Contour Models (ACMs) is a widely used image segmentation method that utilizes the geometric information of objects within images. Region based models are less sensitive to noise and give good performance for images with weak edges or without edges. One of the popular Region based ACMs, active contours without edges Models, is implemented by Chan-Vese. The model has the property of global segmentation to segment all the objects within an image irrespective of the initial contour. In this paper, we propose an improved CV model, which can perform well in the oil spill hyper-spectral image segmentation. The energy function embeds spectral and spatial information, introduces the vector edge stopping function, and constructs a novel length term. Results of the improved model on airborne hyperspectral oil spill images show that it improves the ability of distinguishing between oil spills and sea water, as well as the capability of noise reduction.

  9. 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.

  10. 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.

  11. Spectral-spatial classification combined with diffusion theory based inverse modeling of hyperspectral images

    Science.gov (United States)

    Paluchowski, Lukasz A.; Bjorgan, Asgeir; Nordgaard, Hâvard B.; Randeberg, Lise L.

    2016-02-01

    Hyperspectral imagery opens a new perspective for biomedical diagnostics and tissue characterization. High spectral resolution can give insight into optical properties of the skin tissue. However, at the same time the amount of collected data represents a challenge when it comes to decomposition into clusters and extraction of useful diagnostic information. In this study spectral-spatial classification and inverse diffusion modeling were employed to hyperspectral images obtained from a porcine burn model using a hyperspectral push-broom camera. The implemented method takes advantage of spatial and spectral information simultaneously, and provides information about the average optical properties within each cluster. The implemented algorithm allows mapping spectral and spatial heterogeneity of the burn injury as well as dynamic changes of spectral properties within the burn area. The combination of statistical and physics informed tools allowed for initial separation of different burn wounds and further detailed characterization of the injuries in short post-injury time.

  12. Distance Metric Learning Using Privileged Information for Face Verification and Person Re-Identification.

    Science.gov (United States)

    Xu, Xinxing; Li, Wen; Xu, Dong

    2015-12-01

    In this paper, we propose a new approach to improve face verification and person re-identification in the RGB images by leveraging a set of RGB-D data, in which we have additional depth images in the training data captured using depth cameras such as Kinect. In particular, we extract visual features and depth features from the RGB images and depth images, respectively. As the depth features are available only in the training data, we treat the depth features as privileged information, and we formulate this task as a distance metric learning with privileged information problem. Unlike the traditional face verification and person re-identification tasks that only use visual features, we further employ the extra depth features in the training data to improve the learning of distance metric in the training process. Based on the information-theoretic metric learning (ITML) method, we propose a new formulation called ITML with privileged information (ITML+) for this task. We also present an efficient algorithm based on the cyclic projection method for solving the proposed ITML+ formulation. Extensive experiments on the challenging faces data sets EUROCOM and CurtinFaces for face verification as well as the BIWI RGBD-ID data set for person re-identification demonstrate the effectiveness of our proposed approach.

  13. Longwave infrared compressive hyperspectral imager

    Science.gov (United States)

    Dupuis, Julia R.; Kirby, Michael; Cosofret, Bogdan R.

    2015-06-01

    Physical Sciences Inc. (PSI) is developing a longwave infrared (LWIR) compressive sensing hyperspectral imager (CS HSI) based on a single pixel architecture for standoff vapor phase plume detection. The sensor employs novel use of a high throughput stationary interferometer and a digital micromirror device (DMD) converted for LWIR operation in place of the traditional cooled LWIR focal plane array. The CS HSI represents a substantial cost reduction over the state of the art in LWIR HSI instruments. Radiometric improvements for using the DMD in the LWIR spectral range have been identified and implemented. In addition, CS measurement and sparsity bases specifically tailored to the CS HSI instrument and chemical plume imaging have been developed and validated using LWIR hyperspectral image streams of chemical plumes. These bases enable comparable statistics to detection based on uncompressed data. In this paper, we present a system model predicting the overall performance of the CS HSI system. Results from a breadboard build and test validating the system model are reported. In addition, the measurement and sparsity basis work demonstrating the plume detection on compressed hyperspectral images is presented.

  14. Hyperspectral fluorescence imaging with multi wavelength LED excitation

    Science.gov (United States)

    Luthman, A. Siri; Dumitru, Sebastian; Quirós-Gonzalez, Isabel; Bohndiek, Sarah E.

    2016-04-01

    Hyperspectral imaging (HSI) can combine morphological and molecular information, yielding potential for real-time and high throughput multiplexed fluorescent contrast agent imaging. Multiplexed readout from targets, such as cell surface receptors overexpressed in cancer cells, could improve both sensitivity and specificity of tumor identification. There remains, however, a need for compact and cost effective implementations of the technology. We have implemented a low-cost wide-field multiplexed fluorescence imaging system, which combines LED excitation at 590, 655 and 740 nm with a compact commercial solid state HSI system operating in the range 600 - 1000 nm. A key challenge for using reflectance-based HSI is the separation of contrast agent fluorescence from the reflectance of the excitation light. Here, we illustrate how it is possible to address this challenge in software, using two offline reflectance removal methods, prior to least-squares spectral unmixing. We made a quantitative comparison of the methods using data acquired from dilutions of contrast agents prepared in well-plates. We then established the capability of our HSI system for non-invasive in vivo fluorescence imaging in small animals using the optimal reflectance removal method. The HSI presented here enables quantitative unmixing of at least four fluorescent contrast agents (Alexa Fluor 610, 647, 700 and 750) simultaneously in living mice. A successful unmixing of the four fluorescent contrast agents was possible both using the pure contrast agents and with mixtures. The system could in principle also be applied to imaging of ex vivo tissue or intraoperative imaging in a clinical setting. These data suggest a promising approach for developing clinical applications of HSI based on multiplexed fluorescence contrast agent imaging.

  15. Sampling scheme optimization from hyperspectral data

    NARCIS (Netherlands)

    Debba, P.

    2006-01-01

    This thesis presents statistical sampling scheme optimization for geo-environ-menta] purposes on the basis of hyperspectral data. It integrates derived products of the hyperspectral remote sensing data into individual sampling schemes. Five different issues are being dealt with.First, the optimized

  16. Classifications of objects on hyperspectral images

    DEFF Research Database (Denmark)

    Kucheryavskiy, Sergey

    Hyperspectral imaging is a modern analytical technique combining benefits of digital imaging and vibrational spectroscopy. It allows to reveal and visualise spatial distribution of various chemical components. In a hyperspectral image every pixel is a spectrum (usually VNIR, SWIR or Raman...

  17. 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

  18. Classification of hyperspectral images based on conditional random fields

    Science.gov (United States)

    Hu, Yang; Saber, Eli; Monteiro, Sildomar T.; Cahill, Nathan D.; Messinger, David W.

    2015-02-01

    A significant increase in the availability of high resolution hyperspectral images has led to the need for developing pertinent techniques in image analysis, such as classification. Hyperspectral images that are correlated spatially and spectrally provide ample information across the bands to benefit this purpose. Conditional Random Fields (CRFs) are discriminative models that carry several advantages over conventional techniques: no requirement of the independence assumption for observations, flexibility in defining local and pairwise potentials, and an independence between the modules of feature selection and parameter leaning. In this paper we present a framework for classifying remotely sensed imagery based on CRFs. We apply a Support Vector Machine (SVM) classifier to raw remotely sensed imagery data in order to generate more meaningful feature potentials to the CRFs model. This approach produces promising results when tested with publicly available AVIRIS Indian Pine imagery.

  19. 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.

  20. Airborne measurements in the longwave infrared using an imaging hyperspectral sensor

    Science.gov (United States)

    Allard, Jean-Pierre; Chamberland, Martin; Farley, Vincent; Marcotte, Frédérick; Rolland, Matthias; Vallières, Alexandre; Villemaire, André

    2008-08-01

    Emerging applications in Defense and Security require sensors with state-of-the-art sensitivity and capabilities. Among these sensors, the imaging spectrometer is an instrument yielding a large amount of rich information about the measured scene. Standoff detection, identification and quantification of chemicals in the gaseous state is one important application. Analysis of the surface emissivity as a means to classify ground properties and usage is another one. Imaging spectrometers have unmatched capabilities to meet the requirements of these applications. Telops has developed the FIRST, a LWIR hyperspectral imager. The FIRST is based on the Fourier Transform technology yielding high spectral resolution and enabling high accuracy radiometric calibration. The FIRST, a man portable sensor, provides datacubes of up to 320x256 pixels at 0.35mrad spatial resolution over the 8-12 μm spectral range at spectral resolutions of up to 0.25cm-1. The FIRST has been used in several field campaigns, including the demonstration of standoff chemical agent detection [http://dx.doi.org/10.1117/12.795119.1]. More recently, an airborne system integrating the FIRST has been developed to provide airborne hyperspectral measurement capabilities. The airborne system and its capabilities are presented in this paper. The FIRST sensor modularity enables operation in various configurations such as tripod-mounted and airborne. In the airborne configuration, the FIRST can be operated in push-broom mode, or in staring mode with image motion compensation. This paper focuses on the airborne operation of the FIRST sensor.

  1. Probabilistic anomaly detector for remotely sensed hyperspectral data

    Science.gov (United States)

    Gao, Lianru; Guo, Qiandong; Plaza, Antonio; Li, Jun; Zhang, Bing

    2014-01-01

    Anomaly detection is an important technique for remotely sensed hyperspectral data exploitation. In the last decades, several algorithms have been developed for detecting anomalies in hyperspectral images. The Reed-Xiaoli detector (RXD) is one of the most widely used approaches for this purpose. Since the RXD assumes that the distribution of the background is Gaussian, it generally suffers from a high false alarm rate. In order to address this issue, we introduce an unsupervised probabilistic anomaly detector (PAD) based on estimating the difference between the probabilities of the anomalies and the background. The proposed PAD takes advantage of the results provided by the RXD to estimate statistical information for the targets and background, respectively, and then uses an automatic strategy to find the most suitable threshold for the separation of targets from the background. The proposed technique is validated using a synthetic data set and two real hyperspectral data sets with ground-truth information. Our experimental results indicate that the proposed method achieves good detection ratios with adequate computational complexity as compared with other widely used anomaly detectors.

  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. Assessment of Giant Kelp Physiological State Using Airborne Hyperspectral Imagery

    Science.gov (United States)

    Bell, T. W.; Siegel, D.

    2016-02-01

    Giant kelp is a highly dynamic foundation species that supports an ecologically and economically important ecosystem found throughout the globe. Currently, multispectral sensors (Landsat) provide valuable time series of emergent kelp canopy biomass that are useful for many applications. Hyperspectral sensors can provide information that quantify the quality or physiological condition of the kelp canopy, which can be linked to characteristics such as canopy age and morphology, light exposure, nutrient stress and photosynthetic yield. The HyspIRI Preparatory Airborne Campaign delivered near seasonal hyperspectral imagery of giant kelp canopy using the AVIRIS sensor ( 20 m spatial resolution; 10 nm spectral resolution), to support the proposed spaceborne hyperspectral imager mission. These images, combined with additional AVIRIS imagery, were used to assess giant kelp canopy condition across several years and biogeographical regions, including Monterey Bay, the Santa Barbara Channel, and the Southern California coast. Specifically, we developed novel techniques to infer the chlorophyll a to carbon ratio (Chl:C) from the AVIRIS imagery, derived from field observations of canopy blade reflectance, pigment concentrations and carbon content, and these determinations of Chl:C are used as measures of the physiological state of the kelp canopy. We found that the spatial and temporal variability in physiological condition of the kelp canopy varied with light exposure and timing of nutrient pulses due to coastal upwelling. These observations are consistent with photophysiological theory and field observations. Physiological state dynamics gleaned from airborne sensors and proposed spaceborne hyperspectral sensors enhance our understanding of this important ecosystem engineer, and provide useful information for marine scientists and ecosystem managers.

  4. 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...

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

    OpenAIRE

    Jorge Buzzi; Asunción Riaza; Eduardo García-Meléndez; Sebastian Weide; Martin Bachmann

    2014-01-01

    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 hyp...

  6. Detection and monitoring of oil spills using hyperspectral imagery

    Science.gov (United States)

    Sanchez, Glenda; Roper, William E.; Gomez, Richard B.

    2003-08-01

    Oil pollution is a very important aspect in the environmental field. Oil pollution is an important subject due to its capacity to adversely affect animals, aquatic life, vegetation and drinking water. The movement of open water oil spills can be affected by mind, waves and tides. Land based oil spills are often affected by rain and temperature. It is important to have an accurate management of the cleanup. Remote sensing and in particular hyper-spectral capabilities, are being use to identify oil spills and prevent worse problems. In addition to this capability, this technology can be used for federal and state compliance of petroleum related companies. There are several hyper-spectral sensors used in the identification of oil spills. One commonly use sensor is the Airborne Imaging Spectroradiometer for Applications (AISA). The main concern associated with the use of these sensors is the potential for false identification of oil spills. The use of AISA to identify an oil spill over the Patuxent River is an example of how this tool can assist with investigating an oil pipeline accident, and its potential to affect the surrounding environment. A scenario like this also serves as a good test of the accuracy with which spills may be identified using new airborne sensors.

  7. Detection of cold stressed maize seedlings for high throughput phenotyping using hyperspectral imagery

    Science.gov (United States)

    Xie, Chuanqi; Yang, Ce; Moghimi, Ali

    2017-04-01

    Hyperspectral imaging can provide hundreds of images at different wave bands covering the visible and near infrared regions, which is superior to traditional spectral and RGB techniques. Minnesota produced a lot of maize every year, while the temperature in Minnesota can change abruptly during spring. This study was carried out to use hyperspectral imaging technique to identify maize seedlings with cold stress prior to having visible phenotypes. A total of 60 samples were scanned by the hyperspectral camera at the wave range of 395-885 nm. The spectral reflectance information was extracted from the corrected hyperspectral images. By spectral reflectance information, support vector machine (SVM) classification models were established to identify the cold stressed samples. Then, the wavelengths which could play significant roles for the detection were selected using two-wavelength combination method. The classifiers were built again using the selected wavelengths. From the results, it can be found the selected wavelengths can even perform better than full wave range. The overall results indicated that hyperspectral imaging has the potential to classify cold stress symptoms in maize seedlings and thus help in selecting the corn genome lines with cold stress resistance.

  8. A Hybrid DBN and CRF Model for Spectral-Spatial Classification of Hyperspectral Images

    Directory of Open Access Journals (Sweden)

    Ping Zhong

    2017-06-01

    Full Text Available Hyperspectral image classification plays an important role in remote sensing image analysis. Recent techniques have attempted to investigate the capabilities of deep learning approaches to tackle the hyperspectral image classification. This work shows how to further improve the hyperspectral image classification through using both a deep representation and contextual information. To implement this objective, this work proposes a new Conditional Random Field (CRF model (named DBN-CRF with the potentials defined over the deep features produced by a Deep Belief Network (DBN. The newly formulated DBN-CRF model takes advantage of the strength of DBNs in learning a good representation and the ability of CRFs to model contextual (spatial information in both the observations and labels. Within a piecewise training framework, an efficient training method is proposed to train the whole DBN-CRF model end-to-end. This means that the parameters in DBN and CRF can be jointly trained and thus the proposed method can fully use the strength of both DBN and CRF. Moreover, in the proposed training method, the end-to-end training can be implemented with a standard back-propagation algorithm, avoiding the repeated inference usually involved in CRF training and thus is computationally efficient. Experiments on real-world hyperspectral data show that our method outperforms the most recent approaches in hyperspectral image classification.

  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. Multicriteria classification method for dimensionality reduction adapted to hyperspectral images

    Science.gov (United States)

    Khoder, Mahdi; Kashana, Serge; Khoder, Jihan; Younes, Rafic

    2017-04-01

    Due to the incredible growth of high dimensional datasets, we address the problem of unsupervised methods sensitive to undergoing different variations, such as noise degradation, and to preserving rare information. Therefore, researchers nowadays are forced to develop techniques to meet the needed requirements. In this work, we introduce a dimensionality reduction method that focuses on the multiobjectives of multiple images taken from multiple frequency bands, which form a hyperspectral image. The multicriteria classification algorithm technique compares and classifies these images based on multiple similarity criteria, which allows the selection of particular images from the whole set of images. The selected images are the ones chosen to represent the original set of data while respecting certain quality thresholds. Knowing that the number of images in a hyperspectral image signifies its dimension, choosing a smaller number of images to represent the data leads to dimensionality reduction. Also, results of tests of the developed algorithm on multiple hyperspectral image samples are shown. A comparative study later on will show the advantages of this technique compared to other common methods used in the field of dimensionality reduction.

  11. Image enhancement based on in vivo hyperspectral gastroscopic images: a case study.

    Science.gov (United States)

    Gu, Xiaozhou; Han, Zhimin; Yao, Liqing; Zhong, Yunshi; Shi, Qiang; Fu, Ye; Liu, Changsheng; Wang, Xiguang; Xie, Tianyu

    2016-10-01

    Hyperspectral imaging (HSI) has been recognized as a powerful tool for noninvasive disease detection in the gastrointestinal field. However, most of the studies on HSI in this field have involved ex vivo biopsies or resected tissues. We proposed an image enhancement method based on in vivo hyperspectral gastroscopic images. First, we developed a flexible gastroscopy system capable of obtaining in vivo hyperspectral images of different types of stomach disease mucosa. Then, depending on a specific object, an appropriate band selection algorithm based on dependence of information was employed to determine a subset of spectral bands that would yield useful spatial information. Finally, these bands were assigned to be the color components of an enhanced image of the object. A gastric ulcer case study demonstrated that our method yields higher color tone contrast, which enhanced the displays of the gastric ulcer regions, and that it will be valuable in clinical applications.

  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. Image enhancement based on in vivo hyperspectral gastroscopic images: a case study

    Science.gov (United States)

    Gu, Xiaozhou; Han, Zhimin; Yao, Liqing; Zhong, Yunshi; Shi, Qiang; Fu, Ye; Liu, Changsheng; Wang, Xiguang; Xie, Tianyu

    2016-10-01

    Hyperspectral imaging (HSI) has been recognized as a powerful tool for noninvasive disease detection in the gastrointestinal field. However, most of the studies on HSI in this field have involved ex vivo biopsies or resected tissues. We proposed an image enhancement method based on in vivo hyperspectral gastroscopic images. First, we developed a flexible gastroscopy system capable of obtaining in vivo hyperspectral images of different types of stomach disease mucosa. Then, depending on a specific object, an appropriate band selection algorithm based on dependence of information was employed to determine a subset of spectral bands that would yield useful spatial information. Finally, these bands were assigned to be the color components of an enhanced image of the object. A gastric ulcer case study demonstrated that our method yields higher color tone contrast, which enhanced the displays of the gastric ulcer regions, and that it will be valuable in clinical applications.

  14. Hyperspectral Imaging to Determine the Properties and Homogeneity of Renewable Carbon Materials.

    Science.gov (United States)

    Mäkelä, Mikko; Geladi, Paul

    2017-07-10

    Hyperspectral imaging within the near infrared (NIR) region offers a fast and reliable way for determining the properties of renewable carbon materials. The chemical information provided by a spectrum combined with the spatial information of an image allows mathematical operations that can be performed in both the spectral and spatial domains. Here, we show that hyperspectral NIR imaging can be successfully used to determine the properties of hydrothermally prepared carbon on the material and pixel levels. Materials produced from different feedstocks or prepared under different temperatures can also be distinguished, and their homogeneity can be evaluated. As hyperspectral imaging within the NIR region is non-destructive and requires very little sample preparation, it can be used for controlling the quality of renewable carbon materials destined for a wide range of different applications. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  15. Parallel implementation of linear and nonlinear spectral unmixing of remotely sensed hyperspectral images

    Science.gov (United States)

    Plaza, Antonio; Plaza, Javier

    2011-11-01

    Hyperspectral unmixing is a very important task for remotely sensed hyperspectral data exploitation. It addresses the (possibly) mixed nature of pixels collected by instruments for Earth observation, which are due to several phenomena including limited spatial resolution, presence of mixing effects at different scales, etc. Spectral unmixing involves the separation of a mixed pixel spectrum into its pure component spectra (called endmembers) and the estimation of the proportion (abundance) of endmember in the pixel. Two models have been widely used in the literature in order to address the mixture problem in hyperspectral data. The linear model assumes that the endmember substances are sitting side-by-side within the field of view of the imaging instrument. On the other hand, the nonlinear mixture model assumes nonlinear interactions between endmember substances. Both techniques can be computationally expensive, in particular, for high-dimensional hyperspectral data sets. In this paper, we develop and compare parallel implementations of linear and nonlinear unmixing techniques for remotely sensed hyperspectral data. For the linear model, we adopt a parallel unsupervised processing chain made up of two steps: i) identification of pure spectral materials or endmembers, and ii) estimation of the abundance of each endmember in each pixel of the scene. For the nonlinear model, we adopt a supervised procedure based on the training of a parallel multi-layer perceptron neural network using intelligently selected training samples also derived in parallel fashion. The compared techniques are experimentally validated using hyperspectral data collected at different altitudes over a so-called Dehesa (semi-arid environment) in Extremadura, Spain, and evaluated in terms of computational performance using high performance computing systems such as commodity Beowulf clusters.

  16. Hyperspectral remote sensing for terrestrial applications

    Science.gov (United States)

    Thenkabail, Prasad S.; Teluguntla, Pardhasaradhi G.; Murali Krishna Gumma,; Venkateswarlu Dheeravath,

    2015-01-01

    Remote sensing data are considered hyperspectral when the data are gathered from numerous wavebands, contiguously over an entire range of the spectrum (e.g., 400–2500 nm). Goetz (1992) defines hyperspectral remote sensing as “The acquisition of images in hundreds of registered, contiguous spectral bands such that for each picture element of an image it is possible to derive a complete reflectance spectrum.” However, Jensen (2004) defines hyperspectral remote sensing as “The simultaneous acquisition of images in many relatively narrow, contiguous and/or non contiguous spectral bands throughout the ultraviolet, visible, and infrared portions of the electromagnetic spectrum.

  17. Geometric and Reflectance Signature Characterization of Complex Canopies Using Hyperspectral Stereoscopic Images from Uav and Terrestrial Platforms

    Science.gov (United States)

    Honkavaara, E.; Hakala, T.; Nevalainen, O.; Viljanen, N.; Rosnell, T.; Khoramshahi, E.; Näsi, R.; Oliveira, R.; Tommaselli, A.

    2016-06-01

    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.

  18. 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.

  19. Infrared upconversion hyperspectral imaging

    DEFF Research Database (Denmark)

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

    2015-01-01

    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...

  20. Infrared hyperspectral imaging stokes polarimeter

    Science.gov (United States)

    Jones, Julia Craven

    This work presents the design, development, and testing of a field portable imaging spectropolarimeter that operates over the short-wavelength and middle-wavelength portion of the infrared spectrum. The sensor includes a pair of sapphire Wollaston prisms and several high order retarders to produce the first infrared implementation of an imaging Fourier transform spectropolarimeter, providing for the measurement of the complete spectropolarimetric datacube over the passband. The Wollaston prisms serve as a birefringent interferometer with reduced sensitivity to vibration when compared to an unequal path interferometer, such as a Michelson. Polarimetric data are acquired through the use of channeled spectropolarimetry to modulate the spectrum with the Stokes parameter information. The collected interferogram is Fourier filtered and reconstructed to recover the spatially and spectrally varying Stokes vector data across the image. The intent of this dissertation is to provide the reader with a detailed understanding of the steps involved in the development of this infrared hyperspectral imaging polarimeter (IHIP) instrument. First, Chapter 1 provides an overview of the fundamental concepts relevant to this research. These include imaging spectrometers, polarimeters, and spectropolarimeters. A detailed discussion of channeled spectropolarimetry, including a historical study of previous implementations, is also presented. Next a few of the design alternatives that are possible for this work are outlined and discussed in Chapter 2. The configuration that was selected for the IHIP is then presented in detail, including the optical layout, design, and operation. Chapter 3 then presents an artifact reduction technique (ART) that was developed to improve the IHIP's spectropolarimetric reconstructions by reducing errors associated with non-band-limited spectral features. ART is experimentally verified in the infrared using a commercial Fourier transform spectrometer in

  1. 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 report with the RDE response pairs in a random order were significantly slower (16%, P report formats. Such information may be useful in deciding between different format options.

  2. 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...

  3. Landslide Identification and Information Extraction Based on Optical and Multispectral UAV Remote Sensing Imagery

    Science.gov (United States)

    Lin, Jiayuan; Wang, Meimei; Yang, Jia; Yang, Qingxia

    2017-02-01

    Landslide is one of the most serious natural disasters which caused enormous economic losses and casualties in the world. Fast and accurate identification of newly occurred landslide and extraction of relevant information are the premise and foundation for landslide disaster assessment and relief. As the places where landslides occur are often inaccessible for field observation because of the temporary failure in transportation and communication. Therefore, UAV remote sensing can be adopted to collect landslide information efficiently and quickly with the advantages of low cost, flexible launch and landing, safety, under-cloud-flying, and hyperspatial image resolution. Newly occurred landslides are usually accompanied with those phenomena such as vegetation burying and bedrock or bare soil exposure, which can be easily detected in optical or multispectral UAV images. By taking one typical landslide occurred in Wenchuan Earthquake stricken area in 2010 as an example, this paper demonstrates the process of integration of multispectral camera with UAV platform, NDVI generation with multispectral UAV images, three-dimensional terrain and orthophoto generation with optical UAV images, and identification and extraction of landslide information such as its location, impacted area, and earthwork volume.

  4. Object-Based Tree Species Classification in Urban Ecosystems Using LiDAR and Hyperspectral Data

    Directory of Open Access Journals (Sweden)

    Zhongya Zhang

    2016-06-01

    Full Text Available In precision forestry, tree species identification is key to evaluating the role of forest ecosystems in the provision of ecosystem services, such as carbon sequestration and assessing their effects on climate regulation and climate change. In this study, we investigated the effectiveness of tree species classification of urban forests using aerial-based HyMap hyperspectral imagery and light detection and ranging (LiDAR data. First, we conducted an object-based image analysis (OBIA to segment individual tree crowns present in LiDAR-derived Canopy Height Models (CHMs. Then, hyperspectral values for individual trees were extracted from HyMap data for band reduction through Minimum Noise Fraction (MNF transformation which allowed us to reduce the data to 20 significant bands out of 118 bands acquired. Finally, we compared several different classifications using Random Forest (RF and Multi Class Classifier (MCC methods. Seven tree species were classified using all 118 bands which resulted in 46.3% overall classification accuracy for RF versus 79.6% for MCC. Using only the 20 optimal bands extracted through MNF, both RF and MCC achieved an increase in overall accuracy to 87.0% and 88.9%, respectively. Thus, the MNF band selection process is a preferable approach for tree species classification when using hyperspectral data. Further, our work also suggests that RF is heavily disadvantaged by the high-dimensionality and noise present in hyperspectral data, while MCC is more robust when handling high-dimensional datasets with small sample sizes. Our overall results indicated that individual tree species identification in urban forests can be accomplished with the fusion of object-based LiDAR segmentation of crowns and hyperspectral characterization.

  5. Spatial-Spectral Classification Based on the Unsupervised Convolutional Sparse Auto-Encoder for Hyperspectral Remote Sensing Imagery

    Science.gov (United States)

    Han, Xiaobing; Zhong, Yanfei; Zhang, Liangpei

    2016-06-01

    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.

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

    Directory of Open Access Journals (Sweden)

    Juha Hyyppä

    2011-05-01

    Full Text Available 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.

  7. Ground Viewing Perspective Hyperspectral Anomaly Detection

    National Research Council Canada - National Science Library

    Rosario, Dalton; Romano, Joao

    2008-01-01

    ...., ground vehicles, camouflaged personnel) using passive hyperspectral (HS) devices. This report focuses on the first stage of a two-stage algorithm suite features autonomous clutter background characterization (ACBC...

  8. Spherical stochastic neighbor embedding of hyperspectral data

    CSIR Research Space (South Africa)

    Lunga, D

    2012-07-01

    Full Text Available In hyperspectral imagery, low-dimensional representations are sought in order to explain well the nonlinear characteristics that are hidden in high-dimensional spectral channels. While many algorithms have been proposed for dimension reduction...

  9. Automated Feature Extraction from Hyperspectral Imagery Project

    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...

  10. Automated Feature Extraction from Hyperspectral Imagery Project

    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...

  11. 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.

  12. 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.

  13. 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.

  14. Progressive Line Processing of Kernel RX Anomaly Detection Algorithm for Hyperspectral Imagery.

    Science.gov (United States)

    Zhao, Chunhui; Deng, Weiwei; Yan, Yiming; Yao, Xifeng

    2017-08-07

    The Kernel-RX detector (KRXD) has attracted widespread interest in hyperspectral image processing with the utilization of nonlinear information. However, the kernelization of hyperspectral data leads to poor execution efficiency in KRXD. This paper presents an approach to the progressive line processing of KRXD (PLP-KRXD) that can perform KRXD line by line (the main data acquisition pattern). Parallel causal sliding windows are defined to ensure the causality of PLP-KRXD. Then, with the employment of the Woodbury matrix identity and the matrix inversion lemma, PLP-KRXD has the capacity to recursively update the kernel matrices, thereby avoiding a great many repetitive calculations of complex matrices, and greatly reducing the algorithm's complexity. To substantiate the usefulness and effectiveness of PLP-KRXD, three groups of hyperspectral datasets are used to conduct experiments.

  15. Mapping urban impervious surfaces from an airborne hyperspectral imagery using the object-oriented classification approach

    Directory of Open Access Journals (Sweden)

    Aguejdad Rahim

    2017-01-01

    Full Text Available The objective of this research is to explore the capabilities of the hyperspectral imagery in mapping the urban impervious objects and identifying the surface materials using an object-oriented approach. The application is conducted to Toulouse city (France within the HYEP research project in charge of using hyperspectral imagery for the environmental urban planning. The method uses the multi-resolution segmentation and classification algorithms. The first results highlight a high potential of the hyperspectral imagery in land cover mapping of the urban environment, especially the extraction of impervious surfaces. They, also, illustrate, that the object-oriented approach by means of the fuzzy logic classifier yields promising results in distinguishing the mean roofing materials based only on the spectral information. Conversely to the red clay tiles and metal roofs, which are easily identified, the concrete, gravel and asphalt roofs are still confused with roads.

  16. Minimum distance constrained non-negative matrix factorization for the endmember extraction of hyperspectral images

    Science.gov (United States)

    Yu, Yue; Guo, Shan; Sun, Weidong

    2007-11-01

    Endmember extraction and spectral unmixing is a very challenging task in multispectral/hyperspectral image processing due to the incompleteness of information. In this paper, a new method for endmember extraction and spectral unmixing of hyperspectral images is proposed, which is called as minimum distance constrained nonnegative matrix factorization (MDC-NMF). After being compared with a newly developed method named MVC-NMF, MDC-NMF not only has been demonstrated more reasonable in theory but also shows promising results in the experiments.

  17. 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.

  18. 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......). The MAF projection exploits the fact that interesting phenomena in images typically exhibit spatial autocorrelation. The analysis is based on nearinfrared hyperspectral images of maize grains demonstrating the superiority of the kernelbased MAF method....

  19. 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

  20. 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.

  1. 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.

  2. Automatic Identification of Travel Locations in Rare Books - Object Oriented Information Management

    Directory of Open Access Journals (Sweden)

    Detlev Doherr

    2017-04-01

    Full Text Available The digital content of the Internet is growing exponentially and mass digitization of printed media opens access to literature, in particular the genre of travel literature from the 18th and 19th century, which consists of diaries or travel books describing routes, observations or inspirations. The identification of described locations in the digital text is a long-standing challenge which requires information technology to supply dynamic links to sources by new forms of interaction and synthesis between humanistic texts and scientific observations. Using object oriented information technology, a prototype of a software tool is developed which makes it possible to automatically identify geographic locations and travel routes mentioned in rare books. The information objects contain properties such as names and classification codes for populated places, streams, mountains and regions. Together, with the latitudes and longitudes of every single location, it is possible to geo-reference this information in order that all processed and filtered datasets can be displayed by a map application. This method has already been used in the Humboldt Digital Library to present Alexander von Humboldt's maps and was tested in a case study to prove the correctness and reliability of the automatic identification of locations based on the work of Alexander von Humboldt and Johann Wolfgang von Goethe. The results reveal numerous errors due to misspellings, change of location names, equality of terms and location names. But on the other hand it becomes very clear that results of the automatic object detection and recognition can be improved by error-free and comprehensive sources. As a result an increase in quality and usability of the service can be expected, accompanied by more options to detect unknown locations in the descriptions of rare books.

  3. 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

  4. The Value of Molecular vs. Morphometric and Acoustic Information for Species Identification Using Sympatric Molossid Bats.

    Directory of Open Access Journals (Sweden)

    Yann Gager

    Full Text Available A fundamental condition for any work with free-ranging animals is correct species identification. However, in case of bats, information on local species assemblies is frequently limited especially in regions with high biodiversity such as the Neotropics. The bat genus Molossus is a typical example of this, with morphologically similar species often occurring in sympatry. We used a multi-method approach based on molecular, morphometric and acoustic information collected from 962 individuals of Molossus bondae, M. coibensis, and M. molossus captured in Panama. We distinguished M. bondae based on size and pelage coloration. We identified two robust species clusters composed of M. molossus and M. coibensis based on 18 microsatellite markers but also on a more stringently determined set of four markers. Phylogenetic reconstructions using the mitochondrial gene co1 (DNA barcode were used to diagnose these microsatellite clusters as M. molossus and M. coibensis. To differentiate species, morphological information was only reliable when forearm length and body mass were combined in a linear discriminant function (95.9% correctly identified individuals. When looking in more detail at M. molossus and M. coibensis, only four out of 13 wing parameters were informative for species differentiation, with M. coibensis showing lower values for hand wing area and hand wing length and higher values for wing loading. Acoustic recordings after release required categorization of calls into types, yielding only two informative subsets: approach calls and two-toned search calls. Our data emphasizes the importance of combining morphological traits and independent genetic data to inform the best choice and combination of discriminatory information used in the field. Because parameters can vary geographically, the multi-method approach may need to be adjusted to local species assemblies and populations to be entirely informative.

  5. Hyperspectral remote sensing of vegetation: knowledge gain and knowledge gap after 50 years of research (Conference Presentation)

    Science.gov (United States)

    Thenkabail, Prasad S.

    2017-04-01

    This presentation summarizes the advances made over 40+ years in understanding, modeling, and mapping terrestrial vegetation as reported in the new book on "Hyperspectral Remote Sensing of Vegetation" (Publisher:Taylor and Francis inc.). The advent of spaceborne hyperspectral sensors or imaging spectroscopy (e.g., NASA's Hyperion, ESA's PROBA, and upcoming Italy's ASI's Prisma, Germany's DLR's EnMAP, Japanese HIUSI, NASA's HyspIRI) as well as the advances made in processing when handling large volumes of hyperspectral data have generated tremendous interest in advancing the hyperspectral applications' knowledge base to large areas. Advances made in using hyperspectral data, relative to broadband data, include: (a) significantly improved characterization and modeling of a wide array of biophysical and biochemical properties of vegetation, (b) ability to discriminate plant species and vegetation types with high degree of accuracy, (c) reducing uncertainties in determining net primary productivity or carbon assessments from terrestrial vegetation, (d) improved crop productivity and water productivity models, (e) ability to assess stress resulting from causes such as management practices, pests and disease, water deficit or water excess, and (f) establishing more sensitive wavebands and indices to study vegetation characteristics. The presentation will discuss topics such as: (1) hyperspectral sensors and their characteristics, (2) methods of overcoming the Hughes phenomenon, (3) characterizing biophysical and biochemical properties, (4) advances made in using hyperspectral data in modeling evapotranspiration or actual water use by plants, (5) study of phenology, light use efficiency, and gross primary productivity, (5) improved accuracies in species identification and land cover classifications, and (6) applications in precision farming.

  6. 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.

  7. GPU implementation issues for fast unmixing of hyperspectral images

    Science.gov (United States)

    Legendre, Maxime; Capriotti, Luca; Schmidt, Frédéric; Moussaoui, Saïd; Schmidt, Albrecht

    2013-04-01

    Space missions usually use hyperspectral imaging techniques to analyse the composition of planetary surfaces. Missions such as ESA's Mars Express and Venus Express generate extensive datasets whose processing demands so far have exceeded the resources available to many researchers. To overcome this limitation, the challenge is to develop numerical methods allowing to exploit the potential of modern calculation tools. The processing of a hyperspectral image consists of the identification of the observed surface components and eventually the assessment of their fractional abundances inside each pixel area. In this latter case, the problem is referred to as spectral unmixing. This work focuses on a supervised unmixing approach where the relevant component spectra are supposed to be part of an available spectral library. Therefore, the question addressed here is reduced to the estimation of the fractional abundances, or abundance maps. It requires the solution of a large-scale optimization problem subject to linear constraints; positivity of the abundances and their partial/full additivity (sum less/equal to one). Conventional approaches to such a problem usually suffer from a high computational overhead. Recently, an interior-point optimization using a primal-dual approach has been proven an efficient method to solve this spectral unmixing problem at reduced computational cost. This is achieved with a parallel implementation based on Graphics Processing Units (GPUs). Several issues are discussed such as the data organization in memory and the strategy used to compute efficiently one global quantity from a large dataset in a parallel fashion. Every step of the algorithm is optimized to be GPU-efficient. Finally, the main steps of the global system for the processing of a large number of hyperspectral images are discussed. The advantage of using a GPU is demonstrated by unmixing a large dataset consisting of 1300 hyperspectral images from Mars Express' OMEGA instrument

  8. Compressive sensing and hyperspectral imaging

    Science.gov (United States)

    Barducci, A.; Guzzi, D.; Lastri, C.; Marcoionni, P.; Nardino, V.; Pippi, I.

    2017-11-01

    Compressive sensing (sampling) is a novel technology and science domain that exploits the option to sample radiometric and spectroscopic signals at a lower sampling rate than the one dictated by the traditional theory of ideal sampling. In the paper some general concepts and characteristics regarding the use of compressive sampling in instruments devoted to Earth observation is discussed. The remotely sensed data is assumed to be constituted by sampled images collected by a passive device in the optical spectral range from the visible up to the thermal infrared, with possible spectral discrimination ability, e.g. hyperspectral imaging. According to recent investigations, compressive sensing necessarily employs a signal multiplexing architecture, which in spite of traditional expectations originates a significant SNR disadvantage.

  9. Preliminary results from an infrared hyperspectral imaging polarimeter

    Science.gov (United States)

    Craven-Jones, Julia; Kudenov, Michael W.; Stapelbroek, Maryn G.; Dereniak, Eustace L.

    2011-10-01

    We present results from a SWIR/MWIR infrared hyperspectral imaging polarimeter (IHIP). The sensor includes a pair of sapphire Wollaston prisms and several high order retarders to form an imaging Fourier transform spectropolarimeter. The Wollaston prisms serve as a birefringent interferometer with reduced sensitivity to vibration versus an unequal path interferometer, such as a Michelson. Polarimetric data are acquired through the use of channeled spectropolarimetry to modulate the spectrum with the Stokes parameter information. We discuss the operation of the IHIP sensor, in addition to our calibration techniques. Lastly, spectropolarimetric results from the laboratory and outdoor tests are presented.

  10. 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.

  11. 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.

  12. MYCONET : European network of information sources for an identification system of emerging mycotoxins in wheat based supply chains

    NARCIS (Netherlands)

    Fels-Klerx, van der H.J.; Booij, C.J.H.

    2008-01-01

    This report describes the results of the MYCONET project, an international research project aimed at initiating a sustainable platform (network) of information sources that proactively provides specified information for an emerging risk identification system. As a case study, the project focused on

  13. Hyperspectral Imagery for Large Area Survey of Organophosphate Pesticides

    Science.gov (United States)

    2015-03-26

    HYPERSPECTRAL IMAGERY FOR LARGE AREA SURVEY OF ORGANOPHOSPHATE PESTICIDES THESIS MARCH 2015...States. AFIT-ENV-MS-15-M-203 HYPERSPECTRAL IMAGERY FOR LARGE AREA SURVEY OF ORGANOPHOSPHATE PESTICIDES THESIS Presented to the Faculty...HYPERSPECTRAL IMAGERY FOR LARGE AREA SURVEY OF ORGANOPHOSPHATE PESTICIDES Daniel R. Baseley, BSE Captain, USAF Committee Membership

  14. Infrared hyperspectral imaging polarimeter using birefringent prisms.

    Science.gov (United States)

    Craven-Jones, Julia; Kudenov, Michael W; Stapelbroek, Maryn G; Dereniak, Eustace L

    2011-03-10

    A compact short-wavelength and middle-wavelength infrared hyperspectral imaging polarimeter (IHIP) is introduced. The sensor includes a pair of sapphire Wollaston prisms and several high-order retarders to form an imaging Fourier transform spectropolarimeter. The Wollaston prisms serve as a birefringent interferometer with reduced sensitivity to vibration versus an unequal path interferometer, such as a Michelson. Polarimetric data are acquired through the use of channeled spectropolarimetry to modulate the spectrum with the Stokes parameter information. The collected interferogram is Fourier filtered and reconstructed to recover the spatially and spectrally varying Stokes vector data across the image. The IHIP operates over a ±5° field of view and implements a dual-scan false signature reduction technique to suppress polarimetric aliasing artifacts. In this paper, the optical layout and operation of the IHIP sensor are presented in addition to the radiometric, spectral, and polarimetric calibration techniques used with the system. Spectral and spectropolarimetric results from the laboratory and outdoor tests with the instrument are also presented.

  15. Food inspection using hyperspectral imaging and SVDD

    Science.gov (United States)

    Uslu, Faruk Sukru; Binol, Hamidullah; Bal, Abdullah

    2016-05-01

    Nowadays food inspection and evaluation is becoming significant public issue, therefore robust, fast, and environmentally safe methods are studied instead of human visual assessment. Optical sensing is one of the potential methods with the properties of being non-destructive and accurate. As a remote sensing technology, hyperspectral imaging (HSI) is being successfully applied by researchers because of having both spatial and detailed spectral information about studied material. HSI can be used to inspect food quality and safety estimation such as meat quality assessment, quality evaluation of fish, detection of skin tumors on chicken carcasses, and classification of wheat kernels in the food industry. In this paper, we have implied an experiment to detect fat ratio in ground meat via Support Vector Data Description which is an efficient and robust one-class classifier for HSI. The experiments have been implemented on two different ground meat HSI data sets with different fat percentage. Addition to these implementations, we have also applied bagging technique which is mostly used as an ensemble method to improve the prediction ratio. The results show that the proposed methods produce high detection performance for fat ratio in ground meat.

  16. 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.

  17. A Multichannel Gray Level Co-Occurrence Matrix for Multi/Hyperspectral Image Texture Representation

    Directory of Open Access Journals (Sweden)

    Xin Huang

    2014-09-01

    Full Text Available This study proposes a novel method for multichannel image gray level co-occurrence matrix (GLCM texture representation. It is well known that the standard procedure for the automatic extraction of GLCM textures is based on a mono-spectral image. In real applications, however, the GLCM texture feature extraction always refers to multi/hyperspectral images. The widely used strategy to deal with this issue is to calculate the GLCM from the first principal component or the panchromatic band, which do not include all the useful information. Accordingly, in this study, we propose to represent the multichannel textures for multi/hyperspectral imagery by the use of: (1 clustering algorithms; and (2 sparse representation, respectively. In this way, the multi/hyperspectral images can be described using a series of quantized codes or dictionaries, which are more suitable for multichannel texture representation than the traditional methods. Specifically, K-means and fuzzy c-means methods are adopted to generate the codes of an image from the clustering point of view, while a sparse dictionary learning method based on two coding rules is proposed to produce the texture primitives. The proposed multichannel GLCM textural extraction methods were evaluated with four multi/hyperspectral datasets: GeoEye-1 and QuickBird multispectral images of the city of Wuhan, the well-known AVIRIS hyperspectral dataset from the Indian Pines test site, and the HYDICE airborne hyperspectral dataset from the Washington DC Mall. The results show that both the clustering-based and sparsity-based GLCM textures outperform the traditional method (extraction based on the first principal component in terms of classification accuracies in all the experiments.

  18. Spectral-Spatial Classification of Hyperspectral Image Based on Kernel Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Chen Chen

    2014-06-01

    Full Text Available Extreme learning machine (ELM is a single-layer feedforward neural network based classifier that has attracted significant attention in computer vision and pattern recognition due to its fast learning speed and strong generalization. In this paper, we propose to integrate spectral-spatial information for hyperspectral image classification and exploit the benefits of using spatial features for the kernel based ELM (KELM classifier. Specifically, Gabor filtering and multihypothesis (MH prediction preprocessing are two approaches employed for spatial feature extraction. Gabor features have currently been successfully applied for hyperspectral image analysis due to the ability to represent useful spatial information. MH prediction preprocessing makes use of the spatial piecewise-continuous nature of hyperspectral imagery to integrate spectral and spatial information. The proposed Gabor-filtering-based KELM classifier and MH-prediction-based KELM classifier have been validated on two real hyperspectral datasets. Classification results demonstrate that the proposed methods outperform the conventional pixel-wise classifiers as well as Gabor-filtering-based support vector machine (SVM and MH-prediction-based SVM in challenging small training sample size conditions.

  19. 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

  20. Identification and Emotions Experienced after a Celebrity Cancer Death Shape Information Sharing and Prosocial Behavior.

    Science.gov (United States)

    Myrick, Jessica Gall

    2017-06-01

    Based on the previous work investigating public reactions to celebrity cancer deaths as well as on the appraisal theory of emotions, an online survey (N = 641) was conducted after the cancer death of popular sportscaster Stuart Scott. The aim was to better understand how the public shared news and reactions with others and if this social sharing impacted prosocial cancer-related behaviors (e.g., donating, volunteering, talking to others about cancer research). Two hierarchical logistic regression models were run. In the first, identification with Scott and emotional reactions to hearing about his death were significant predictors of sharing, even after controlling for demographics. In the second, feeling hopeful and having shared information with others predicted prosocial cancer-related behaviors. These results suggest promising strategies for designing more effective cancer awareness messages and fundraising campaigns after celebrity cancer announcements.

  1. Information/testing strategy for identification of substances with endocrine disrupting properties

    DEFF Research Database (Denmark)

    Hass, Ulla; Christiansen, Sofie; Bjerregaard, Poul

    . The overall scope of this project is to provide a science based input to the ongoing work in EU with regard to endocrine disruptors, i.e. the development of criteria for identification, REACH review on EDs and the revised strategy for the future work on endocrine disruptors, focusing on adequate detection......This report has been prepared by the Danish Centre on Endocrine Disrupters (CeHoS) as a project contracted by the Danish Environmental Protection Agency. The Danish Centre on Endocrine Disrupters is an interdisciplinary scientific network without walls. The main purpose of the Centre is to build...... and gather new knowledge on endocrine disrupters (EDs) with the focus on providing information requested for the preventive work of the regulatory authorities. The Centre is financed by the Ministry of the Environment and the scientific work programme is followed by an international scientific advisory board...

  2. LWIR hyperspectral imaging application and detection of chemical precursors

    Science.gov (United States)

    Lavoie, Hugo; Thériault, Jean-Marc; Bouffard, François; Puckrin, Eldon; Dubé, Denis

    2012-10-01

    Detection and identification of Toxic industrial chemicals (TICs) represent a major challenge to protect and sustain first responder and public security. In this context, passive Hyperspectral Imaging (HSI) is a promising technology for the standoff detection and identification of chemical vapors emanating from a distant location. To investigate this method, the Department of National Defense and Public Safety Canada have mandated Defense Research and Development Canada (DRDC) - Valcartier to develop and test Very Long Wave Infrared (VLWIR) HSI sensors for standoff detection. The initial effort was focused to address the standoff detection and identification of toxic industrial chemicals (TICs), surrogates and precursors. Sensors such as the Improved Compact ATmospheric Sounding Interferometer (iCATSI) and the Multi-option Differential Detection and Imaging Fourier Spectrometer (MoDDIFS) were developed for this application. This paper presents the sensor developments and preliminary results of standoff detection and identification of TICs and precursors. The iCATSI and MoDDIFS sensors are based on the optical differential Fourier-transform infrared (FTIR) radiometric technology and are able to detect, spectrally resolve and identify small leak at ranges in excess of 1 km. Results from a series of trials in asymmetric threat type scenarios are reported. These results serve to establish the potential of passive standoff HSI detection of TICs, precursors and surrogates.

  3. Parallel hyperspectral image reconstruction using random projections

    Science.gov (United States)

    Sevilla, Jorge; Martín, Gabriel; Nascimento, José M. P.

    2016-10-01

    Spaceborne sensors systems are characterized by scarce onboard computing and storage resources and by communication links with reduced bandwidth. Random projections techniques have been demonstrated as an effective and very light way to reduce the number of measurements in hyperspectral data, thus, the data to be transmitted to the Earth station is reduced. However, the reconstruction of the original data from the random projections may be computationally expensive. SpeCA is a blind hyperspectral reconstruction technique that exploits the fact that hyperspectral vectors often belong to a low dimensional subspace. SpeCA has shown promising results in the task of recovering hyperspectral data from a reduced number of random measurements. In this manuscript we focus on the implementation of the SpeCA algorithm for graphics processing units (GPU) using the compute unified device architecture (CUDA). Experimental results conducted using synthetic and real hyperspectral datasets on the GPU architecture by NVIDIA: GeForce GTX 980, reveal that the use of GPUs can provide real-time reconstruction. The achieved speedup is up to 22 times when compared with the processing time of SpeCA running on one core of the Intel i7-4790K CPU (3.4GHz), with 32 Gbyte memory.

  4. 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.

  5. 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.

  6. 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.

  7. Inertial parameter identification using contact force information for an unknown object captured by a space manipulator

    Science.gov (United States)

    Chu, Zhongyi; Ma, Ye; Hou, Yueyang; Wang, Fengwen

    2017-02-01

    This paper presents a novel identification method for the intact inertial parameters of an unknown object in space captured by a manipulator in a space robotic system. With strong dynamic and kinematic coupling existing in the robotic system, the inertial parameter identification of the unknown object is essential for the ideal control strategy based on changes in the attitude and trajectory of the space robot via capturing operations. Conventional studies merely refer to the principle and theory of identification, and an error analysis process of identification is deficient for a practical scenario. To solve this issue, an analysis of the effect of errors on identification is illustrated first, and the accumulation of measurement or estimation errors causing poor identification precision is demonstrated. Meanwhile, a modified identification equation incorporating the contact force, as well as the force/torque of the end-effector, is proposed to weaken the accumulation of errors and improve the identification accuracy. Furthermore, considering a severe disturbance condition caused by various measured noises, the hybrid immune algorithm, Recursive Least Squares and Affine Projection Sign Algorithm (RLS-APSA), is employed to decode the modified identification equation to ensure a stable identification property. Finally, to verify the validity of the proposed identification method, the co-simulation of ADAMS-MATLAB is implemented by multi-degree of freedom models of a space robotic system, and the numerical results show a precise and stable identification performance, which is able to guarantee the execution of aerospace operations and prevent failed control strategies.

  8. 77 FR 57055 - Agency Information Collection Activities; Proposed Collection; Unique Device Identification...

    Science.gov (United States)

    2012-09-17

    ... Identification System; Extension of Comment Period AGENCY: Food and Drug Administration, HHS. ACTION... PRA) associated with the proposed rule, Unique Device Identification System, that appeared in the... Identification System. The Agency has received requests for a 45-day extension of the comment period for the...

  9. 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.

  10. Single aflatoxin contaminated corn kernel analysis with fluorescence hyperspectral image

    Science.gov (United States)

    Yao, Haibo; Hruska, Zuzana; Kincaid, Russell; Ononye, Ambrose; Brown, Robert L.; Cleveland, Thomas E.

    2010-04-01

    Aflatoxins are toxic secondary metabolites of the fungi Aspergillus flavus and Aspergillus parasiticus, among others. Aflatoxin contaminated corn is toxic to domestic animals when ingested in feed and is a known carcinogen associated with liver and lung cancer in humans. Consequently, aflatoxin levels in food and feed are regulated by the Food and Drug Administration (FDA) in the US, allowing 20 ppb (parts per billion) limits in food and 100 ppb in feed for interstate commerce. Currently, aflatoxin detection and quantification methods are based on analytical tests including thin-layer chromatography (TCL) and high performance liquid chromatography (HPLC). These analytical tests require the destruction of samples, and are costly and time consuming. Thus, the ability to detect aflatoxin in a rapid, nondestructive way is crucial to the grain industry, particularly to corn industry. Hyperspectral imaging technology offers a non-invasive approach toward screening for food safety inspection and quality control based on its spectral signature. The focus of this paper is to classify aflatoxin contaminated single corn kernels using fluorescence hyperspectral imagery. Field inoculated corn kernels were used in the study. Contaminated and control kernels under long wavelength ultraviolet excitation were imaged using a visible near-infrared (VNIR) hyperspectral camera. The imaged kernels were chemically analyzed to provide reference information for image analysis. This paper describes a procedure to process corn kernels located in different images for statistical training and classification. Two classification algorithms, Maximum Likelihood and Binary Encoding, were used to classify each corn kernel into "control" or "contaminated" through pixel classification. The Binary Encoding approach had a slightly better performance with accuracy equals to 87% or 88% when 20 ppb or 100 ppb was used as classification threshold, respectively.

  11. Hyperspectral Image Sharpening Based on Ehlers Fusion

    Science.gov (United States)

    Xu, S.; Ehlers, M.

    2017-09-01

    As the application of hyperspectral images is increasing, many researchers attempt to extend existing pansharpening techniques to hyperspectral images. This paper focuses on the application of Ehlers fusion to hyperspectral image sharpening. Ehlers fusion involves two crucial algorithms: filter technique in the frequency domain and intensity transform. In this study, different filter types and intensity transform methods were analysed separately. With a combination of filter types and intensity transforms, the fusion procedure was implemented to test data sets. The spectral profiles of the pixels of the images were then used as a tool to control the quality of the fused image. Finally, the performance of Ehlers fusion is compared with Principle Component (PC) analysis, Gram-Schmidt transform (Gram-Schmidt), High-Pass Filtering in the spatial domain (HPF), and Wavelet Principal Component (Wavelet-PC) analysis using the same input data. The comparison shows that Ehlers high-pass filter fusion shows outstanding performance both on spatial enhancement and colour preservation.

  12. Sparse representation-based color visualization method for hyperspectral imaging

    Science.gov (United States)

    Wang, Li-Guo; Liu, Dan-Feng; Zhao, Liang

    2013-06-01

    In this paper, we designed a color visualization model for sparse representation of the whole hyperspectral image, in which, not only the spectral information in the sparse representation but also the spatial information of the whole image is retained. After the sparse representation, the color labels of the effective elements of the sparse coding dictionary are selected according to the sparse coefficient and then the mixed images are displayed. The generated images maintain spectral distance preservation and have good separability. For local ground objects, the proposed single-pixel mixed array and improved oriented sliver textures methods are integrated to display the specific composition of each pixel. This avoids the confusion of the color presentation in the mixed-pixel color display and can also be used to reconstruct the original hyperspectral data. Finally, the model effectiveness was proved using real data. This method is promising and can find use in many fields, such as energy exploration, environmental monitoring, disaster warning, and so on.

  13. Rapid identification of paragonimiasis foci by lay informants in Lao People's Democratic Republic.

    Directory of Open Access Journals (Sweden)

    Peter Odermatt

    Full Text Available BACKGROUND: Paragonimiasis is a food-borne trematodiasis leading to lung disease. Worldwide, an estimated 21 million people are infected. Foci of ongoing transmission remain often unnoticed. We evaluated a simple questionnaire approach using lay-informants at the village level to identify paragonimiasis foci and suspected paragonimiasis cases. METHODOLOGY/PRINCIPAL FINDINGS: The study was carried out in an endemic area of Lao People's Democratic Republic. Leaders of 49 remote villages in northern Vientiane Province were asked to notify suspected paragonimiasis patients using a four-item questionnaire sent through administrative channels: persons responding positively for having chronic cough (more than 3 weeks and/or blood in sputum with or without fever. We validated the village leaders' reports in ten representative villages with a door-to-door survey. We examined three sputa of suspected patients for the presence of Paragonimus eggs and acid fast bacilli. 91.8% of village leaders participated and notified a total of 220 suspected patients; 76.2% were eventually confirmed; an additional 138 suspected cases were found in the survey. Sensitivity of village leaders' notice for "chronic cough" and "blood in sputum" was 100%; "blood in sputum" alone reached a sensitivity of 85.7%. SIGNIFICANCE: Our approach led to the identification of three previously unknown foci of transmission. A rapid and simple lay-informant questionnaire approach is a promising low-cost community diagnostic tool of paragonimiasis control programs.

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

    Energy Technology Data Exchange (ETDEWEB)

    Ebihara, Kenichi [Japan Atomic Energy Research Inst., Tokai, Ibaraki (Japan). Tokai Research Establishment

    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)

  15. Identification at the crime scene: The sooner, the better? The interpretation of rapid identification information by CSIs at the crime scene.

    Science.gov (United States)

    de Gruijter, Madeleine; Nee, Claire; de Poot, Christianne J

    2017-07-01

    New technologies will allow Crime Scene Investigators (CSIs) in the near future to analyse traces at the crime scene and receive identification information while still conducting the investigation. These developments could have considerable effects on the way an investigation is conducted. CSIs may start reasoning based on possible database-matches which could influence scenario formation (i.e. the construction of narratives that explain the observed traces) during very early phases of the investigation. The goal of this study is to gain more insight into the influence of the rapid identification information on the reconstruction of the crime and the evaluation of traces by addressing two questions, namely 1) is scenario formation influenced from the moment that ID information is provided and 2) do database matches influence the evaluation of traces and the reconstruction of the crime. We asked 48 CSIs from England to investigate a potential murder crime scene on a computer. Our findings show that the interpretation of the crime scene by CSIs is affected by the moment identification information is provided. This information has a higher influence on scenario formation when provided after an initial scenario has been formed. Also, CSIs seem to attach great value to traces that produce matches with databases and hence yield a name of a known person. Similar traces that did not provide matches were considered less important. We question whether this kind of selective attention is desirable as it may cause ignorance of other relevant information at the crime scene. Copyright © 2017 The Chartered Society of Forensic Sciences. Published by Elsevier B.V. All rights reserved.

  16. Use of information about maternal distress and negative life events to facilitate identification of psychosocial problems in children.

    Science.gov (United States)

    Yerkey, Thomas M; Wildman, Beth G

    2004-06-01

    Despite the availability of effective screening measures, primary care physicians fail to identify and manage many children with psychosocial problems. Physicians often have information about significant negative events in a child's life. The present study evaluated the potential utility of using information about negative life events to facilitate physician identification of children with psychosocial problems. Negative life events, maternal distress and child psychosocial functioning measures were completed by 185 mothers of children, aged 4-12 years. Family physicians provided data about the children's psychosocial functioning. Mothers identified 15.1% (n = 28) of the children as having psychosocial problems. Physicians correctly identified 21% (n = 6) of these at-risk children. Physician use of negative life events would have led to the identification of 39.2% (n = 11) at-risk children. Information about maternal distress and negative life events would have resulted in an additional 18% (n = 5) of children identified by the physicians. Information about maternal distress alone would have resulted in an identification rate of 53.5% (n = 15). Using information about negative events in a child's life, physicians could improve their rate of identification of children with psychosocial problems. Children who have had more than two negative events in their lives are at increased risk for psychosocial problems.

  17. Quantitative interpretation of mineral hyperspectral images based on principal component analysis and independent component analysis methods.

    Science.gov (United States)

    Jiang, Xiping; Jiang, Yu; Wu, Fang; Wu, Fenghuang

    2014-01-01

    Interpretation of mineral hyperspectral images provides large amounts of high-dimensional data, which is often complicated by mixed pixels. The quantitative interpretation of hyperspectral images is known to be extremely difficult when three types of information are unknown, namely, the number of pure pixels, the spectrum of pure pixels, and the mixing matrix. The problem is made even more complex by the disturbance of noise. The key to interpreting abstract mineral component information, i.e., pixel unmixing and abundance inversion, is how to effectively reduce noise, dimension, and redundancy. A three-step procedure is developed in this study for quantitative interpretation of hyperspectral images. First, the principal component analysis (PCA) method can be used to process the pixel spectrum matrix and keep characteristic vectors with larger eigenvalues. This can effectively reduce the noise and redundancy, which facilitates the abstraction of major component information. Second, the independent component analysis (ICA) method can be used to identify and unmix the pixels based on the linear mixed model. Third, the pure-pixel spectrums can be normalized for abundance inversion, which gives the abundance of each pure pixel. In numerical experiments, both simulation data and actual data were used to demonstrate the performance of our three-step procedure. Under simulation data, the results of our procedure were compared with theoretical values. Under the actual data measured from core hyperspectral images, the results obtained through our algorithm are compared with those of similar software (Mineral Spectral Analysis 1.0, Nanjing Institute of Geology and Mineral Resources). The comparisons show that our method is effective and can provide reference for quantitative interpretation of hyperspectral images.

  18. Classification of Peronospora infected grapevine leaves with the use of hyperspectral imaging analysis

    Science.gov (United States)

    Serranti, S.; Bonifazi, G.; Luciani, V.; D'Aniello, L.

    2017-05-01

    The present work explores the possible utilization of hyperspectral devices, following a proximity based approach, for the diagnosis of Peronospora infection in the vineyards. It compares the performance of two hyperspectral cameras, characterized by different spectral acquisition ranges, in the identification of different levels of infection as detectable from the analysis of the leaf surface. For this purpose, healthy grapevine leaves and leaves affected by a different grade of Peronospora infection have been acquired in laboratory conditions using two different sensing devices: a Specim Imspector V10™ and a Specim Spectral Camera N17™ working in the region between 400-1000 nm and 1000-1700 nm, respectively. A Partial Least Squares Discriminant Analysis (PLS-DA) model has been built to perform the classification of healthy, infected and necrotic leaves.

  19. 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.

  20. Ultraspectral: hyperspectral and rf features registered by IFSAR

    Science.gov (United States)

    Szu, Harold H.; Hsu, Charles C.

    2001-03-01

    Hyperspectral remote sensing by air platforms can passively generate over two hundred channels of images of terabyte data the ground surface reflectance/eminence simultaneously, with wavelength ranging from 0.4 to 2.5 micrometers and to include a full infrared spectrum. We have extended the hyperspectral to include RF spectral for both the foliage penetration (from L band 1 GHz to UHF band 0.5 GHz,) using the polarization RF features and the terrain location ID for automatic navigation registration. These generalizations are possible because we have based our design of the foliage penetration (FOPEN) Interferometric Synthetic Aperture Radar (IFSAR) on all digital transceiver array and Field Programmable Gate Arrays (FPGA). We are able to do that, since we have leveraged the ONR 100 dB Digital Array Radar (DAR) for shipboard volume search radar (VSR) using the matured & rugged GaAs cellular phone technology. We study whether the high dynamic range DAR VSR approach can overcome the long baseline terran curvature (that might otherwise not be suited for the FOLPEN low frequency IFSAR). We show the standard deviation of the phase digital resolution better then 1o might overcome the terrain curvature due to low frequency, and long time integration. The applications of this technology include environmental monitoring and mineral exploration and mining, communication and Aided Target Recognition (ATR). The hyperspectral imagery takes the advantage of more unique spectral signature in terms of the massively parallel artificial neural network computation using the unsupervised learning Independent Component Analyses (ICA) algorithm introduced to the Landsat by Szu. The supervised classification is based on the library of spectral signals of known object material characteristics using various constrained versions of the orthogonal subspace projections (OSP) by. In this paper, we combine both the supervised OSP and the unsupervised ICA hyperspectral imaging algorithms. Then

  1. 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.

  2. Application of Peptide LC Retention Time Information in a Discriminant Function for Peptide Identification by Tandem Mass Spectrometry

    Energy Technology Data Exchange (ETDEWEB)

    Strittmatter, Eric F.; Kangas, Lars J.; Petritis, Konstantinos; Mottaz, Heather M.; Anderson, Gordon A.; Shen, Yufeng; Jacobs, Jon M.; Camp, David G.; Smith, Richard D.

    2004-07-09

    We describe the application of a peptide retention time reversed phase liquid chromatography (RPLC) prediction model previously reported (Petritis et al. Anal. Chem. 99, 2002, 11049) for improved peptide identification. The model uses peptide sequence information to generate a theoretical (predicted) elution time that can be compared with the observed elution time. Using data from a set of known proteins, the retention time parameter was incorporated into a discriminant function for use with tandem mass spectrometry (MS/MS) data analyzed with the peptide/protein identification program SEQUEST. For singly charged ions, the number of identifications increased by 12% when the elution time metric is included compared to when mass spectral data is the sole source of information in the context of a Drosophila melanogaster database. A 3-4% improvement was obtained for doubly and triply charged ions for the same biological system. Application to the larger Rattus norvegicus (rat) and human proteome databases resulted in an 8-9% overall increase in the number of identifications, when both the discriminant function and elution time are used. The effect of adding “runner-up” hits (peptide matches that are not the highest scoring for a spectra) from SEQUEST is also explored, and we find that the number of confident identifications is further increased when these hits are also considered. Finally, application of the discriminant functions derived in this work with ~2.2 million spectra from 330 LC-MS/MS analyses of peptides from human plasma protein resulted in a 19% increase in confident peptide identifications (9551 vs 8049) using elution time information. Further improvements from the use of elution time information can be expected as both the experimental control of elution time reproducibility and the predictive capability are improved.

  3. Detection of chemical pollutants by passive LWIR hyperspectral imaging

    Science.gov (United States)

    Lavoie, Hugo; Thériault, Jean-Marc; Bouffard, François; Puckrin, Eldon; Dubé, Denis

    2012-09-01

    Toxic industrial chemicals (TICs) represent a major threat to public health and security. Their detection constitutes a real challenge to security and first responder's communities. One promising detection method is based on the passive standoff identification of chemical vapors emanating from the laboratory under surveillance. To investigate this method, the Department of National Defense and Public Safety Canada have mandated Defense Research and Development Canada (DRDC) - Valcartier to develop and test passive Long Wave Infrared (LWIR) hyperspectral imaging (HSI) sensors for standoff detection. The initial effort was focused to address the standoff detection and identification of toxic industrial chemicals (TICs) and precursors. Sensors such as the Multi-option Differential Detection and Imaging Fourier Spectrometer (MoDDIFS) and the Improved Compact ATmospheric Sounding Interferometer (iCATSI) were developed for this application. This paper describes the sensor developments and presents initial results of standoff detection and identification of TICs and precursors. The standoff sensors are based on the differential Fourier-transform infrared (FTIR) radiometric technology and are able to detect, spectrally resolve and identify small leak plumes at ranges in excess of 1 km. Results from a series of trials in asymmetric threat type scenarios will be presented. These results will serve to establish the potential of the method for standoff detection of TICs precursors and surrogates.

  4. 40 CFR 91.113 - Requirement of certification-emission control information label and engine identification number.

    Science.gov (United States)

    2010-07-01

    ...; (10) Engine displacement ; and (11) Advertised power; (12) Engine tuneup specifications and... control information label and engine identification number. 91.113 Section 91.113 Protection of... MARINE SPARK-IGNITION ENGINES Emission Standards and Certification Provisions § 91.113 Requirement of...

  5. A method of minimum volume simplex analysis constrained unmixing for hyperspectral image

    Science.gov (United States)

    Zou, Jinlin; Lan, Jinhui; Zeng, Yiliang; Wu, Hongtao

    2017-07-01

    The signal recorded by a low resolution hyperspectral remote sensor from a given pixel, letting alone the effects of the complex terrain, is a mixture of substances. To improve the accuracy of classification and sub-pixel object detection, hyperspectral unmixing(HU) is a frontier-line in remote sensing area. Unmixing algorithm based on geometric has become popular since the hyperspectral image possesses abundant spectral information and the mixed model is easy to understand. However, most of the algorithms are based on pure pixel assumption, and since the non-linear mixed model is complex, it is hard to obtain the optimal endmembers especially under a highly mixed spectral data. To provide a simple but accurate method, we propose a minimum volume simplex analysis constrained (MVSAC) unmixing algorithm. The proposed approach combines the algebraic constraints that are inherent to the convex minimum volume with abundance soft constraint. While considering abundance fraction, we can obtain the pure endmember set and abundance fraction correspondingly, and the final unmixing result is closer to reality and has better accuracy. We illustrate the performance of the proposed algorithm in unmixing simulated data and real hyperspectral data, and the result indicates that the proposed method can obtain the distinct signatures correctly without redundant endmember and yields much better performance than the pure pixel based algorithm.

  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. Hyperspectral visible-near infrared imaging for the detection of waxed rice

    Science.gov (United States)

    Zhao, Mantong

    2014-11-01

    Presently, unscrupulous traders in the market use the industrial wax to wax the rice. The industrial wax is a particularly hazardous substance. Visible-near infrared hyperspectral images (400-1,000 nm) can be used for the detection of the waxed rice and the non-waxed rice. This study was carried out to find effective testing methods based on the visible-near infrared imaging spectrometry to detect whether the rice was waxed or not. An imaging spectroscopy system was assembled to acquire hyperspectral images from 80 grains of waxed rice and 80 grains of non-waxed rice over visible and near infrared spectral region. Spectra of 100 grains of rice were analyzed by principal component analysis (PCA) to extract the information of hyperspectral images. PCA provides an effective compressed representation of the spectral signal of each pixel in the spectral domain. We used PCA to acquire the effective wavelengths from the spectra. Based on the effective wavelengths, the predict models were set up by using partial least squares (PLS) analysis and linear discriminant analysis (LDA). Also, compared with the PLS of 80% for the waxed rice and 86.7% for the non-waxed rice detection rate, LDA gives 93.3% and 96.7% detection rate. The results demonstrated that the LDA could detect the waxed rice better, while illustrating the hyperspectral imaging technique with the visible-near infrared region could be a reliable method for the waxed rice detection.

  8. Quantitative Estimating Salt Content of Saline Soil Using Laboratory Hyperspectral Data Treated by Fractional Derivative

    Directory of Open Access Journals (Sweden)

    Dong Zhang

    2016-01-01

    Full Text Available Most present researches on estimation of soil salinity by hyperspectral data have focused on the spectral reflectance or their integer derivatives but ignored the fractional derivative information of hyperspectral data. Motivated by this situation, the selected study area is the Ebinur Lake basin located in the southwest border in the Xinjiang Uygur Autonomous Region, China, with severe salinization. The field work was conducted from 15 to 25 October, 2014, and a total of 180 soil samples were collected from 45 sampling sites; after measuring the soil salt content and spectral reflectance in the laboratory, the range from 0 to 2 was divided into 11 orders (interval 0.2 and then the hyperspectral data were treated by 4 kinds of mathematical transformations and 11 orders of fractional derivatives. Combined with the soil salt content, partial least square regression method was applied for model calibrations and predictions and some indexes were used to evaluate the performance of models. The results showed that the retrieval model built up by 250 bands based on 1.2-order derivative of 1/lg⁡R had excellent capacity of estimating soil salt content in the study area (RMSEC=14.685 g/kg, RMSEP=14.713 g/kg, R2C=0.782, R2P=0.768, and RPD = 2.080. This study provides an application reference for quantitative estimations of other land surface parameters and some other applications on hyperspectral technology.

  9. Differentiating Biological Colours with Few and Many Sensors: Spectral Reconstruction with RGB and Hyperspectral Cameras.

    Directory of Open Access Journals (Sweden)

    Jair E Garcia

    Full Text Available The ability to discriminate between two similar or progressively dissimilar colours is important for many animals as it allows for accurately interpreting visual signals produced by key target stimuli or distractor information. Spectrophotometry objectively measures the spectral characteristics of these signals, but is often limited to point samples that could underestimate spectral variability within a single sample. Algorithms for RGB images and digital imaging devices with many more than three channels, hyperspectral cameras, have been recently developed to produce image spectrophotometers to recover reflectance spectra at individual pixel locations. We compare a linearised RGB and a hyperspectral camera in terms of their individual capacities to discriminate between colour targets of varying perceptual similarity for a human observer.(1 The colour discrimination power of the RGB device is dependent on colour similarity between the samples whilst the hyperspectral device enables the reconstruction of a unique spectrum for each sampled pixel location independently from their chromatic appearance. (2 Uncertainty associated with spectral reconstruction from RGB responses results from the joint effect of metamerism and spectral variability within a single sample.(1 RGB devices give a valuable insight into the limitations of colour discrimination with a low number of photoreceptors, as the principles involved in the interpretation of photoreceptor signals in trichromatic animals also apply to RGB camera responses. (2 The hyperspectral camera architecture provides means to explore other important aspects of colour vision like the perception of certain types of camouflage and colour constancy where multiple, narrow-band sensors increase resolution.

  10. 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.

  11. 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.

  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. 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

  14. Terrestrial hyperspectral image shadow restoration through fusion with terrestrial lidar

    Science.gov (United States)

    Hartzell, Preston J.; Glennie, Craig L.; Finnegan, David C.; Hauser, Darren L.

    2017-05-01

    Recent advances in remote sensing technology have expanded the acquisition and fusion of active lidar and passive hyperspectral imagery (HSI) from exclusively airborne observations to include terrestrial modalities. In contrast to airborne collection geometry, hyperspectral imagery captured from terrestrial cameras is prone to extensive solar shadowing on vertical surfaces leading to reductions in pixel classification accuracies or outright removal of shadowed areas from subsequent analysis tasks. We demonstrate the use of lidar spatial information for sub-pixel HSI shadow detection and the restoration of shadowed pixel spectra via empirical methods that utilize sunlit and shadowed pixels of similar material composition. We examine the effectiveness of radiometrically calibrated lidar intensity in identifying these similar materials in sun and shade conditions and further evaluate a restoration technique that leverages ratios derived from the overlapping lidar laser and HSI wavelengths. Simulations of multiple lidar wavelengths, i.e., multispectral lidar, indicate the potential for HSI spectral restoration that is independent of the complexity and costs associated with rigorous radiometric transfer models, which have yet to be developed for horizontal-viewing terrestrial HSI sensors. The spectral restoration performance of shadowed HSI pixels is quantified for imagery of a geologic outcrop through improvements in spectral shape, spectral scale, and HSI band correlation.

  15. 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.

  16. Near infrared hyperspectral imaging system for root phenotyping

    Science.gov (United States)

    Arnold, Thomas; Leitner, Raimund; Bodner, Gernot

    2017-05-01

    This paper presents the development and application of a hyper-spectral imaging system for root phenotyping. For sustainable plant production root systems optimized for growing conditions in the field are required. Therefore, the presented system is used for the research in the field of plant drought resistance. The system is used to acquire spatially resolved near infrared (NIR) spectroscopy data of rhizoboxes. In contrast to using visible light (380 nm-780 nm) the NIR wavelength range (900 nm-1700 nm) allows to discriminate essential features for the root segmentation and water distribution mappings. The increased image contrast in the NIR range allows roots to be segmented from soil and additional information, e.g. basic root-architecture, to be extracted. In addition, the water absorption bands in the NIR wavelength range can be used to determine the water content and to estimate the age of the roots. In this paper the hardware setup of the hyper-spectral root imaging system, the data analysis, the soil water content estimations and the root segmentation using different methods to optimize separation between roots and soil, both constituting complex materials of variable properties, are presented.

  17. Graph based hyperspectral image segmentation with improved affinity matrix

    Science.gov (United States)

    Fan, Lei; Messinger, David W.

    2014-06-01

    Image segmentation and clustering is a method to extract a set of components whose members are similar in some way. Instead of focusing on the consistencies of local image characteristics such as borders and regions in a perceptual way, the spectral graph theoretic approach is based on the eigenvectors of an affinity matrix; therefore it captures perceptually important non-local properties of an image. A typical spectral graph segmentation algorithm, normalized cuts, incorporates both the dissimilarity between groups and similarity within groups by capturing global consistency making the segmentation process more balanced and stable. For spectral graph partitioning, we create a graph-image representation wherein each pixel is taken as a graph node, and two pixels are connected by an edge based on certain similarity criteria. In most cases, nearby pixels are likely to be in the same region, therefore each pixel is connected to its spatial neighbors in the normalized cut algorithm. However, this ignores the difference between distinct groups or the similarity within a group. A hyperspectral image contains high spatial correlation among pixels, but each pixel is better described by its high dimensional spectral feature vector which provides more information when characterizing the similarities among every pair of pixels. Also, to facilitate the fact that boundary usually resides in low density regions in spectral domain, a local density adaptive affinity matrix is presented in this paper. Results will be shown for airborne hyperspectral imagery collected with the HyMAP, AVIRIS, HYDICE sensors.

  18. Hyperspectral imaging for detection of arthritis: feasibility and prospects

    Science.gov (United States)

    Milanic, Matija; Paluchowski, Lukasz A.; Randeberg, Lise L.

    2015-09-01

    Rheumatoid arthritis (RA) is a disease that frequently leads to joint destruction. It has a high incidence rate worldwide, and the disease significantly reduces patients' quality of life. Detecting and treating inflammatory arthritis before structural damage to the joint has occurred is known to be essential for preventing patient disability and pain. Existing diagnostic technologies are expensive, time consuming, and require trained personnel to collect and interpret data. Optical techniques might be a fast, noninvasive alternative. Hyperspectral imaging (HSI) is a noncontact optical technique which provides both spectral and spatial information in one measurement. In this study, the feasibility of HSI in arthritis diagnostics was explored by numerical simulations and optimal imaging parameters were identified. Hyperspectral reflectance and transmission images of RA and normal human joint models were simulated using the Monte Carlo method. The spectral range was 600 to 1100 nm. Characteristic spatial patterns for RA joints and two spectral windows with transmission were identified. The study demonstrated that transmittance images of human joints could be used as one parameter for discrimination between arthritic and unaffected joints. The presented work shows that HSI is a promising imaging modality for the diagnostics and follow-up monitoring of arthritis in small joints.

  19. A new deblurring morphological filter for hyperspectral images

    Science.gov (United States)

    Abdelkawy, Ezz Eldin F.; Mahmoud, Tarek A.; Hussein, Wesam M.

    2011-06-01

    Hyperspectral imaging becomes an important technique that increases the valuable information enclosed within the image. Spectral cube produced by this type of imaging introduces a new material signature known as "spectral signature". This signature is unique for each material as it depends on the molecular composition of the material surface. To produce the spectral cube, a spectrometer should be used in the imagery device to split the electromagnetic energy at different wavelengths before its projection on the imaging array. This spectrometer may be a dispersive element, such as prism and grating, or an electronically tuneable filter. Some of dispersive spectrometers, such as Fourier transform interferometer (FTIR) and image multi-spectral imaging (IMSS), are based on sliding the lenses, or mirrors, along the optical axis which may result in a slightly out-of-focus blurring. Blind deconvolution techniques have been successfully used to decrease this blurring but at the expense of edge sharpening which may be a problem in some applications such as target detection and recognition. In this paper, we introduce a new method to deblurr the hyperspectral images keeping edges as sharp as possible. This is done by firstly detecting the edges locations and then applying a class of morphological filtering. Motivated by the success of threshold decomposition, gradient-based operators are used to detect the locations of these edges followed by an adaptive morphological filter to sharpen these detected edges. Experimental results demonstrate that the performance of the proposed deblurring filter is superior to that of the blind deconvolution methods.

  20. 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.

  1. Ground-based hyperspectral analysis of the urban nightscape

    Science.gov (United States)

    Alamús, Ramon; Bará, Salvador; Corbera, Jordi; Escofet, Jaume; Palà, Vicenç; Pipia, Luca; Tardà, Anna

    2017-02-01

    Airborne hyperspectral cameras provide the basic information to estimate the energy wasted skywards by outdoor lighting systems, as well as to locate and identify their sources. However, a complete characterization of the urban light pollution levels also requires evaluating these effects from the city dwellers standpoint, e.g. the energy waste associated to the excessive illuminance on walls and pavements, light trespass, or the luminance distributions causing potential glare, to mention but a few. On the other hand, the spectral irradiance at the entrance of the human eye is the primary input to evaluate the possible health effects associated with the exposure to artificial light at night, according to the more recent models available in the literature. In this work we demonstrate the possibility of using a hyperspectral imager (routinely used in airborne campaigns) to measure the ground-level spectral radiance of the urban nightscape and to retrieve several magnitudes of interest for light pollution studies. We also present the preliminary results from a field campaign carried out in the downtown of Barcelona.

  2. 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.

  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. 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.

  5. Parallel hyperspectral compressive sensing method on GPU

    Science.gov (United States)

    Bernabé, Sergio; Martín, Gabriel; Nascimento, José M. P.

    2015-10-01

    Remote hyperspectral sensors collect large amounts of data per flight usually with low spatial resolution. It is known that the bandwidth connection between the satellite/airborne platform and the ground station is reduced, thus a compression onboard method is desirable to reduce the amount of data to be transmitted. This paper presents a parallel implementation of an compressive sensing method, called parallel hyperspectral coded aperture (P-HYCA), for graphics processing units (GPU) using the compute unified device architecture (CUDA). This method takes into account two main properties of hyperspectral dataset, namely the high correlation existing among the spectral bands and the generally low number of endmembers needed to explain the data, which largely reduces the number of measurements necessary to correctly reconstruct the original data. Experimental results conducted using synthetic and real hyperspectral datasets on two different GPU architectures by NVIDIA: GeForce GTX 590 and GeForce GTX TITAN, reveal that the use of GPUs can provide real-time compressive sensing performance. The achieved speedup is up to 20 times when compared with the processing time of HYCA running on one core of the Intel i7-2600 CPU (3.4GHz), with 16 Gbyte memory.

  6. GPU Lossless Hyperspectral Data Compression System

    Science.gov (United States)

    Aranki, Nazeeh I.; Keymeulen, Didier; Kiely, Aaron B.; Klimesh, Matthew A.

    2014-01-01

    Hyperspectral imaging systems onboard aircraft or spacecraft can acquire large amounts of data, putting a strain on limited downlink and storage resources. Onboard data compression can mitigate this problem but may require a system capable of a high throughput. In order to achieve a high throughput with a software compressor, a graphics processing unit (GPU) implementation of a compressor was developed targeting the current state-of-the-art GPUs from NVIDIA(R). The implementation is based on the fast lossless (FL) compression algorithm reported in "Fast Lossless Compression of Multispectral-Image Data" (NPO- 42517), NASA Tech Briefs, Vol. 30, No. 8 (August 2006), page 26, which operates on hyperspectral data and achieves excellent compression performance while having low complexity. The FL compressor uses an adaptive filtering method and achieves state-of-the-art performance in both compression effectiveness and low complexity. The new Consultative Committee for Space Data Systems (CCSDS) Standard for Lossless Multispectral & Hyperspectral image compression (CCSDS 123) is based on the FL compressor. The software makes use of the highly-parallel processing capability of GPUs to achieve a throughput at least six times higher than that of a software implementation running on a single-core CPU. This implementation provides a practical real-time solution for compression of data from airborne hyperspectral instruments.

  7. Mercury `s composition studied by the Visible and Infrared Hyperspectral Imager (VIHI) of the Bepi colombo MPO Mission

    Science.gov (United States)

    de Sanctis, Maria Cristina; Capaccioni, Fabrizio; Filacchione, Gianrico; Ammannito, Eleonora; Doressoundiram, Alain; Erard, Stephane; Sgavetti, Maria

    We describe the contributions that we expect by the Visual and Infrared Hyper-spectral Imager channel (VIHI) to make towards increased knowledge and understanding of Mercury's surface and composition. Due to the difficulties of observing Mercury from ground, relatively little is known about its surface composition, and Mercury spectra are vulnerable to incomplete removal of telluric absorptions. VIHI will provide valuable information to help constrain the Mercury formation models, through the identification of the major silicates compounds and the mapping of their spatial distribution over the entire surface with a spatial resolution better than 500m. The selected spectral range 400 - 2000 nm, encompassing all the major diagnostic bands of the expected minerals, coupled with a spectral sampling of 6.25 nm is suitable to perform the required mineralogical study of the Mercury surface. Determination of surface mineralogy and the origin of geologically significant morphologic features are among the primary objectives of VIHI. The imaging capability coupled with the hyperspectral data of VIHI will be a powerful tool for discriminating, identifying and mapping variations in the surface reflectance spectrum. A high spatial sampling capability will allow to investigate in detail the boundary zones between different geologic regions as well as local surface features (craters, scarps, lava flows, ejecta), thus relating the observed morphology to spectral characteristic. Such analysis will give information on the processes that have been dominant in planet history: tectonics, volcanism and cratering. Preliminary inspection of some of the MESSENGER fly-by images indicates that there may be multiple generations of volcanic activity preserved on Mercury. VIHI will help in the identification of the products of separate episodes of volcanism. Using the measurements of this products we will have an opportunity to study Mercury's history of magma genesis and magma fractionation

  8. Schroedinger Eigenmaps with nondiagonal potentials for spatial-spectral clustering of hyperspectral imagery

    Science.gov (United States)

    Cahill, Nathan D.; Czaja, Wojciech; Messinger, David W.

    2014-06-01

    Schroedinger Eigenmaps (SE) has recently emerged as a powerful graph-based technique for semi-supervised manifold learning and recovery. By extending the Laplacian of a graph constructed from hyperspectral imagery to incorporate barrier or cluster potentials, SE enables machine learning techniques that employ expert/labeled information provided at a subset of pixels. In this paper, we show how different types of nondiagonal potentials can be used within the SE framework in a way that allows for the integration of spatial and spectral information in unsupervised manifold learning and recovery. The nondiagonal potentials encode spatial proximity, which when combined with the spectral proximity information in the original graph, yields a framework that is competitive with state-of-the-art spectral/spatial fusion approaches for clustering and subsequent classification of hyperspectral image data.

  9. 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

  10. HYPERSPECTRAL DATA CLASSIFICATION USING FACTOR GRAPHS

    Directory of Open Access Journals (Sweden)

    A. Makarau

    2012-07-01

    Full Text Available Accurate classification of hyperspectral data is still a competitive task and new classification methods are developed to achieve desired tasks of hyperspectral data use. The objective of this paper is to develop a new method for hyperspectral data classification ensuring the classification model properties like transferability, generalization, probabilistic interpretation, etc. While factor graphs (undirected graphical models are unfortunately not widely employed in remote sensing tasks, these models possess important properties such as representation of complex systems to model estimation/decision making tasks. In this paper we present a new method for hyperspectral data classification using factor graphs. Factor graph (a bipartite graph consisting of variables and factor vertices allows factorization of a more complex function leading to definition of variables (employed to store input data, latent variables (allow to bridge abstract class to data, and factors (defining prior probabilities for spectral features and abstract classes; input data mapping to spectral features mixture and further bridging of the mixture to an abstract class. Latent variables play an important role by defining two-level mapping of the input spectral features to a class. Configuration (learning on training data of the model allows calculating a parameter set for the model to bridge the input data to a class. The classification algorithm is as follows. Spectral bands are separately pre-processed (unsupervised clustering is used to be defined on a finite domain (alphabet leading to a representation of the data on multinomial distribution. The represented hyperspectral data is used as input evidence (evidence vector is selected pixelwise in a configured factor graph and an inference is run resulting in the posterior probability. Variational inference (Mean field allows to obtain plausible results with a low calculation time. Calculating the posterior probability for

  11. An identification and informative guide to the Tenebrionidae of Malta (Coleoptera)

    OpenAIRE

    Lillig, Martin; Borg Barthet, Henry; Mifsud, David

    2012-01-01

    A simplified dichotomous key for the identification of the 61 species of Maltese Tenebrionidae is provided. In order to aid further identification, colour photographs of most species are included and for each species ecological and other relevant notes are provided. Distribution maps of 57 species are presented

  12. Q-bank, a database with information for identification of plant quarantine plant pest and diseases

    NARCIS (Netherlands)

    Bonants, P.J.M.; Edema, M.J.; Robert, V.

    2013-01-01

    This paper describes the database Q-bank (www.q-bank.eu). This freely accessible database contains data on plant pathogenic quarantine organisms to allow fast and reliable identification. Development of accurate identification tools for plant pests is vital to support European Plant Health Policies.

  13. 76 FR 16609 - Proposed Information Collection; Comment Request; Identification of Human Cell Lines Project

    Science.gov (United States)

    2011-03-24

    ...; Identification of Human Cell Lines Project AGENCY: National Institute of Standards and Technology (NIST...) profiling up to 1500 human cell line samples as part of the Identification of Human Cell Lines Project. All... in Designation: ASN-0002 Authentication of Human Cell Lines: Standardization of STR Profiling by the...

  14. Identification and uncertainty analysis of a hydrological water quality model with varying input data information content

    Science.gov (United States)

    Jiang, Sanyuan; Jomaa, Seifeddine; Rode, Michael

    2013-04-01

    the on-going work MCMC is used to investigate effects of calibration data information content in terms of observation variable and temporal and special resolution on parameter identification and predictive uncertainty.

  15. Hyperspectral imaging using near infrared spectroscopy to monitor coat thickness uniformity in the manufacture of a transdermal drug delivery system.

    Science.gov (United States)

    Pavurala, Naresh; Xu, Xiaoming; Krishnaiah, Yellela S R

    2017-05-15

    Hyperspectral imaging using near infrared spectroscopy (NIRS) integrates spectroscopy and conventional imaging to obtain both spectral and spatial information of materials. The non-invasive and rapid nature of hyperspectral imaging using NIRS makes it a valuable process analytical technology (PAT) tool for in-process monitoring and control of the manufacturing process for transdermal drug delivery systems (TDS). The focus of this investigation was to develop and validate the use of Near Infra-red (NIR) hyperspectral imaging to monitor coat thickness uniformity, a critical quality attribute (CQA) for TDS. Chemometric analysis was used to process the hyperspectral image and a partial least square (PLS) model was developed to predict the coat thickness of the TDS. The goodness of model fit and prediction were 0.9933 and 0.9933, respectively, indicating an excellent fit to the training data and also good predictability. The % Prediction Error (%PE) for internal and external validation samples was less than 5% confirming the accuracy of the PLS model developed in the present study. The feasibility of the hyperspectral imaging as a real-time process analytical tool for continuous processing was also investigated. When the PLS model was applied to detect deliberate variation in coating thickness, it was able to predict both the small and large variations as well as identify coating defects such as non-uniform regions and presence of air bubbles. Published by Elsevier B.V.

  16. 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.

  17. Evaluation of hyperspectral technology for assessing the presence and severity of peripheral artery disease.

    Science.gov (United States)

    Chin, Jason A; Wang, Edward C; Kibbe, Melina R

    2011-12-01

    Hyperspectral imaging is a novel technology that can noninvasively measure oxyhemoglobin and deoxyhemoglobin concentrations to create an anatomic oxygenation map. It has predicted healing of diabetic foot ulcers; however, its ability to assess peripheral arterial disease (PAD) has not been studied. The aims of this study were to determine if hyperspectral imaging could accurately assess the presence or absence of PAD and accurately predict PAD severity. This prospective study included consecutive consenting patients presenting to the vascular laboratory at the Jesse Brown VA Medical Center during a 10-week period for a lower extremity arterial study, including ankle-brachial index (ABI) and Doppler waveforms. Patients with lower extremity edema were excluded. Patients underwent hyperspectral imaging at nine angiosomes on each extremity. Additional sites were imaged when tissue loss was present. Medical records of enrolled patients were reviewed for demographic data, active medications, surgical history, and other information pertinent to PAD. Patients were separated into no-PAD and PAD groups. Differences in hyperspectral values between the groups were evaluated using the two-tailed t test. Analysis for differences in values over varying severities of PAD, as defined by triphasic, biphasic, or monophasic Doppler waveforms, was conducted using one-way analysis of variance. Hyperspectral values were correlated with the ABI using a Pearson bivariate linear correlation test. The study enrolled 126 patients (252 limbs). After exclusion of 15 patients, 111 patients were left for analysis, including 46 (92 limbs) no-PAD patients and 65 (130 limbs) PAD patients. Groups differed in age, diabetes, coronary artery disease, congestive heart failure, tobacco use, and insulin use. Deoxyhemoglobin values for the plantar metatarsal, arch, and heel angiosomes were significantly different between patients with and without PAD (P Oxyhemoglobin values did not predict the presence or

  18. 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

  19. 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.

  20. Challenges in automatic sorting of construction and demolition waste by hyperspectral imaging

    Science.gov (United States)

    Hollstein, Frank; Cacho, Íñigo; Arnaiz, Sixto; Wohllebe, Markus

    2016-05-01

    EU-28 countries currently generate 460 Mt/year of construction and demolition waste (C&DW) and the generation rate is expected to reach around 570 Mt/year between 2025 and 2030. There is great potential for recycling C&DW materials since they are massively produced and content valuable resources. But new C&DW is more complex than existing one and there is a need for shifting from traditional recycling approaches to novel recycling solutions. One basic step to achieve this objective is an improvement in (automatic) sorting technology. Hyperspectral Imaging is a promising candidate to support the process. However, the industrial distribution of Hyperspectral Imaging in the C&DW recycling branch is currently insufficiently pronounced due to high investment costs, still insufficient robustness of optical sensor hardware in harsh ambient conditions and, because of the need of sensor fusion, not well-engineered special software methods to perform the (on line) sorting tasks. Thereby frame rates of over 300 Hz are needed for a successful sorting result. Currently the biggest challenges with regard to C&DW detection cover the need of overlapping VIS, NIR and SWIR hyperspectral images in time and space, in particular for selective recognition of contaminated particles. In the study on hand a new approach for hyperspectral imagers is presented by exploiting SWIR hyperspectral information in real time (with 300 Hz). The contribution describes both laboratory results with regard to optical detection of the most important C&DW material composites as well as a development path for an industrial implementation in automatic sorting and separation lines. The main focus is placed on the closure of the two recycling circuits "grey to grey" and "red to red" because of their outstanding potential for sustainability in conservation of construction resources.

  1. Retrieval of leaf area index in different plant species using thermal hyperspectral data

    Science.gov (United States)

    Neinavaz, Elnaz; Skidmore, Andrew K.; Darvishzadeh, Roshanak; Groen, Thomas A.

    2016-09-01

    Leaf area index (LAI) is an important variable of terrestrial ecosystems because it is strongly correlated with many ecosystem processes (e.g., water balance and evapotranspiration) and directly related to the plant energy balance and gas exchanges. Although LAI has been accurately predicted using visible and short-wave infrared hyperspectral data (0.3-2.5 μm), LAI estimation using thermal infrared (TIR, 8-14 μm) measurements has not yet been addressed. The novel approach of this study is to evaluate the retrieval of LAI using TIR hyperspectral data. The leaf area indices were destructively acquired for four plant species: Azalea japonica, Buxussempervirens, Euonymus japonicus, and Ficus benjamina. Canopy emissivity spectral measurements were obtained under controlled laboratory conditions using a MIDAC (M4401-F) spectrometer. The LAI retrieval was assessed using a partial least squares regression (PLSR), artificial neural networks (ANNs), and narrow band indices calculated from all possible combinations of waveband pairs for three vegetation indices including simple difference, simple ratio, and normalized difference. ANNs retrieved LAI more accurately than PLSR and vegetation indices (0.67 < R2 < 0.95 versus 11.54% < RMSEcv < 31.23%). The accuracy of LAI retrieval did not differ significantly between the vegetation indices. The results revealed that wavebands from the 8-12 μm region contain relevant information for LAI estimation, irrespective of the chosen vegetation index. Moreover, they demonstrated that LAI may be successfully predicted from TIR hyperspectral data, even for higher values of LAI (LAI ⩾ 5.5). The study showed the significance of using PLSR and ANNs as multivariate methods compared to the univariate technique (e.g., narrow band vegetation indices) when hyperspectral thermal data is utilized. We thus demonstrated for the first time the potential of hyperspectral thermal data to accurately retrieve LAI.

  2. Differentiating aquatic plant communities in a eutrophic river using hyperspectral and multispectral remote sensing

    Science.gov (United States)

    Tian, Y.Q.; Yu, Q.; Zimmerman, M.J.; Flint, S.; Waldron, M.C.

    2010-01-01

    This study evaluates the efficacy of remote sensing technology to monitor species composition, areal extent and density of aquatic plants (macrophytes and filamentous algae) in impoundments where their presence may violate water-quality standards. Multispectral satellite (IKONOS) images and more than 500 in situ hyperspectral samples were acquired to map aquatic plant distributions. By analyzing field measurements, we created a library of hyperspectral signatures for a variety of aquatic plant species, associations and densities. We also used three vegetation indices. Normalized Difference Vegetation Index (NDVI), near-infrared (NIR)-Green Angle Index (NGAI) and normalized water absorption depth (DH), at wavelengths 554, 680, 820 and 977 nm to differentiate among aquatic plant species composition, areal density and thickness in cases where hyperspectral analysis yielded potentially ambiguous interpretations. We compared the NDVI derived from IKONOS imagery with the in situ, hyperspectral-derived NDVI. The IKONOS-based images were also compared to data obtained through routine visual observations. Our results confirmed that aquatic species composition alters spectral signatures and affects the accuracy of remote sensing of aquatic plant density. The results also demonstrated that the NGAI has apparent advantages in estimating density over the NDVI and the DH. In the feature space of the three indices, 3D scatter plot analysis revealed that hyperspectral data can differentiate several aquatic plant associations. High-resolution multispectral imagery provided useful information to distinguish among biophysical aquatic plant characteristics. Classification analysis indicated that using satellite imagery to assess Lemna coverage yielded an overall agreement of 79% with visual observations and >90% agreement for the densest aquatic plant coverages. Interpretation of biophysical parameters derived from high-resolution satellite or airborne imagery should prove to be a

  3. Electronic support tools for identification and management of rice weeds in Africa for better-informed agricultural change agents

    Directory of Open Access Journals (Sweden)

    Rodenburg Jonne

    2016-01-01

    Full Text Available We developed an interactive electronic weed identification tool, AFROweeds, and an online network, Weedsbook, for agricultural change agents to aid communication and offer assistance to rice farmers with specific weed problems. We collected quantitative and qualitative data to assess effectiveness and usefulness of these products with potential users. With the online version of AFROweeds, used on an electronic tablet, average weed identification time in the field was 7 min 6 s with 44% successful identifications. Poor mobile network coverage and slow internet were the main reasons for the relative long identification time and low success rate. A second trial was done with the offline version, pre-installed on a tablet. The average identification time was 6 min 34 s, with a success rate of 75%. The online network Weedsbook, established alongside AFROweeds, was assessed by the test users as a useful additional aid, enabling agricultural change agents and agronomists to exchange information or request assistance on all aspects of weeds and weed management. The potential improvements of both products are discussed.

  4. 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.

  5. Parallel implementation of a hyperspectral data geometry-based estimation of number of endmembers algorithm

    Science.gov (United States)

    Bernabé, Sergio; Martin, Gabriel; Botella, Guillermo; Prieto-Matias, Manuel; Plaza, Antonio

    2016-04-01

    In the last years, hyperspectral analysis have been applied in many remote sensing applications. In fact, hyperspectral unmixing has been a challenging task in hyperspectral data exploitation. This process consists of three stages: (i) estimation of the number of pure spectral signatures or endmembers, (ii) automatic identification of the estimated endmembers, and (iii) estimation of the fractional abundance of each endmember in each pixel of the scene. However, unmixing algorithms can be computationally very expensive, a fact that compromises their use in applications under real-time constraints. In recent years, several techniques have been proposed to solve the aforementioned problem but until now, most works have focused on the second and third stages. The execution cost of the first stage is usually lower than the other stages. Indeed, it can be optional if we known a priori this estimation. However, its acceleration on parallel architectures is still an interesting and open problem. In this paper we have addressed this issue focusing on the GENE algorithm, a promising geometry-based proposal introduced in.1 We have evaluated our parallel implementation in terms of both accuracy and computational performance through Monte Carlo simulations for real and synthetic data experiments. Performance results on a modern GPU shows satisfactory 16x speedup factors, which allow us to expect that this method could meet real-time requirements on a fully operational unmixing chain.

  6. 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.

  7. [Hyperspectral Detection Model for Soil Dispersion in Zhouqu Debris Flow Source Region].

    Science.gov (United States)

    Wang, Qin-jun; Wei, Yong-ming; Chen, Yu; Chen, Jia-ge; Lin, Qi-zhong

    2016-02-01

    Sensitive band positions, models and the principles of soil dispersion detected by hyperspectral remote sensing were firstly discussed according to the results of soil dispersive hyperspectral remote sensing experiment. Results showed that, (1) signals and noises could be separated by Fourier transformation. A finely mineral identification system was developed to remove spectral noises and provide highly accurate data for establishing soil dispersive model; (2) Soil dispersive hyperspectral remote sensing model established by the multiple linear regression method was good at soil dispersion forecasting for the high correlation between sensitive bands and the soil dispersions. (3) According to mineral spectra, soil minerals and their absorbed irons were reflected by sensitive bands which revealed reasons causing soils to be dispersive. Sodium was the closest iron correlated with soil dispersion. The secondary was calcite, montmorillonite and illite. However, the correlation between soil dispersion and chlorite, kaolinite, PH value, quartz, potassium feldspar, plagioclase was weak. The main reason was probably that sodium was low in ionic valence, small ionic radius and strong hydration forces; calcite was high water soluble and illite was weak binding forces between two layers under high pH value.

  8. Diffusion Geometry Based Nonlinear Methods for Hyperspectral Change Detection

    Science.gov (United States)

    2010-05-12

    Schaum and A. Stocker, “Hyperspectral change detection and supervised matched filtering based on covariance equalization,” Proceedings of the SPIE, vol...5425, pp. 77- 90 (2004). 10. A. Schaum and A. Stocker, “Linear chromodynamics models for hyperspectral target detection,” Proceedings of the IEEE...Aerospace Conference (February 2003). 11. A. Schaum and A. Stocker, “Linear chromodynamics models for hyperspectral target detection

  9. 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/ .

  10. A Gimbal-Stabilized Compact Hyperspectral Imaging System Project

    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,...

  11. Abundance Estimation of Hyperspectral Data with Low Compressive Sampling Rate

    Science.gov (United States)

    Wang, Zhongliang; Feng, Yan

    2017-12-01

    Hyperspectral data processing typically demands enormous computational resources in terms of storage, computation, and I/O throughputs. In this paper, a compressive sensing framework with low sampling rate is described for hyperspectral imagery. It is based on the widely used linear spectral mixture model. Abundance fractions can be calculated directly from compressively sensed data with no need to reconstruct original hyperspectral imagery. The proposed abundance estimation model is based on the sparsity of abundance fractions and an alternating direction method of multipliers is developed to solve this model. Experiments show that the proposed scheme has a high potential to unmix compressively sensed hyperspectral data with low sampling rate.

  12. Data Reduction and Rapid Analysis of Hyperspectral Data Sets Project

    Data.gov (United States)

    National Aeronautics and Space Administration — Hyperspectral sensors offer great opportunities for increasingly sensitive automated target recognition (ATR) systems though a common problem is the lack of...

  13. Fusion of Imperfect Information in the Unified Framework of Random Sets Theory: Application to Target Identification

    Science.gov (United States)

    2007-11-01

    droit du Canada), telle que représentée par le ministre de la Défense nationale, 2007 Abstract This is a study of the applicability of random sets...tracking and target identification, and non-military applications like medicine and robotics , have been developed using the mathematics of data fusion. The...been used in non-military applications such as robotics or medicine. The purpose of this section is to describe the target identification problem in

  14. Classification of objects on hyperspectral images — further developments

    DEFF Research Database (Denmark)

    Kucheryavskiy, Sergey V.; Williams, Paul

    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......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...

  15. Classification of objects on hyperspectral images — further method development

    DEFF Research Database (Denmark)

    Kucheryavskiy, Sergey V.; Williams, Paul James

    the pixels. This makes classification of the objects inefficient. Recently, several methods, which combine spectral and spatial information, has been also proposed 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......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...

  16. Estimating the number of endmembers in hyperspectral imagery using accumulated convex hull vertex and similarity measure

    Science.gov (United States)

    Wu, Kang-Pei; Teng, Hong-Chao; Wu, Jee-Cheng

    2017-10-01

    The key to successful spectral un-mixing is indicating number of endmembers and their corresponding spectral signatures. Nevertheless, correctly estimate the number of end members without a priori knowledge is a very hard task because pixels in a hyperspectral image are always contain a mixture of the several reflected spectra. Currently, Noise Whitened Harsanyi, Farrand, and Chang (NWHFC) and hyperspectral signal subspace identification by minimum error (HySime) are two well-known methods for estimating the number of endmembers. However, in practice, because NWHFC requires fixing the false-alarm probability and HySime needs estimate noise of each spectral band, these two methods may not only ignore small objects but also can't identify endmembers. In this paper, assuming endmembers in a hyperspectral image can be modeled by convex geometry. We propose a threestage process to estimate the number of endmembers. At the first stage, principal component (PC) is used to transform original image to low-dimensional components for speeding up algorithm execution. At second stage, successive volume maximization (SVMAX) is used to obtain vertex using convex properties. At the third stage, spectral angle mapper (SAM) is used to compute similarity measures among vertex, and minimum SAM value represents vertex separation. Repeat the second and third stages by increasing transformed component dimensions until reach a predefined criteria. The number of endmembers of the image is the vertex with maximum of the vertex separations Finally, the proposed method is applied to synthetic and real AVIRIS and HYDICE hyperspectral data sets for estimating the number of endmembers. The results demonstrate that the proposed method can be used to estimate more reasonable and precise number of endmembers than the two published methods.

  17. 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.

  18. 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

  19. Mesoscale, Radiometrically Referenced, Multi-Temporal Hyperspectral Data for Co2 Leak Detection by Locating Spatial Variation of Biophysically Relevant Parameters

    Science.gov (United States)

    McCann, Cooper Patrick

    Low-cost flight-based hyperspectral imaging systems have the potential to provide valuable information for ecosystem and environmental studies as well as aide in land management and land health monitoring. This thesis describes (1) a bootstrap method of producing mesoscale, radiometrically-referenced hyperspectral data using the Landsat surface reflectance (LaSRC) data product as a reference target, (2) biophysically relevant basis functions to model the reflectance spectra, (3) an unsupervised classification technique based on natural histogram splitting of these biophysically relevant parameters, and (4) local and multi-temporal anomaly detection. The bootstrap method extends standard processing techniques to remove uneven illumination conditions between flight passes, allowing the creation of radiometrically self-consistent data. Through selective spectral and spatial resampling, LaSRC data is used as a radiometric reference target. Advantages of the bootstrap method include the need for minimal site access, no ancillary instrumentation, and automated data processing. Data from a flight on 06/02/2016 is compared with concurrently collected ground based reflectance spectra as a means of validation achieving an average error of 2.74%. Fitting reflectance spectra using basis functions, based on biophysically relevant spectral features, allows both noise and data reductions while shifting information from spectral bands to biophysical features. Histogram splitting is used to determine a clustering based on natural splittings of these fit parameters. The Indian Pines reference data enabled comparisons of the efficacy of this technique to established techniques. The splitting technique is shown to be an improvement over the ISODATA clustering technique with an overall accuracy of 34.3/19.0% before merging and 40.9/39.2% after merging. This improvement is also seen as an improvement of kappa before/after merging of 24.8/30.5 for the histogram splitting technique

  20. Identification of Information Sharing in Supply Chain Management (Case of Woven Bamboo Crafts in Tomohon City)

    OpenAIRE

    Palandeng, Indrie Debbie; Taroreh, Rita N.; Sengka, Ryan Reynaldo

    2015-01-01

    One method of the application of Supply Chain Management is Information Sharing, where the level or quantity of information sharing is the level at which information is important and confidential in the company (proprietary) to be communicated to business partners in the supply chain. This shared information can vary, ranging from strategic information to the tactical information or information about events logistics, to information about markets and consumers. Through the use of available da...

  1. DETECTION OF BUILT-UP AREAS USING POLARIMETRIC SYNTHETIC APERTURE RADAR DATA AND HYPERSPECTRAL IMAGE

    Directory of Open Access Journals (Sweden)

    R. Bordbari

    2015-12-01

    Full Text Available Polarimetric synthetic aperture radar (POLSAR is an advantageous data for information extraction about objects and structures by using the wave scattering and polarization properties. Hyperspectral remote sensing exploits the fact that all materials reflect, absorb, and emit electromagnetic energy, at specific wavelengths, in distinctive patterns related to their molecular composition. As a result of their fine spectral resolution, Hyperspectral image (HIS sensors provide a significant amount of information about the physical and chemical composition of the materials occupying the pixel surface. In target detection applications, the main objective is to search the pixels of an HSI data cube for the presence of a specific material (target. In this research, a hierarchical constrained energy minimization (hCEM method using 5 different adjusting parameters has been used for target detection from hyperspectral data. Furthermore, to detect the built-up areas from POLSAR data, building objects discriminated from surrounding natural media presented on the scene using Freeman polarimetric target decomposition (PTD and the correlation coefficient between co-pol and cross-pol channels. Also, target detection method has been implemented based on the different polarization basis for using the more information. Finally a majority voting method has been used for fusing the target maps. The polarimetric image C-band SAR data acquired by Radarsat-2, over San Francisco Bay area was used for the evaluation of the proposed method.

  2. Detection of Built-Up Areas Using Polarimetric Synthetic Aperture Radar Data and Hyperspectral Image

    Science.gov (United States)

    Bordbari, R.; Maghsoudi, Y.; Salehi, M.

    2015-12-01

    Polarimetric synthetic aperture radar (POLSAR) is an advantageous data for information extraction about objects and structures by using the wave scattering and polarization properties. Hyperspectral remote sensing exploits the fact that all materials reflect, absorb, and emit electromagnetic energy, at specific wavelengths, in distinctive patterns related to their molecular composition. As a result of their fine spectral resolution, Hyperspectral image (HIS) sensors provide a significant amount of information about the physical and chemical composition of the materials occupying the pixel surface. In target detection applications, the main objective is to search the pixels of an HSI data cube for the presence of a specific material (target). In this research, a hierarchical constrained energy minimization (hCEM) method using 5 different adjusting parameters has been used for target detection from hyperspectral data. Furthermore, to detect the built-up areas from POLSAR data, building objects discriminated from surrounding natural media presented on the scene using Freeman polarimetric target decomposition (PTD) and the correlation coefficient between co-pol and cross-pol channels. Also, target detection method has been implemented based on the different polarization basis for using the more information. Finally a majority voting method has been used for fusing the target maps. The polarimetric image C-band SAR data acquired by Radarsat-2, over San Francisco Bay area was used for the evaluation of the proposed method.

  3. 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.

  4. Hyperspectral all-sky imaging of auroras.

    Science.gov (United States)

    Sigernes, Fred; Ivanov, Yuriy; Chernouss, Sergey; Trondsen, Trond; Roldugin, Alexey; Fedorenko, Yury; Kozelov, Boris; Kirillov, Andrey; Kornilov, Ilia; Safargaleev, Vladimir; Holmen, Silje; Dyrland, Margit; Lorentzen, Dag; Baddeley, Lisa

    2012-12-03

    A prototype auroral hyperspectral all-sky camera has been constructed and tested. It uses electro-optical tunable filters to image the night sky as a function of wavelength throughout the visible spectrum with no moving mechanical parts. The core optical system includes a new high power all-sky lens with F-number equal to f/1.1. The camera has been tested at the Kjell Henriksen Observatory (KHO) during the auroral season of 2011/2012. It detects all sub classes of aurora above ~½ of the sub visual 1kR green intensity threshold at an exposure time of only one second. Supervised classification of the hyperspectral data shows promise as a new method to process and identify auroral forms.

  5. Hyperspectral digital imagery collection experiment (HYDICE)

    Science.gov (United States)

    Mitchell, Peter A.

    1995-11-01

    In order to advance the state-of-the-art in the collection of imaging spectroscopy, the U.S. Navy Space and Warfare Systems Command sponsored the development and fabrication of a new generation, well calibrated hyperspectral imaging spectrometer. Called the Hyperspectral Digital Imagery Collection Experiment (HYDICE), the sensor was built by Hughes Danbury Optical Systems, Danbury, Conn., delivered for integration into the Environmental Institute of Michigan's (ERIM) CV-580 aircraft in December 1994, tested and characterized between January and June 1995, and has since been involved in several airborne data collection experiments. In this paper, the HYDICE Program Office organization, sensor specifications, airborne characterization results, and a summary of the results of the most recent data exploitation and analyses are presented.

  6. Piecewise flat embeddings for hyperspectral image analysis

    Science.gov (United States)

    Hayes, Tyler L.; Meinhold, Renee T.; Hamilton, John F.; Cahill, Nathan D.

    2017-05-01

    Graph-based dimensionality reduction techniques such as Laplacian Eigenmaps (LE), Local Linear Embedding (LLE), Isometric Feature Mapping (ISOMAP), and Kernel Principal Components Analysis (KPCA) have been used in a variety of hyperspectral image analysis applications for generating smooth data embeddings. Recently, Piecewise Flat Embeddings (PFE) were introduced in the computer vision community as a technique for generating piecewise constant embeddings that make data clustering / image segmentation a straightforward process. In this paper, we show how PFE arises by modifying LE, yielding a constrained ℓ1-minimization problem that can be solved iteratively. Using publicly available data, we carry out experiments to illustrate the implications of applying PFE to pixel-based hyperspectral image clustering and classification.

  7. 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.

  8. Automatic Target Recognition for Hyperspectral Imagery

    Science.gov (United States)

    2012-03-01

    two modified ATRs to study the effects of including steps three and four. Also explored is the impact on the ATR with two different anomaly detection...geological exploration , and surveillance (Stein, Beaven, Hoff, Winter, Schaum, & Stocker, 2002). With decreases in manning levels and the ever increasing...Bourennane, S. (2005). Whitening Spacial Correlation Filtering for Hyperspectral Anomaly Detection. IEEE International Conference on Acoustics, Speech

  9. Object-based habitat mapping using very high spatial resolution multispectral and hyperspectral imagery with LiDAR data

    Science.gov (United States)

    Onojeghuo, Alex Okiemute; Onojeghuo, Ajoke Ruth

    2017-07-01

    This study investigated the combined use of multispectral/hyperspectral imagery and LiDAR data for habitat mapping across parts of south Cumbria, North West England. The methodology adopted in this study integrated spectral information contained in pansharp QuickBird multispectral/AISA Eagle hyperspectral imagery and LiDAR-derived measures with object-based machine learning classifiers and ensemble analysis techniques. Using the LiDAR point cloud data, elevation models (such as the Digital Surface Model and Digital Terrain Model raster) and intensity features were extracted directly. The LiDAR-derived measures exploited in this study included Canopy Height Model, intensity and topographic information (i.e. mean, maximum and standard deviation). These three LiDAR measures were combined with spectral information contained in the pansharp QuickBird and Eagle MNF transformed imagery for image classification experiments. A fusion of pansharp QuickBird multispectral and Eagle MNF hyperspectral imagery with all LiDAR-derived measures generated the best classification accuracies, 89.8 and 92.6% respectively. These results were generated with the Support Vector Machine and Random Forest machine learning algorithms respectively. The ensemble analysis of all three learning machine classifiers for the pansharp QuickBird and Eagle MNF fused data outputs did not significantly increase the overall classification accuracy. Results of the study demonstrate the potential of combining either very high spatial resolution multispectral or hyperspectral imagery with LiDAR data for habitat mapping.

  10. Hyperspectral Anomaly Detection by Graph Pixel Selection.

    Science.gov (United States)

    Yuan, Yuan; Ma, Dandan; Wang, Qi

    2016-12-01

    Hyperspectral anomaly detection (AD) is an important problem in remote sensing field. It can make full use of the spectral differences to discover certain potential interesting regions without any target priors. Traditional Mahalanobis-distance-based anomaly detectors assume the background spectrum distribution conforms to a Gaussian distribution. However, this and other similar distributions may not be satisfied for the real hyperspectral images. Moreover, the background statistics are susceptible to contamination of anomaly targets which will lead to a high false-positive rate. To address these intrinsic problems, this paper proposes a novel AD method based on the graph theory. We first construct a vertex- and edge-weighted graph and then utilize a pixel selection process to locate the anomaly targets. Two contributions are claimed in this paper: 1) no background distributions are required which makes the method more adaptive and 2) both the vertex and edge weights are considered which enables a more accurate detection performance and better robustness to noise. Intensive experiments on the simulated and real hyperspectral images demonstrate that the proposed method outperforms other benchmark competitors. In addition, the robustness of the proposed method has been validated by using various window sizes. This experimental result also demonstrates the valuable characteristic of less computational complexity and less parameter tuning for real applications.

  11. 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.

  12. DISTRIBUTED UNMIXING OF HYPERSPECTRAL DATAWITH SPARSITY CONSTRAINT

    Directory of Open Access Journals (Sweden)

    S. Khoshsokhan

    2017-09-01

    Full Text Available 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.

  13. Informed consent in implantable BCI research: identification of research risks and recommendations for development of best practices

    Science.gov (United States)

    Klein, Eran; Ojemann, Jeffrey

    2016-08-01

    Objective. Implantable brain-computer interface (BCI) research promises improvements in human health and enhancements in quality of life. Informed consent of subjects is a central tenet of this research. Rapid advances in neuroscience, and the intimate connection between functioning of the brain and conceptions of the self, make informed consent particularly challenging in BCI research. Identification of safety and research-related risks associated with BCI devices is an important step in ensuring meaningful informed consent. Approach. This paper highlights a number of BCI research risks, including safety concerns, cognitive and communicative impairments, inappropriate subject expectations, group vulnerabilities, privacy and security, and disruptions of identity. Main results. Based on identified BCI research risks, best practices are needed for understanding and incorporating BCI-related risks into informed consent protocols. Significance. Development of best practices should be guided by processes that are: multidisciplinary, systematic and transparent, iterative, relational and exploratory.

  14. Performance portability study of an automatic target detection and classification algorithm for hyperspectral image analysis using OpenCL

    Science.gov (United States)

    Bernabe, Sergio; Igual, Francisco D.; Botella, Guillermo; Garcia, Carlos; Prieto-Matias, Manuel; Plaza, Antonio

    2015-10-01

    Recent advances in heterogeneous high performance computing (HPC) have opened new avenues for demanding remote sensing applications. Perhaps one of the most popular algorithm in target detection and identification is the automatic target detection and classification algorithm (ATDCA) widely used in the hyperspectral image analysis community. Previous research has already investigated the mapping of ATDCA on graphics processing units (GPUs) and field programmable gate arrays (FPGAs), showing impressive speedup factors that allow its exploitation in time-critical scenarios. Based on these studies, our work explores the performance portability of a tuned OpenCL implementation across a range of processing devices including multicore processors, GPUs and other accelerators. This approach differs from previous papers, which focused on achieving the optimal performance on each platform. Here, we are more interested in the following issues: (1) evaluating if a single code written in OpenCL allows us to achieve acceptable performance across all of them, and (2) assessing the gap between our portable OpenCL code and those hand-tuned versions previously investigated. Our study includes the analysis of different tuning techniques that expose data parallelism as well as enable an efficient exploitation of the complex memory hierarchies found in these new heterogeneous devices. Experiments have been conducted using hyperspectral data sets collected by NASA's Airborne Visible Infra- red Imaging Spectrometer (AVIRIS) and the Hyperspectral Digital Imagery Collection Experiment (HYDICE) sensors. To the best of our knowledge, this kind of analysis has not been previously conducted in the hyperspectral imaging processing literature, and in our opinion it is very important in order to really calibrate the possibility of using heterogeneous platforms for efficient hyperspectral imaging processing in real remote sensing missions.

  15. Multi-scale hyperspectral imaging of cervical neoplasia.

    Science.gov (United States)

    Wang, Chaojian; Zheng, Wenli; Bu, Yanggao; Chang, Shufang; Zhang, Shiwu; Xu, Ronald X

    2016-06-01

    This preliminary study aimed at investigating the feasibility and effective of multi-scale hyperspectral imaging in detecting cervical neoplasia at both tissue and cellular levels. In this paper, we describe a noninvasive diagnosis method with a hyperspectral imager for detection and location of cervical intraepithelial neoplasia (CIN) at multiple scales. At the macroscopic level, the hyperspectral imager was applied to capture the reflectance images of the entire cervix in vivo at a series of wavelengths. At the microscopic level, the hyperspectral imager was coupled with a microscope to collect the transmittance images of the pathological slide. The collected image data were calibrated. A wide-gap second derivative analysis was applied to differentiate CIN from other types of tissue. At both macroscopic and microscopic levels, hyperspectral imaging analysis results were consistent with those of histopathological analysis, indicating the technical feasibility of multi-scale hyperspectral imaging for cervical neoplasia detection with accuracy and efficacy. We propose a multi-scale hyperspectral imaging method for noninvasive detection of cervical neoplasia. Comparison of the imaging results with those of gold standard histologic measurements demonstrates that the hyperspectral diagnostic imaging system can distinguish CIN at both tissue and cellular levels.

  16. 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...

  17. 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

    2015-02-01

    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.

  18. Session 2 – Identification of endocrine disruptors and amendments of standard information requirements

    DEFF Research Database (Denmark)

    Christiansen, Sofie; Holbech, Henrik

    2014-01-01

    Discussions regarding regulation of endocrine disruptors (EDs) and combination effects are ongoing in Europe. Among the central topics of discussion are establishment of criteria for identification of EDs, whether there is a threshold for endocrine disrupting effects and how EDs should be handled...

  19. 77 FR 75702 - Supplemental Identification Information for 1 Individual Designated Pursuant to Executive Order...

    Science.gov (United States)

    2012-12-21

    ... title 3, United States Code. In the Order, the President declared a national emergency to address the... Lebanon or Its Democratic Processes and Institutions.'' DATES: The publishing of updated identification... issued Executive Order 13441 (the ``Order'') pursuant to the International Emergency Economic Powers Act...

  20. 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.

  1. 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.

  2. Airborne Hyperspectral Survey of Afghanistan 2007: Flight Line Planning and HyMap Data Collection

    Science.gov (United States)

    Kokaly, Raymond F.; King, Trude V.V.; Livo, K. Eric

    2008-01-01

    Hyperspectral remote sensing data were acquired over Afghanistan with the HyMap imaging spectrometer (Cocks and others, 1998) operating on the WB-57 high altitude NASA research aircraft (http://jsc-aircraft-ops.jsc.nasa.gov/wb57/index.html). These data were acquired during the interval of August 22, 2007 to October 2, 2007, as part of the United States Geological Survey (USGS) project 'Oil and Gas Resources Assessment of the Katawaz and Helmand Basins'. A total of 218 flight lines of hyperspectral remote sensing data were collected over the country. This report describes the planning of the airborne survey and the flight lines that were flown. Included with this report are digital files of the nadir tracks of the flight lines, including a map of the labeled flight lines and corresponding vector shape files for geographic information systems (GIS).

  3. Airborne Demonstration of FPGA Implementation of Fast Lossless Hyperspectral Data Compression System

    Science.gov (United States)

    Keymeulen, D.; Aranki, N.; Bakhshi, A.; Luong, H.; Sartures, C.; Dolman, D.

    2014-01-01

    Efficient 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.

  4. Target detection in hyperspectral images using projection pursuit with interference rejection

    Science.gov (United States)

    Ifarraguerri, Agustin I.; Ren, Hsuan; Chang, Chein-I.

    1998-12-01

    We present a method for the automatic, unsupervised detection of spectrally distinct targets from the background using hyperspectral imaging. The approach is based on the concepts of projection pursuit (PP) and unsupervised orthogonal subspace projection (UOSP). It has the advantage of not requiring any prior knowledge of the scene or the objects' spectral signatures. All information is obtained from the data. First, PP is used to both reduce the data dimensionality and locate potential targets. Then, UOSP suppresses the signatures from undesired objects or interferers that cause false detections when a spectral filter is applied. The result is a set of gray scale images where objects belonging to the same spectral class are enhanced while the background and other undesired objects are suppressed. This method is demonstrated using data from the Hyperspectral Digital Imagery Collection Experiment (HYDICE).

  5. 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.

  6. 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

  7. Rapid identification information and its influence on the perceived clues at a crime scene: An experimental study.

    Science.gov (United States)

    de Gruijter, Madeleine; Nee, Claire; de Poot, Christianne J

    2017-11-01

    Crime scenes can always be explained in multiple ways. Traces alone do not provide enough information to infer a whole series of events that has taken place; they only provide clues for these inferences. CSIs need additional information to be able to interpret observed traces. In the near future, a new source of information that could help to interpret a crime scene and testing hypotheses will become available with the advent of rapid identification techniques. A previous study with CSIs demonstrated that this information had an influence on the interpretation of the crime scene, yet it is still unknown what exact information was used for this interpretation and for the construction of their scenario. The present study builds on this study and gains more insight into (1) the exact investigative and forensic information that was used by CSIs to construct their scenario, (2) the inferences drawn from this information, and (3) the kind of evidence that was selected at the crime scene to (dis)prove this scenario. We asked 48 CSIs to investigate a potential murder crime scene on the computer and explicate what information they used to construct a scenario and to select traces for analysis. The results show that the introduction of rapid ID information at the start of an investigation contributes to the recognition of different clues at the crime scene, but also to different interpretations of identical information, depending on the kind of information available and the scenario one has in mind. Furthermore, not all relevant traces were recognized, showing that important information can be missed during the investigation. In this study, accurate crime scenarios where mainly build with forensic information, but we should be aware of the fact that crime scenes are always contaminated with unrelated traces and thus be cautious of the power of rapid ID at the crime scene. Copyright © 2017 The Chartered Society of Forensic Sciences. Published by Elsevier B.V. All rights

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

    2016-10-01

    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.

  13. Region-based geometric active contour for classification using hyperspectral remote sensing images

    Science.gov (United States)

    Yan, Lin

    2011-12-01

    The high spectral resolution of hyperspectral imaging (HSI) systems greatly enhances the capabilities of discrimination, identification and quantification of objects of different materials from remote sensing images, but they also bring challenges to the processing and analysis of HSI data. One issue is the high computation cost and the curse of dimensionality associated with the high dimensions of HSI data. A second issue is how to effectively utilize the information including spectral and spatial information embedded in HSI data. Geometric Active Contour (GAC) is a widely used image segmentation method that utilizes the geometric information of objects within images. One category of GAC models, the region-based GAC models (RGAC), have good potential for remote sensing image processing because they use both spectral and geometry information in images are robust to initial contour placement. These models have been introduced to target extractions and classifications on remote sensing images. However, there are some restrictions on the applications of the RGAC models on remote sensing. First, the heavy involvement of iterative contour evolutions makes GAC applications time-consuming and inconvenient to use. Second, the current RGAC models must be based on a certain distance metric and the performance of RGAC classifiers are restricted by the performance of the employed distance metrics. According to the key features of the RGAC models analyzed in this dissertation, a classification framework is developed for remote sensing image classifications using the RGAC models. This framework allows the RGAC models to be combined with conventional pixel-based classifiers to promote them to spectral-spatial classifiers and also greatly reduces the iterations of contour evolutions. An extended Chan-Vese (ECV) model is proposed that is able to incorporate the widely used distance metrics in remote sensing image processing. A new type of RGAC model, the edge-oriented RGAC model

  14. Hyperspectral Technologies for Assessing Seed Germination and Trifloxysulfuron-methyl Response in Amaranthus palmeri (Palmer Amaranth).

    Science.gov (United States)

    Matzrafi, Maor; Herrmann, Ittai; Nansen, Christian; Kliper, Tom; Zait, Yotam; Ignat, Timea; Siso, Dana; Rubin, Baruch; Karnieli, Arnon; Eizenberg, Hanan

    2017-01-01

    /resistance) was also demonstrated. We demonstrated that hyperspectral reflectance analyses can provide reliable information about seed germination and levels of susceptibility in A. palmeri. The use of reflectance-based analyses can help to better understand the invasiveness of A. palmeri, and thus facilitate the development of targeted control methods. It also has enormous potential for impacting environmental management in that it can be used to prevent ineffective herbicide applications. It also has potential for use in mapping tempo-spatial population dynamics in agro-ecological landscapes.

  15. Hyperspectral fluorescence microscopy detects autofluorescent factors that can be exploited as a diagnostic method for Candida species differentiation

    Science.gov (United States)

    Graus, Matthew S.; Neumann, Aaron K.; Timlin, Jerilyn A.

    2017-01-01

    Fungi in the Candida genus are the most common fungal pathogens. They not only cause high morbidity and mortality but can also cost billions of dollars in healthcare. To alleviate this burden, early and accurate identification of Candida species is necessary. However, standard identification procedures can take days and have a large false negative error. The method described in this study takes advantage of hyperspectral confocal fluorescence microscopy, which enables the capability to quickly and accurately identify and characterize the unique autofluorescence spectra from different Candida species with up to 84% accuracy when grown in conditions that closely mimic physiological conditions.

  16. 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

  17. 78 FR 68817 - Proposed Information Collection; Comment Request; Northeast Region Gear Identification

    Science.gov (United States)

    2013-11-15

    ... additional information or copies of the information collection instrument and instructions should be directed... gear conflicts. II. Method of Collection No information is submitted to the NMFS as a result of this... National Oceanic and Atmospheric Administration Proposed Information Collection; Comment Request; Northeast...

  18. Mapping mine tailings using airborne geophysical and hyperspectral remote sensing data

    Science.gov (United States)

    Shang, Jiali

    Mine tailings are the waste products from mining operations. Most mine tailings contain a considerable amount of reactive sulphides which can cause acid mine drainage (AMD) when exposed to air and water. AMD constitutes a threat both to the environment and to public health. Increased awareness of AMD has led to growing activities in mine-tailing monitoring and reclamation worldwide. Mining companies in Canada are required to provide information to provincial governments about their waste disposal and control activities. There is an urgent need to develop new automated ways to provide information on short- to long-term evolution of tailings, thus enabling the mining companies to monitor their tailings more effectively. The overall goal of the thesis is to explore the potential of hyperspectral remote sensing and geophysical techniques for mapping variations within and immediately outside of the tailings. Data used for this study are from three sources: airborne geophysical data, hyperspectral casi and Probe-1 data, and field data. This study has contributed to both the remote sensing data analysis techniques and the understanding of mine-tailing surface and subsurface processes. Specifically, this study has the following important findings: (1) Airborne magnetic and electromagnetic data can provide information regarding the subsurface distribution of mine tailings on the basis of sulphide mineral content. A procedure has been developed in this study to use these data sources for rapidly surveying large tailings areas. This procedure can minimize expenditures for mining companies when designing remedial plans for the closure of the mines. This study has also identified regions of enhanced conductivity that extend beyond the tailing containment area. This information indicates seepage pathways, and is important for monitoring the effectiveness of tailing containment structures. (2) High-spatial-resolution hyperspectral casi (Compact Airborne Spectrographic Imagery

  19. Using Visible Spectral Information to Predict Long-Wave Infrared Spectral Emissivity: A Case Study over the Sokolov Area of the Czech Republic with an Airborne Hyperspectral Scanner Sensor

    OpenAIRE

    Gila Notesco; Eyal Ben-Dor; Simon Adar; Yoel Shkolnisky

    2013-01-01

    Remote-sensing platforms are often comprised of a cluster of different spectral range detectors or sensors to benefit from the spectral identification capabilities of each range. Missing data from these platforms, caused by problematic weather conditions, such as clouds, sensor failure, low temporal coverage or a narrow field of view (FOV), is one of the problems preventing proper monitoring of the Earth. One of the possible solutions is predicting a detector or sensor’s missing data using an...

  20. 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.

  1. EXhype: A tool for mineral classification using hyperspectral data

    Science.gov (United States)

    Adep, Ramesh Nityanand; shetty, Amba; Ramesh, H.

    2017-02-01

    Various supervised classification algorithms have been developed to classify earth surface features using hyperspectral data. Each algorithm is modelled based on different human expertises. However, the performance of conventional algorithms is not satisfactory to map especially the minerals in view of their typical spectral responses. This study introduces a new expert system named 'EXhype (Expert system for hyperspectral data classification)' to map minerals. The system incorporates human expertise at several stages of it's implementation: (i) to deal with intra-class variation; (ii) to identify absorption features; (iii) to discriminate spectra by considering absorption features, non-absorption features and by full spectra comparison; and (iv) finally takes a decision based on learning and by emphasizing most important features. It is developed using a knowledge base consisting of an Optimal Spectral Library, Segmented Upper Hull method, Spectral Angle Mapper (SAM) and Artificial Neural Network. The performance of the EXhype is compared with a traditional, most commonly used SAM algorithm using Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data acquired over Cuprite, Nevada, USA. A virtual verification method is used to collect samples information for accuracy assessment. Further, a modified accuracy assessment method is used to get a real users accuracies in cases where only limited or desired classes are considered for classification. With the modified accuracy assessment method, SAM and EXhype yields an overall accuracy of 60.35% and 90.75% and the kappa coefficient of 0.51 and 0.89 respectively. It was also found that the virtual verification method allows to use most desired stratified random sampling method and eliminates all the difficulties associated with it. The experimental results show that EXhype is not only producing better accuracy compared to traditional SAM but, can also rightly classify the minerals. It is proficient in avoiding

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

    Energy Technology Data Exchange (ETDEWEB)

    Chaudhari, Abhijit J [Signal and Image Processing Institute, Department of Electrical Engineering-Systems, University of Southern California, Los Angeles, CA 90089 (United States); Darvas, Felix [Signal and Image Processing Institute, Department of Electrical Engineering-Systems, University of Southern California, Los Angeles, CA 90089 (United States); Bading, James R [Department of Radiology, University of Southern California, Los Angeles, CA 90033 (United States); Moats, Rex A [Department of Radiology, University of Southern California, Los Angeles, CA 90033 (United States); Conti, Peter S [Department of Radiology, University of Southern California, Los Angeles, CA 90033 (United States); Smith, Desmond J [Department of Molecular and Medical Pharmacology, UCLA School of Medicine, Los Angeles, CA 90095 (United States); Cherry, Simon R [Department of Biomedical Engineering, University of California-Davis, Davis, CA 95616 (United States); Leahy, Richard M [Signal and Image Processing Institute, Department of Electrical Engineering-Systems, University of Southern California, Los Angeles, CA 90089 (United States)

    2005-12-07

    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.

  3. Fuzzy Chance-constrained Programming Based Security Information Optimization for Low Probability of Identification Enhancement in Radar Network Systems

    Directory of Open Access Journals (Sweden)

    C. G. Shi

    2015-04-01

    Full Text Available In this paper, the problem of low probability of identification (LPID improvement for radar network systems is investigated. Firstly, the security information is derived to evaluate the LPID performance for radar network. Then, without any prior knowledge of hostile intercept receiver, a novel fuzzy chance-constrained programming (FCCP based security information optimization scheme is presented to achieve enhanced LPID performance in radar network systems, which focuses on minimizing the achievable mutual information (MI at interceptor, while the attainable MI outage probability at radar network is enforced to be greater than a specified confidence level. Regarding to the complexity and uncertainty of electromagnetic environment in the modern battlefield, the trapezoidal fuzzy number is used to describe the threshold of achievable MI at radar network based on the credibility theory. Finally, the FCCP model is transformed to a crisp equivalent form with the property of trapezoidal fuzzy number. Numerical simulation results demonstrating the performance of the proposed strategy are provided.

  4. Online Study of Melanoma Identification: The Roles of ABC Information and Photographic Examples in Lesion Discrimination

    OpenAIRE

    Cornell, Ella

    2013-01-01

    Public education campaigns designed to increase awareness about malignant melanoma, the most fatal form of skin cancer, currently use written criteria (ABCD) to describe its common features, but an increasing body of evidence has suggested that the public would benefit more from the use of photographic examples of lesions. This study explored possible public education techniques to optimize laypeople’s recognition of melanoma through an online melanoma identification task. We were particularl...

  5. Tag Anti-collision Algorithm for RFID Systems with Minimum Overhead Information in the Identification Process

    Directory of Open Access Journals (Sweden)

    Usama S. Mohammed

    2011-04-01

    Full Text Available This paper describes a new tree based anti-collision algorithm for Radio Frequency Identification (RFID systems. The proposed technique is based on fast parallel binary splitting (FPBS technique. It follows a new identification path through the binary tree. The main advantage of the proposed protocol is the simple dialog between the reader and tags. It needs only one bit tag response followed by one bit reader reply (one-to-one bit dialog. The one bit reader response represents the collision report (0: collision; 1: no collision of the tags' one bit message. The tag achieves self transmission control by dynamically updating its relative replying order due to the received collision report. The proposed algorithm minimizes the overhead transmitted bits per one tag identification. In the collision state, tags do modify their next replying order in the next bit level. Performed computer simulations have shown that the collision recovery scheme is very fast and simple even with the successive reading process. Moreover, the proposed algorithm outperforms most of the recent techniques in most cases.

  6. Hyperspectral Based Skin Detection for Person of Interest Identification

    Science.gov (United States)

    2015-03-01

    and geology [19]. However, over the last two decades, HSI has been adopted into other fields such as ecology and surveillance. The increased use and...2] [4 4 2] [4 4 2] A ct iv at io n Fu nc tio n radbas 88% 84% 91.5% 83.5% tribas 97.5% 84% 88% 75% Table 4.9: Top two activation functions as...calculated Equal Weighted Accuracy generalized results. Normalization Method Max Samples Unary Samples [4 1 2] [4 1 2] A ct iv at io n Fu nc tio n

  7. OPTIMAL BAND SELECTION OF HYPERSPECTRAL DATA FOR TRANSGENIC CORN IDENTIFICATION

    Science.gov (United States)

    Resistance development by insect pests to the insecticidal proteins expressed in transgenic crops would increase reliance on broad spectrum chemical insecticides subsequently reducing environmental quality and increasing worker exposure to toxic chemicals. An important component ...

  8. Mesosiderites on Vesta: A Hyperspectral VIS-NIR Investigation

    Science.gov (United States)

    Palomba, E.; Longobardo, A.; DeSanctis, M. C.; Mittlefehldt, D. W.; Ammannito, E.; Capaccioni, F.; Capria, M. T.; Frigeri, A.; Tosi, F.; Zambon, F.; hide

    2013-01-01

    The discussion about the mesosiderite origin is an open issue since several years. Mesosiderites are mixtures of silicate mineral fragments or clasts, embedded in a FeNi metal matrix. Silicates are very similar in mineralogy and texture to howardites [1]. This led some scientists to conclude that mesosiderites could come from the same parent parent asteroid of the howardite, eucrite and diogenite (HED) meteorites [2, 3]. Other studies found a number of differences between HEDs and mesosiderite silicates that could be explained only by separate parent asteroids [4]. Recently, high precision oxygen isotope measurements of m esosiderites silicate fraction were found to be isotopically identical to the HEDs, requiring common parent body, i.e. 4 Vesta [5]. Another important element in favor of a common origin was given by the identification of a centimeter-sized mesosiderite clast in a howardite (Dar al Gani 779): a metal-rich inclusion with fragments of olivine, anorthite, and orthopyroxene plus minor amounts of chromite, tridymite, and troilite [6]. The Dawn mission with its instruments, the Infrared Mapping Spectrometer (VIR) [7], the Framing Camera [8] and the Gamma-Ray and Neutron Detector (GRaND) [9] confirmed that Vesta has a composition fully compatible with HED meteorites [10]. We investigate here the possibility to discern mesosiderite rich locations on the surface of Vesta by means of hyperspectral IR images.

  9. Near infrared hyperspectral imaging for forensic analysis of document forgery.

    Science.gov (United States)

    Silva, Carolina S; Pimentel, Maria Fernanda; Honorato, Ricardo S; Pasquini, Celio; Prats-Montalbán, José M; Ferrer, Alberto

    2014-10-21

    Hyperspectral images in the near infrared range (HSI-NIR) were evaluated as a nondestructive method to detect fraud in documents. Three different types of typical forgeries were simulated by (a) obliterating text, (b) adding text and (c) approaching the crossing lines problem. The simulated samples were imaged in the range of 928-2524 nm with spectral and spatial resolutions of 6.3 nm and 10 μm, respectively. After data pre-processing, different chemometric techniques were evaluated for each type of forgery. Principal component analysis (PCA) was performed to elucidate the first two types of adulteration, (a) and (b). Moreover, Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) was used in an attempt to improve the results of the type (a) obliteration and type (b) adding text problems. Finally, MCR-ALS and Partial Least Squares-Discriminant Analysis (PLS-DA), employed as a variable selection tool, were used to study the type (c) forgeries, i.e. crossing lines problem. Type (a) forgeries (obliterating text) were successfully identified in 43% of the samples using both the chemometric methods (PCA and MCR-ALS). Type (b) forgeries (adding text) were successfully identified in 82% of the samples using both the methods (PCA and MCR-ALS). Finally, type (c) forgeries (crossing lines) were successfully identified in 85% of the samples. The results demonstrate the potential of HSI-NIR associated with chemometric tools to support document forgery identification.

  10. Identification of new and emerging occupational risks using a text mining based information system

    NARCIS (Netherlands)

    Pronk, A.; Goede, H.; Lucas Luijckx, N.; Brug, F. van de; Cnossen, H.; Tielemans, E.

    2011-01-01

    Introduction On the internet and in scientific databases relevant information is available on new and emerging occupational risks. However, the amount of information is enormous and the information is scattered over multiple and diverse data sources complicating the full utilization of the data

  11. 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.

  12. Mineral Classification of Makhtesh Ramon in Israel Using Hyperspectral Longwave Infrared (LWIR Remote-Sensing Data

    Directory of Open Access Journals (Sweden)

    Gila Notesco

    2015-09-01

    Full Text Available Hyperspectral remote-sensing techniques offer an efficient procedure for mineral mapping, with a unique hyperspectral remote-sensing fingerprint in the longwave infrared spectral region enabling identification of the most abundant minerals in the continental crust—quartz and feldspars. This ability was examined by acquiring airborne data with the AisaOWL sensor over the Makhtesh Ramon area in Israel. The at-sensor radiance measured from each pixel in a longwave infrared image represents the emissivity, expressing chemical and physical properties such as surface mineralogy, and the atmospheric contribution which is expressed differently during the day and at night. Therefore, identifying similar features in day and night radiance enabled identifying the major minerals in the surface—quartz, silicates (feldspars and clay minerals, gypsum and carbonates—and mapping their spatial distribution. Mineral identification was improved by applying the radiance of an in situ surface that is featureless for minerals but distinctive for the atmospheric contribution as a gain spectrum to each pixel in the image, reducing the atmospheric contribution and emphasizing the mineralogical features. The results were in agreement with the mineralogy of selected rock samples collected from the study area as derived from laboratory X-ray diffraction analysis. The resulting mineral map of the major minerals in the surface was in agreement with the geological map of the area.

  13. 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.

  14. 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.

  15. 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.

  16. Improved hyperspectral vegetation detection using neural networks with spectral angle mapper

    Science.gov (United States)

    Özdemir, Okan Bilge; Yardımcı ćetin, Yasemin

    2017-05-01

    Hyperspectral images have been used in many areas including city planning, mining and military decision support systems. Hyperspectral image analysis techniques have a great potential for vegetation detection and classification with their capability to identify the spectral differences across the electromagnetic spectrum due to their ability to provide information about the chemical compositions of materials. This study introduces a vegetation detection method employing Artificial Neural Network (ANN) over hyperspectral imaging. The algorithm employed backpropagation MLP algorithm for training neural networks. The performance of ANN is improved by the joint use with Spectral Angle Mapper(SAM). The algorithm first obtains the certainty measure from ANN, following the completion of this process, every pixels' angular distance is computed by SAM. The certainty measure is divided by angular distance. Results from ANN, SAM and Support Vector Machine (SVM) algorithms are compared and evaluated with the result of the algorithm. Limited number of training samples are used for training. The results demonstrate that joint use of ANN and SAM significantly improves classification accuracy for smaller training samples.

  17. Framework for hyperspectral image processing and quantification for cancer detection during animal tumor surgery

    Science.gov (United States)

    Lu, Guolan; Wang, Dongsheng; Qin, Xulei; Halig, Luma; Muller, Susan; Zhang, Hongzheng; Chen, Amy; Pogue, Brian W.; Chen, Zhuo Georgia; Fei, Baowei

    2015-12-01

    Hyperspectral imaging (HSI) is an imaging modality that holds strong potential for rapid cancer detection during image-guided surgery. But the data from HSI often needs to be processed appropriately in order to extract the maximum useful information that differentiates cancer from normal tissue. We proposed a framework for hyperspectral image processing and quantification, which includes a set of steps including image preprocessing, glare removal, feature extraction, and ultimately image classification. The framework has been tested on images from mice with head and neck cancer, using spectra from 450- to 900-nm wavelength. The image analysis computed Fourier coefficients, normalized reflectance, mean, and spectral derivatives for improved accuracy. The experimental results demonstrated the feasibility of the hyperspectral image processing and quantification framework for cancer detection during animal tumor surgery, in a challenging setting where sensitivity can be low due to a modest number of features present, but potential for fast image classification can be high. This HSI approach may have potential application in tumor margin assessment during image-guided surgery, where speed of assessment may be the dominant factor.

  18. 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.

  19. Towards a simulation framework to maximize the resolution of biomedical hyperspectral imaging

    Science.gov (United States)

    Sawyer, Travis W.; Bohndiek, Sarah E.

    2017-07-01

    When light is incident upon tissue, imaging contrast can be obtained from a range of interactions including absorption, scattering and fluorescence. Clinical optical imaging systems are typically optimized to report on a single contrast source, for example, using standard RGB cameras to produce white light reflectance images or filter-based approaches to extract fluorescence emissions. Hyperspectral imaging has the potential to over-come the need for specialized instrumentation, by sampling spatial and spectral information simultaneously. In particular, spectrally resolved detector arrays (SRDAs) now monolithically integrate spectral filters with CMOS image sensors to provide a robust, compact and low cost solution to video rate hyperspectral imaging. However, SRDAs suffer from a significant limitation, which is the inherent tradeoff between spatial and spectral resolution. Therefore, the properties of the SRDA including the number of filters, their wavelength and bandwidth, needs be optimized for tissue imaging. To achieve this, we have developed a software framework to optimize spectral band selection, simulating the hyperspectral sample illumination, data acquisition and spectral unmixing processes. Our approach shows early promise for selecting appropriate spectral filters, which allows us to maintain high spatial resolution for imaging.

  20. Influence of water waves on hyperspectral remote sensing of subsurface water features

    Science.gov (United States)

    Bostater, Charles R., Jr.; Bassetti, Luce

    2004-11-01

    Modeled hyperspectral reflectance signatures with water wave influences are simulated using an analytical-based, iterative radiative transport model applicable to shallow or deep waters. Light transport within the water body is simulated using a fast, accurate radiative transfer model that calculates the light distribution in any layered media and incorporates realistic water surfaces which are synthesized using empirically-based spectral models of the water surface to generate water surface wave facets. The model simulated synthetic images are displayed as 24 bit RGB images of the water surface using selected channels from the simulated synthetic hyperspectral image cube. We show selected channels centered at 490, 530 and 676 nm. We also demonstrate the use of the model to show the capability of the sensor and image modeling approach to detect or "recover" known features or targets submerged within or on the shallow water bottom in a tidal inlet area in Indian River Lagoon, Florida. Line targets are simulated in shallow water and indicate the influence of water waves in different water quality conditions. The technique demonstrates a methodology to help to develop remote sensing protocols for shallow water remote sensing as well as to develop information useful for future hyperspectral sensor system developments.

  1. Pairwise-Distance-Analysis-Driven Dimensionality Reduction Model with Double Mappings for Hyperspectral Image Visualization

    Directory of Open Access Journals (Sweden)

    Yi Long

    2015-06-01

    Full Text Available This paper describes a novel strategy for the visualization of hyperspectral imagery based on the analysis of image pixel pairwise distances. The goal of this approach is to generate a final color image with excellent interpretability and high contrast at the cost of distorting a few pairwise distances. Specifically, the principle of equal variance is introduced to divide all hyperspectral bands into three subgroups and to ensure the energy is distributed uniformly between them, as in natural color images. Then, after detecting both normal and outlier pixels, these three subgroups are mapped into three color components of the output visualization using two different mapping (i.e., dimensionality reduction schemes for the two types of pixels. The widely-used multidimensional scaling (MDS is used for normal pixels and a new objective function, taking into account the weighting of pairwise distances, is presented for the outlier pixels. The pairwise distance weighting is designed such that small pairwise distances between the outliers and their respective neighbors are emphasized and large deviations are suppressed. This produces an image with high contrast and good interpretability while retaining the detailed information content. The proposed algorithm is compared with several state-of-the-art visualization techniques and evaluated on the well-known AVIRIS hyperspectral images. The effectiveness of the proposed strategy is substantiated both visually and quantitatively.

  2. Framework for hyperspectral image processing and quantification for cancer detection during animal tumor surgery.

    Science.gov (United States)

    Lu, Guolan; Wang, Dongsheng; Qin, Xulei; Halig, Luma; Muller, Susan; Zhang, Hongzheng; Chen, Amy; Pogue, Brian W; Chen, Zhuo Georgia; Fei, Baowei

    2015-01-01

    Hyperspectral imaging (HSI) is an imaging modality that holds strong potential for rapid cancer detection during image-guided surgery. But the data from HSI often needs to be processed appropriately in order to extract the maximum useful information that differentiates cancer from normal tissue. We proposed a framework for hyperspectral image processing and quantification, which includes a set of steps including image preprocessing, glare removal, feature extraction, and ultimately image classification. The framework has been tested on images from mice with head and neck cancer, using spectra from 450- to 900-nm wavelength. The image analysis computed Fourier coefficients, normalized reflectance, mean, and spectral derivatives for improved accuracy. The experimental results demonstrated the feasibility of the hyperspectral image processing and quantification framework for cancer detection during animal tumor surgery, in a challenging setting where sensitivity can be low due to a modest number of features present, but potential for fast image classification can be high. This HSI approach may have potential application in tumor margin assessment during image-guided surgery, where speed of assessment may be the dominant factor.

  3. A Unified Framework for Dimensionality Reduction and Classification of Hyperspectral Data

    Science.gov (United States)

    Kolluru, P.; Pandey, K.; Padalia, H.

    2014-11-01

    The processing of hyperspectral remote sensing data, for information retrieval, is challenging due to its higher dimensionality. Machine learning based algorithms such as Support Vector Machine (SVM) is preferably applied to perform classification of high dimensionality data. A single-step unified framework is required which could decide the intrinsic dimensionality of data and achieve higher classification accuracy using SVM. This work present development of a SVM-based dimensionality reduction and classification (SVMDRC) framework for hyperspectral data. The proposed unified framework was tested at Los Tollos in Rodalquilar district of Spain, which have predominance of alunite, kaolinite, and illite minerals with sparse vegetation cover. Summer season image was utilized for implementing the proposed method. Modified broken stick rule (MBSR) was used to calculate the intrinsic dimensionality of HyMap data which automatically reduce the number of bands. Comparison of SVMDRC with SVM clearly suggests that SVM alone is inadequate in yielding better classification accuracies for minerals from hyperspectral data rather requires dimensionality reduction. Incorporation of modified broken stick method in SVMDRC framework positively influenced the feature separability and provided better classification accuracy. The mineral distribution map produced for the study area would be useful for refining the areas for mineral exploration.

  4. Hyperspectral imaging of endogenous fluorescent metabolic molecules to identify pain states in central nervous system tissue

    Science.gov (United States)

    Staikopoulos, Vasiliki; Gosnell, Martin E.; Anwer, Ayad G.; Mustafa, Sanam; Hutchinson, Mark R.; Goldys, Ewa M.

    2016-12-01

    Fluorescence-based bio-imaging methods have been extensively used to identify molecular changes occurring in biological samples in various pathological adaptations. Auto-fluorescence generated by endogenous fluorescent molecules within these samples can interfere with signal to background noise making positive antibody based fluorescent staining difficult to resolve. Hyperspectral imaging uses spectral and spatial imaging information for target detection and classification, and can be used to resolve changes in endogenous fluorescent molecules such as flavins, bound and free NADH and retinoids that are involved in cell metabolism. Hyperspectral auto-fluorescence imaging of spinal cord slices was used in this study to detect metabolic differences within pain processing regions of non-pain versus sciatic chronic constriction injury (CCI) animals, an established animal model of peripheral neuropathy. By using an endogenous source of contrast, subtle metabolic variations were detected between tissue samples, making it possible to distinguish between animals from non-injured and injured groups. Tissue maps of native fluorophores, flavins, bound and free NADH and retinoids unveiled subtle metabolic signatures and helped uncover significant tissue regions with compromised mitochondrial function. Taken together, our results demonstrate that hyperspectral imaging provides a new non-invasive method to investigate central changes of peripheral neuropathic injury and other neurodegenerative disease models, and paves the way for novel cellular characterisation in health, disease and during treatment, with proper account of intrinsic cellular heterogeneity.

  5. Hyperspectral infrared nanoimaging of organic samples based on Fourier transform infrared nanospectroscopy

    Science.gov (United States)

    Amenabar, Iban; Poly, Simon; Goikoetxea, Monika; Nuansing, Wiwat; Lasch, Peter; Hillenbrand, Rainer

    2017-01-01

    Infrared nanospectroscopy enables novel possibilities for chemical and structural analysis of nanocomposites, biomaterials or optoelectronic devices. Here we introduce hyperspectral infrared nanoimaging based on Fourier transform infrared nanospectroscopy with a tunable bandwidth-limited laser continuum. We describe the technical implementations and present hyperspectral infrared near-field images of about 5,000 pixel, each one covering the spectral range from 1,000 to 1,900 cm−1. To verify the technique and to demonstrate its application potential, we imaged a three-component polymer blend and a melanin granule in a human hair cross-section, and demonstrate that multivariate data analysis can be applied for extracting spatially resolved chemical information. Particularly, we demonstrate that distribution and chemical interaction between the polymer components can be mapped with a spatial resolution of about 30 nm. We foresee wide application potential of hyperspectral infrared nanoimaging for valuable chemical materials characterization and quality control in various fields ranging from materials sciences to biomedicine. PMID:28198384

  6. 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.

  7. 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

  8. Relationship between hyperspectral indices, agronomic parameters and phenolic composition of Vitis vinifera cv Tempranillo grapes.

    Science.gov (United States)

    García-Estévez, Ignacio; Quijada-Morín, Natalia; Rivas-Gonzalo, Julián C; Martínez-Fernández, José; Sánchez, Nilda; Herrero-Jiménez, Carlos M; Escribano-Bailón, M Teresa

    2017-09-01

    The phenolic composition of grapes is key when making decisions about harvest date and ensuring the quality of grapes. The present study aimed to investigate the relationship between the detailed phenolic composition of grapes and the agronomic parameters and hyperspectral indices, with the latter being measured via field radiometry techniques. Good correlations were found between phenolic composition (both anthocyanin and flavanol composition) and some hyperspectral indices related to vigor, such as the NDVI (normalized difference vegetation index) and the SAVI (soil adjusted vegetation index). The strongest correlations were observed between the phenolic composition of grape skin at harvest time and variables measured from grapes at veraison time, as well as variables determined from grapevines at harvest time. The potential usefulness of these hyperspectral indices calculated from measurements performed directly on grapes or grapevines for estimating the anthocyanin and flavanol composition of grape skins was indicated by the high coefficients of determination (R 2 = 0.7955 and R 2 = 0.8594, respectively) as obtained by means of principal component regression. According to the results of the present study, hyperspectral indices calculated from measurements performed directly on grapes at veraison time or on grapevines at harvest time may be useful for estimating the anthocyanin and flavanol composition of grape skins. This suggests that field radiometry might provide valuable information for estimating the phenolic composition of grapes, which may prove to be very useful when establishing strategies for harvest planning. © 2017 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. © 2017 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

  9. [Simultaneous Detection of External and Internal Quality Parameters of Huping Jujube Fruits using Hyperspectral Imaging Technology].

    Science.gov (United States)

    Xue, Jian-xin; Zhang, Shu-juan; Zhang, Jing-jing

    2015-08-01

    Nondestructive detection of external and internal quality parameters of jujube is crucial for improving jujube's shelf life and industry production. Hyperspectral imaging is an emerging technique that integrates conventional imaging and spectroscopy to acquire both spatial and spectral information from a sample. It takes the advantages of the conventional RGB, near-infrared spectroscopy, and multi-spectral imaging. In this work, hyperspectral imaging technology covered the range of 450~1000 nm has been evaluated for nondestructive determination of "natural defects" (shrink, crack, insect damage and peck injury) and soluble solids content (SSC) in Huping jujube fruit. 400 RGB images were acquired through four different defect (50 for each stage) and normal (200) classes of the Huping jujube samples. After acquiring hyperspectral images of Huping jujube fruits, the spectral data were extracted from region of interests (ROIs). Using Kennard-Stone algorithm, all kinds of samples were randomly divided into training set (280) and test set (120) according to the proportion of 3:1. Seven principal components (PCs) were selected based on principal component analysis (PCA), and seven textural feature variables (contrast, correlation, energy, homogeneity, variance, mean and entropy) were extracted by gray level co-occurrence matrix (GLCM). The least squares support vector machine (LS-SVM) models were built based on the PCs spectral, textural, combined PCs and textural features, respectively. The satisfactory results show the correct discrimination rate of 92.5% for the prediction samples, as well as correlation coefficient (Rp) of 0.944 for the prediction set to calculate SSC content based on PCs and textural features. The study demonstrated that hyperspectral image technique can be a reliable tool to simultaneous detection of external ("natural defects") and internal (SSC) quality parameters of Huping jujube fruits, which provided a theoretical reference for nondestructive

  10. The Potential of Hyperspectral Patterns of Winter Wheat to Detect Changes in Soil Microbial Community Composition.

    Science.gov (United States)

    Carvalho, Sabrina; van der Putten, Wim H; Hol, W H G

    2016-01-01

    Reliable information on soil status and crop health is crucial for detecting and mitigating disasters like pollution or minimizing impact from soil-borne diseases. While infestation with an aggressive soil pathogen can be detected via reflected light spectra, it is unknown to what extent hyperspectral reflectance could be used to detect overall changes in soil biodiversity. We tested the hypotheses that spectra can be used to (1) separate plants growing with microbial communities from different farms; (2) to separate plants growing in different microbial communities due to different land use; and (3) separate plants according to microbial species loss. We measured hyperspectral reflectance patterns of winter wheat plants growing in sterilized soils inoculated with microbial suspensions under controlled conditions. Microbial communities varied due to geographical distance, land use and microbial species loss caused by serial dilution. After 3 months of growth in the presence of microbes from the two different farms plant hyperspectral reflectance patterns differed significantly from each other, while within farms the effects of land use via microbes on plant reflectance spectra were weak. Species loss via dilution on the other hand affected a number of spectral indices for some of the soils. Spectral reflectance can be indicative of differences in microbial communities, with the Renormalized Difference Vegetation Index the most common responding index. Also, a positive correlation was found between the Normalized Difference Vegetation Index and the bacterial species richness, which suggests that plants perform better with higher microbial diversity. There is considerable variation between the soil origins and currently it is not possible yet to make sufficient reliable predictions about the soil microbial community based on the spectral reflectance. We conclude that measuring plant hyperspectral reflectance has potential for detecting changes in microbial

  11. Concept and integration of an on-line quasi-operational airborne hyperspectral remote sensing system

    Science.gov (United States)

    Schilling, Hendrik; Lenz, Andreas; Gross, Wolfgang; Perpeet, Dominik; Wuttke, Sebastian; Middelmann, Wolfgang

    2013-10-01

    Modern mission characteristics require the use of advanced imaging sensors in reconnaissance. In particular, high spatial and high spectral resolution imaging provides promising data for many tasks such as classification and detecting objects of military relevance, such as camouflaged units or improvised explosive devices (IEDs). Especially in asymmetric warfare with highly mobile forces, intelligence, surveillance and reconnaissance (ISR) needs to be available close to real-time. This demands the use of unmanned aerial vehicles (UAVs) in combination with downlink capability. The system described in this contribution is integrated in a wing pod for ease of installation and calibration. It is designed for the real-time acquisition and analysis of hyperspectral data. The main component is a Specim AISA Eagle II hyperspectral sensor, covering the visible and near-infrared (VNIR) spectral range with a spectral resolution up to 1.2 nm and 1024 pixel across track, leading to a ground sampling distance below 1 m at typical altitudes. The push broom characteristic of the hyperspectral sensor demands an inertial navigation system (INS) for rectification and georeferencing of the image data. Additional sensors are a high resolution RGB (HR-RGB) frame camera and a thermal imaging camera. For on-line application, the data is preselected, compressed and transmitted to the ground control station (GCS) by an existing system in a second wing pod. The final result after data processing in the GCS is a hyperspectral orthorectified GeoTIFF, which is filed in the ERDAS APOLLO geographical information system. APOLLO allows remote access to the data and offers web-based analysis tools. The system is quasi-operational and was successfully tested in May 2013 in Bremerhaven, Germany.

  12. Research on hyperspectral dynamic scene and image sequence simulation

    Science.gov (United States)

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

    2016-10-01

    This paper presents a simulation method of hyper-spectral dynamic scene and image sequence for hyper-spectral equipment evaluation and target detection algorithm. Because of high spectral resolution, strong band continuity, anti-interference and other advantages, in recent years, hyper-spectral 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 hyper-spectral 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 hyper-spectral 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, hyper-spectral 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 hyper-spectral images are consistent with the theoretical analysis results.

  13. Peculiarities of use of ECOC and AdaBoost based classifiers for thematic processing of hyperspectral data

    Science.gov (United States)

    Dementev, A. O.; Dmitriev, E. V.; Kozoderov, V. V.; Egorov, V. D.

    2017-10-01

    Hyperspectral imaging is up-to-date promising technology widely applied for the accurate thematic mapping. The presence of a large number of narrow survey channels allows us to use subtle differences in spectral characteristics of objects and to make a more detailed classification than in the case of using standard multispectral data. The difficulties encountered in the processing of hyperspectral images are usually associated with the redundancy of spectral information which leads to the problem of the curse of dimensionality. Methods currently used for recognizing objects on multispectral and hyperspectral images are usually based on standard base supervised classification algorithms of various complexity. Accuracy of these algorithms can be significantly different depending on considered classification tasks. In this paper we study the performance of ensemble classification methods for the problem of classification of the forest vegetation. Error correcting output codes and boosting are tested on artificial data and real hyperspectral images. It is demonstrates, that boosting gives more significant improvement when used with simple base classifiers. The accuracy in this case in comparable the error correcting output code (ECOC) classifier with Gaussian kernel SVM base algorithm. However the necessity of boosting ECOC with Gaussian kernel SVM is questionable. It is demonstrated, that selected ensemble classifiers allow us to recognize forest species with high enough accuracy which can be compared with ground-based forest inventory data.

  14. Groupwise consistent image registration: a crucial step for the construction of a standardized near infrared hyper-spectral teeth database

    Science.gov (United States)

    Špiclin, Žiga; Usenik, Peter; Bürmen, Miran; Fidler, Aleš; Pernuš, Franjo; Likar, Boštjan

    2011-03-01

    Construction of a standardized near infrared (NIR) hyper-spectral teeth database is a first step in the development of a reliable diagnostic tool for quantification and early detection of dental diseases. The standardized diffuse reflectance hyper-spectral database was constructed by imaging 12 extracted human teeth with natural lesions of various degrees in the spectral range from 900 to 1700 nm with spectral resolution of 10 nm. Additionally, all the teeth were imaged by X-ray and digital color camera. The color and X-ray teeth images were presented to the expert for localization and classification of the dental diseases, thereby obtaining a dental disease gold standard. Accurate transfer of the dental disease gold standard to the NIR images was achieved by image registration in a groupwise manner, taking advantage of the multichannel image information and promoting image edges as the features for the improvement of spatial correspondence detection. By the presented fully automatic multi-modal groupwise registration method, images of new teeth samples can be accurately and reliably registered and then added to the standardized NIR hyper-spectral teeth database. Adding more samples increases the biological and patho-physiological variability of the NIR hyper-spectral teeth database and can importantly contribute to the objective assessment of the sensitivity and specificity of multivariate image analysis techniques used for the detection of dental diseases. Such assessment is essential for the development and validation of reliable qualitative and especially quantitative diagnostic tools based on NIR spectroscopy.

  15. Hyperspectral optical analysis of Zumpango Lake, Mexico

    Directory of Open Access Journals (Sweden)

    Raúl Aguirre Gómez

    2013-08-01

    Full Text Available This paper shows a hyperspectral optical analysis of Zumpango Lake, relict of one of the lakes that formerly filled the Mexico Basin.  The spectral signatures are dominated by the presence of phytoplankton and submerged vegetation.  Integrated spectral curves have a good statistical correlation with chlorophyll a concentration values.  It indicates that submerged vegetation water, mainly hyacinth (Eichhornia spp and duckweed (Lemna sp, and phytoplankton are homogeneously distributed in the water body, which confers it characteristics of eutrophication.

  16. Hyperspectral Image Turbulence Measurements of the Atmosphere

    Science.gov (United States)

    Lane, Sarah E.; West, Leanne L.; Gimmestad, Gary G.; Kireev, Stanislav; Smith, William L., Sr.; Burdette, Edward M.; Daniels, Taumi; Cornman, Larry

    2012-01-01

    A Forward Looking Interferometer (FLI) sensor has the potential to be used as a means of detecting aviation hazards in flight. One of these hazards is mountain wave turbulence. The results from a data acquisition activity at the University of Colorado s Mountain Research Station will be presented here. Hyperspectral datacubes from a Telops Hyper-Cam are being studied to determine if evidence of a turbulent event can be identified in the data. These data are then being compared with D&P TurboFT data, which are collected at a much higher time resolution and broader spectrum.

  17. Discriminant Context Information Analysis for Post-Ranking Person Re-Identification.

    Science.gov (United States)

    Garcia, Jorge; Martinel, Niki; Gardel, Alfredo; Bravo, Ignacio; Foresti, Gian Luca; Micheloni, Christian

    2017-04-01

    Existing approaches for person re-identification are mainly based on creating distinctive representations or on learning optimal metrics. The achieved results are then provided in the form of a list of ranked matching persons. It often happens that the true match is not ranked first but it is in the first positions. This is mostly due to the visual ambiguities shared between the true match and other "similar" persons. At the current state, there is a lack of a study of such visual ambiguities which limit the re-identification performance within the first ranks. We believe that an analysis of the similar appearances of the first ranks can be helpful in detecting, hence removing, such visual ambiguities. We propose to achieve such a goal by introducing an unsupervised post-ranking framework. Once the initial ranking is available, content and context sets are extracted. Then, these are exploited to remove the visual ambiguities and to obtain the discriminant feature space which is finally exploited to compute the new ranking. An in-depth analysis of the performance achieved on three public benchmark data sets support our believes. For every data set, the proposed method remarkably improves the first ranks results and outperforms the state-of-the-art approaches.

  18. 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.

  19. 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.

  20. Networks of High Mutual Information Define the Structural Proximity of Catalytic Sites: Implications for Catalytic Residue Identification

    DEFF Research Database (Denmark)

    Buslje, Cristina Marino; Teppa, Elin; Di Doménico, Tomas

    2010-01-01

    . 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...... 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.......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...

  1. Inline hyperspectral thickness determination of thin films using neural networks

    Science.gov (United States)

    Tremmel, Anton J.; Weiss, Roman; Schardt, Michael; Koch, Alexander W.

    2017-04-01

    Combining reflectometry and hyperspectral imaging allows mapping of thin film thickness. Therefore, layer thickness is calculated by comparing a dataset of simulated spectra with the measured data. Utilizing the maximum frame rate of the hyperspectral imager, the pixel wise spectra comparing procedure cannot be performed using a standard computer due to the processing load. In this work, a method using neural networks for calculating layer thickness is presented. By the use of the nonlinear equation as result of a trained neural network, thickness data can be determined with a measurement rate matching the maximum frame rate of the hyperspectral imager.

  2. 77 FR 2320 - Agency Information Collection Activities: Proposed Collection; Comments Requested: Identification...

    Science.gov (United States)

    2012-01-17

    ... of Alcohol, Tobacco, Firearms and Explosives Agency Information Collection Activities: Proposed... Information Collection. The Department of Justice (DOJ), Bureau of Alcohol, Tobacco, Firearms and Explosives... Needy Road, Martinsburg, West Virginia 25405, fire[email protected] , (304) 616-4300. Written comments and...

  3. Classification and Characterization of Neotropical Rainforest Vegetation from Hyperspectral and LiDAR Data

    Science.gov (United States)

    Crawford, M. M.; Prasad, S.; Jung, J.; Yang, H.; Zhang, Y.

    2013-12-01

    Mapping species and forest vertical structure at regional, continental, and global scale is of increasing importance for climate science and decision support systems. Remote sensing technologies have been widely utilized to achieve this goal since they help overcome limitations of the direct and indirect measurement approaches. While the use of multi-sensor data for characterizing forest structure has gained significant attention in recent years, research on the integration of full waveform LiDAR and hyperspectral data for a) classification and b) characterization of vegetation structure has been limited. Given sufficient labeled ground reference samples, supervised learning methods have evolved to effectively classify data in a high dimensional feature space. However, it is expensive and time-consuming to obtain labeled data, although the very high dimensionality of feature spaces from hyperspectral and LiDAR inputs make it difficult to design reliable classifiers with a limited quantity of labeled data. Therefore, it is important to concentrate on developing training data sets which are the most 'informative' and 'useful' for the classification task. Active learning (AL) was developed in the machine learning community, and has been demonstrated to be useful for classification of remote sensing data. In the active learning framework, classifiers are initially trained on a very limited pool of training samples, but additional informative and representative samples are identified from the abundant unlabeled data, labeled, and then inducted into this pool, thereby growing the training dataset in a systematic way. The goal is to choose data points such that a more accurate classification boundary is learned. We propose a novel Multi-kernel Active Learning (MKL-AL) approach that incorporates features from multiple sensors with an automatically optimized kernel composite ¬function, and kernel parameters are selected intelligently during the AL learning process. The

  4. [Hyperspectral Band Selection Based on Spectral Clustering and Inter-Class Separability Factor].

    Science.gov (United States)

    Qin, Fang-pu; Zhang, Ai-wu; Wang, Shu-min; Meng, Xian-gang; Hu, Shao-xing; Sun, Wei-dong

    2015-05-01

    With the development of remote sensing technology and imaging spectrometer, the resolution of hyperspectral remote sensing image has been continually improved, its vast amount of data not only improves the ability of the remote sensing detection but also brings great difficulties for analyzing and processing at the same time. Band selection of hyperspectral imagery can effectively reduce data redundancy and improve classification accuracy and efficiency. So how to select the optimum band combination from hundreds of bands of hyperspectral images is a key issue. In order to solve these problems, we use spectral clustering algorithm based on graph theory. Firstly, taking of the original hyperspectral image bands as data points to be clustered , mutual information between every two bands is calculated to generate the similarity matrix. Then according to the graph partition theory, spectral decomposition of the non-normalized Laplacian matrix generated by the similarity matrix is used to get the clusters, which the similarity between is small and the similarity within is large. In order to achieve the purpose of dimensionality reduction, the inter-class separability factor of feature types on each band is calculated, which is as the reference index to choose the representative bands in the clusters furthermore. Finally, the support vector machine and minimum distance classification methods are employed to classify the hyperspectral image after band selection. The method in this paper is different from the traditional unsupervised clustering method, we employ spectral clustering algorithm based on graph theory and compute the interclass separability factor based on a priori knowledge to select bands. Comparing with traditional adaptive band selection algorithm and band index based on automatically subspace divided algorithm, the two sets of experiments results show that the overall accuracy of SVM is about 94. 08% and 94. 24% and the overall accuracy of MDC is about 87

  5. 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

  6. Hyperspectral image filtering with adaptive manifold for classification

    Science.gov (United States)

    Xie, Weiying; Li, Yunsong; Zhou, Weiping

    2017-05-01

    Hyperspectral image (HSI) is a three-dimensional data cube containing two spatial information dimensions and one spectral information dimension. The spectral vectors of different classes may have similar tendency and value that may bring about negative influences on classification. It is, therefore, important to introduce signal preprocessing techniques in the spatial domain to improve classification accuracy of HSIs. Assuming that local pixels in HSI have some correlations with each other, this paper proposes a spatial filtering model based on adaptive manifold (AM) for HSI. The AM for spatial filtering emphasizes the similar neighboring pixels and is robust to resist the noisy points with fast speed. The rich information in the filtered data is effective for improving the performance of the subsequent classification. The filtered data are classified by an extreme learning machine (ELM). The experimental results indicate that the framework built based on AM and ELM provides competitive performance. Specifically, by classifying the filtered data, the average accuracy of ELM can be improved as high as 30.54%, while performing tens to hundreds times faster than those state-of-the-art classifiers.

  7. EpiDEA: extracting structured epilepsy and seizure information from patient discharge summaries for cohort identification.

    Science.gov (United States)

    Cui, Licong; Bozorgi, Alireza; Lhatoo, Samden D; Zhang, Guo-Qiang; Sahoo, Satya S

    2012-01-01

    Sudden Unexpected Death in Epilepsy (SUDEP) is a poorly understood phenomenon. Patient cohorts to power statistical studies in SUDEP need to be drawn from multiple centers due to the low rate of reported SUDEP incidences. But the current practice of manual chart review of Epilepsy Monitoring Units (EMU) patient discharge summaries is time-consuming, tedious, and not scalable for large studies. To address this challenge in the multi-center NIH-funded Prevention and Risk Identification of SUDEP Mortality (PRISM) Project, we have developed the Epilepsy Data Extraction and Annotation (EpiDEA) system for effective processing of discharge summaries. EpiDEA uses a novel Epilepsy and Seizure Ontology (EpSO), which has been developed based on the International League Against Epilepsy (ILAE) classification system, as the core knowledge resource. By extending the cTAKES natural language processing tool developed at the Mayo Clinic, EpiDEA implements specialized functions to address the unique challenges of processing epilepsy and seizure-related clinical free text in discharge summaries. The EpiDEA system was evaluated on a corpus of 104 discharge summaries from the University Hospitals Case Medical Center EMU and achieved an overall precision of 93.59% and recall of 84.01% with an F-measure of 88.53%. The results were compared against a gold standard created by two epileptologists. We demonstrate the use of EpiDEA for cohort identification through use of an intuitive visual query interface that can be directly used by clinical researchers.

  8. 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.

  9. Hyper-spectral scanner design and analysis

    Energy Technology Data Exchange (ETDEWEB)

    Canavan, G.; Moses, J.; Smith, R.

    1996-06-01

    This is the final report of a two-year, Laboratory Directed Research and Development (LDRD) project at the Los Alamos National Laboratory (LANL). An earlier project produced rough designs for key components of a compact hyper-spectral sensor for environmental and ecological measurements. Such sensors could be deployed on unmanned vehicles, aircraft, or satellites for measurements important to agriculture, the environment, and ecologies. This represents an important advance in remote sensing. Motorola invited us to propose an add-on, proof-of-principle sensor for their Comet satellite, whose primary mission is to demonstrate a channel of the IRIDIUM satellite communications system. Our project converted the preliminary designs from the previous effort into final designs for the telescope, camera, computer and interfaces that constitute the hyper-spectral scanning sensor. The work concentrated on design, fabrication, preliminary integration, and testing of the electronic circuit boards for the computer, data compression board, and interface board for the camera-computer and computer-modulator (transmitter) interfaces.

  10. MAXIMUM MARGIN CLUSTERING OF HYPERSPECTRAL DATA

    Directory of Open Access Journals (Sweden)

    S. Niazmardi

    2013-09-01

    Full Text Available 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.

  11. 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.

  12. Onboard Image Processing System for Hyperspectral Sensor.

    Science.gov (United States)

    Hihara, Hiroki; Moritani, Kotaro; Inoue, Masao; Hoshi, Yoshihiro; Iwasaki, Akira; Takada, Jun; Inada, Hitomi; Suzuki, Makoto; Seki, Taeko; Ichikawa, Satoshi; Tanii, Jun

    2015-09-25

    Onboard image processing systems for a hyperspectral sensor have been developed in order to maximize image data transmission efficiency for large volume and high speed data downlink capacity. Since more than 100 channels are required for hyperspectral sensors on Earth observation satellites, fast and small-footprint lossless image compression capability is essential for reducing the size and weight of a sensor system. A fast lossless image compression algorithm has been developed, and is implemented in the onboard correction circuitry of sensitivity and linearity of Complementary Metal Oxide Semiconductor (CMOS) sensors in order to maximize the compression ratio. The employed image compression method is based on Fast, Efficient, Lossless Image compression System (FELICS), which is a hierarchical predictive coding method with resolution scaling. To improve FELICS's performance of image decorrelation and entropy coding, we apply a two-dimensional interpolation prediction and adaptive Golomb-Rice coding. It supports progressive decompression using resolution scaling while still maintaining superior performance measured as speed and complexity. Coding efficiency and compression speed enlarge the effective capacity of signal transmission channels, which lead to reducing onboard hardware by multiplexing sensor signals into a reduced number of compression circuits. The circuitry is embedded into the data formatter of the sensor system without adding size, weight, power consumption, and fabrication cost.

  13. Hyperspectral imaging applied to forensic medicine

    Science.gov (United States)

    Malkoff, Donald B.; Oliver, William R.

    2000-03-01

    Remote sensing techniques now include the use of hyperspectral infrared imaging sensors covering the mid-and- long wave regions of the spectrum. They have found use in military surveillance applications due to their capability for detection and classification of a large variety of both naturally occurring and man-made substances. The images they produce reveal the spatial distributions of spectral patterns that reflect differences in material temperature, texture, and composition. A program is proposed for demonstrating proof-of-concept in using a portable sensor of this type for crime scene investigations. It is anticipated to be useful in discovering and documenting the affects of trauma and/or naturally occurring illnesses, as well as detecting blood spills, tire patterns, toxic chemicals, skin injection sites, blunt traumas to the body, fluid accumulations, congenital biochemical defects, and a host of other conditions and diseases. This approach can significantly enhance capabilities for determining the circumstances of death. Potential users include law enforcement organizations (police, FBI, CIA), medical examiners, hospitals/emergency rooms, and medical laboratories. Many of the image analysis algorithms already in place for hyperspectral remote sensing and crime scene investigations can be applied to the interpretation of data obtained in this program.

  14. Identification of potential human factors issues related to APTS introduction of enhanced information systems

    Science.gov (United States)

    1993-04-01

    Introduction of enhanced information systems into an operational environment requires reallocation of functions among those responsible for providing service. This study describes an effort to develop and apply a methodology to identify the types of ...

  15. 76 FR 9550 - Proposed Information Collection; Comment Request; Northeast Region Vessel Identification Collection

    Science.gov (United States)

    2011-02-18

    ... Annual Cost to Public: $36,900 for paintbrushes, paint, and stencils. IV. Request for Comments Comments... through the use of automated collection techniques or other forms of information technology. Comments...

  16. 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.

  17. Sensitivity in forward modeled hyperspectral reflectance due to phytoplankton groups

    Science.gov (United States)

    Manzo, Ciro; Bassani, Cristiana; Pinardi, Monica; Giardino, Claudia; Bresciani, Mariano

    2016-04-01

    based on the decomposition of the output reflectance variance in partial variances of the output due to each functional group. This approach considers the sensitivity analysis of the model to each variable on its own and the corresponding interaction with the other variables, allowing identifying the single variability as well as the spectral interaction index. The analysis recognized three spectral ranges with specific level of interactions between the inputs. The first part of the spectrum up to 500 nm had average level of 10% of interaction; the second up to 600nm showed values of 5% with a peak around 580nm; the third showed an increasing interaction level until 15% near 715nm. The results presented in this study provide information relating the sensitivity of hyperspectral water reflectance as observable with band setting of the latest generation space- and air-borne sensors depending on different phytoplankton groups. In particular PRISMA was the best in the spectral sensitivity definition in the first part of the spectrum, while APEX in the second and third domain. The Sentinel 3 showed lower performances although in the third domain it was able to identify some spectral features. Results showed the Chlorophyta had high main effect at 440 nm and 480nm; sensitivity indices of phycoerythrin showed peaks at 550-580nm the range and near 680nm; phycocyanin showed high influence at 620-640nm. The research activity is part of the EU FP7 INFORM (Grant No. 606865, http://www.copernicus-inform.eu/).

  18. A block structure Laplacian for hyperspectral image data clustering

    CSIR Research Space (South Africa)

    Lunga, D

    2013-12-01

    Full Text Available Over the past decade, the problem of hyperspectral data clustering has generated a growing interest from various fields including the machine learning community. This paper presents an analysis of the traditional spectral clustering approach...

  19. 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.

  20. Compact high-resolution VIS/NIR hyperspectral sensor

    Science.gov (United States)

    Hyvärinen, Timo; Herrala, Esko; Procino, Wes; Weatherbee, Oliver

    2011-06-01

    Current hyperspectral imagers are either bulky with good performance, or compact with only moderate performance. This paper presents a new hyperspectral technology which overcomes this drawback, and makes it possible to integrate extremely compact and high performance push-broom hyperspectral imagers for Unmanned Aerial Vehicles (UAV) and other demanding applications. Hyperspectral imagers in VIS/NIR, SWIR, MWIR and LWIR spectral ranges have been implemented. This paper presents the measured performance attributes for a VIS/NIR imager which covers 350 to 1000 nm with spectral resolution of 3 nm. The key innovation is a new imaging spectrograph design which employs both transmissive and reflective optics in order to achieve high light throughput and large spatial image size in an extremely compact format. High light throughput is created by numerical aperture of F/2.4 and high diffraction efficiency. Image distortions are negligible, keystone being gimbals. In addition to laboratory characterization, results from a flight test mission are presented.

  1. Wide-Field, Deep UV Raman Hyperspectral Imager Project

    Data.gov (United States)

    National Aeronautics and Space Administration — ChemImage Sensor Systems (CISS), teaming with the University of South Carolina, proposes a revolutionary wide-field Raman hyperspectral imaging system capable of...

  2. 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....

  3. Investigation of noise sources in upconversion based infrared hyperspectral imaging

    DEFF Research Database (Denmark)

    Kehlet, Louis Martinus; Tidemand-Lichtenberg, Peter; Beato, Pablo

    2016-01-01

    Noise sources in infrared hyperspectral imaging based on nonlinear frequency upconversion are investigated. The effects on the spectral and spatial content of the images are evaluated and methods of combating them are suggested.......Noise sources in infrared hyperspectral imaging based on nonlinear frequency upconversion are investigated. The effects on the spectral and spatial content of the images are evaluated and methods of combating them are suggested....

  4. Exploiting Sparsity in Hyperspectral Image Classification via Graphical Models

    Science.gov (United States)

    2013-05-01

    Tang, “Boosting the tree augmented naive Bayes clas- sifier,” in Proc. Intell. Data Eng. Automated Learn., Lecture Notes in Computer Science, 2004...advance in hyperspectral image (HSI) classification relies on the observation that the spectral signature of a pixel can be represented by a sparse...significant recent advance in hyperspectral image (HSI) classification relies on the observation that the spectral signature of a pixel can be represented by a

  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. Forensic identification of spilled biodiesel and its blends with petroleum oil based on fingerprinting information.

    Science.gov (United States)

    Yang, Zeyu; Hollebone, Bruce P; Wang, Zhendi; Yang, Chun; Brown, Carl; Landriault, Mike

    2013-06-01

    A case study is presented for the forensic identification of several spilled biodiesels and its blends with petroleum oil using integrated forensic oil fingerprinting techniques. The integrated fingerprinting techniques combined SPE with GC/MS for obtaining individual petroleum hydrocarbons (aliphatic hydrocarbons, polyaromatic hydrocarbons and their alkylated derivatives and biomarkers), and biodiesel hydrocarbons (fatty acid methyl esters, free fatty acids, glycerol, monoacylglycerides, and free sterols). HPLC equipped with evaporative scattering laser detector was also used for identifying the compounds that conventional GC/MS could not finish. The three environmental samples (E1, E2, and E3) and one suspected source sample (S2) were dominant with vegetable oil with high acid values and low concentration of fatty acid methyl ester. The suspected source sample S2 was responsible for the three spilled samples although E1 was slightly contaminated by petroleum oil with light hydrocarbons. The suspected source sample S1 exhibited with the high content of glycerol, low content of glycerides, and high polarity, indicating its difference from the other samples. These samples may be the separated byproducts in producing biodiesel. Canola oil source is the most possible feedstock for the three environmental samples and the suspected source sample S2. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  7. 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.

  8. 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.

  9. Supercontinuum Light Sources for Hyperspectral Subsurface Laser Scattering

    DEFF Research Database (Denmark)

    Nielsen, Otto Højager Attermann; Dahl, Anders Lindbjerg; Larsen, Rasmus

    2011-01-01

    A materials structural and chemical composition influences its optical scattering properties. In this paper we investigate the use of subsurface laser scattering (SLS) for inferring structural and chemical information of food products. We have constructed a computer vision system based on a super......A materials structural and chemical composition influences its optical scattering properties. In this paper we investigate the use of subsurface laser scattering (SLS) for inferring structural and chemical information of food products. We have constructed a computer vision system based...... on a supercontinuum laser light source and an Acousto- Optic Tunable Filter (AOTF) to provide a collimated light source, which can be tuned to any wavelength in the range from 480 to 900 nm. We present the newly developed hyperspectral vision system together with a proof-of-principle study of its ability...... to discriminate between dairy products with either similar chemical or structural composition. The combined vision system is a new way for industrial food inspection allowing non-intrusive online process inspection of parameters that is hard with existing technology....

  10. Large margin distribution machine for hyperspectral image classification

    Science.gov (United States)

    Zhan, Kun; Wang, Haibo; Huang, He; Xie, Yuange

    2016-11-01

    Support vector machine (SVM) classifiers are widely applied to hyperspectral image (HSI) classification and provide significant advantages in terms of accuracy, simplicity, and robustness. SVM is a well-known learning algorithm that maximizes the minimum margin. However, recent theoretical results pointed out that maximizing the minimum margin leads to a lower generalization performance than optimizing the margin distribution, and proved that the margin distribution is more important. In this paper, a large margin distribution machine (LDM) is applied to HSI classification, and optimizing the margin distribution achieves a better generalization performance than SVM. Since the raw HSI feature space is not the most effective space for representing HSI, we adopt factor analysis to learn an effective HSI feature and the learned features are further filtered by a structure-preserved filter to fully exploit the spatial structure information of HSI. The spatial structure information is integrated in the feature learning process to obtain a better HSI feature. Then we propose a multiclass LDM to classify the filtered HSI feature. Experimental results show that the proposed LDM with feature learning method achieves the classification performance of the state-of-the-art methods in terms of visual quality and three quantitative evaluations and indicates that LDM has a high generalization performance.

  11. 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.

  12. 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…

  13. 75 FR 79029 - Proposed Extension of Existing Information Collection; Request for MSHA Individual Identification...

    Science.gov (United States)

    2010-12-17

    ... or safety standards for the protection of life and prevention of injuries in coal or other mines... FOR FURTHER INFORMATION CONTACT section of this notice, or viewed on the Internet by selecting ``Rules.... Agency: Mine Safety and Health Administration. OMB Number: 1219-0143. Frequency: On Occasion. Affected...

  14. 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.

  15. 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

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

    African Journals Online (AJOL)

    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 African indigenous breeds. Sequencing should therefore be considered to discover new SNPs that are common among indigenous ...

  17. 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:

  18. 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

  19. 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.

  20. 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.

  1. Identification of functionally diverse lipocalin proteins from sequence information using support vector machine.

    Science.gov (United States)

    Pugalenthi, Ganesan; Kandaswamy, Krishna Kumar; Suganthan, P N; Archunan, G; Sowdhamini, R

    2010-08-01

    Lipocalins are functionally diverse proteins that are composed of 120-180 amino acid residues. Members of this family have several important biological functions including ligand transport, cryptic coloration, sensory transduction, endonuclease activity, stress response activity in plants, odorant binding, prostaglandin biosynthesis, cellular homeostasis regulation, immunity, immunotherapy and so on. Identification of lipocalins from protein sequence is more challenging due to the poor sequence identity which often falls below the twilight zone. So far, no specific method has been reported to identify lipocalins from primary sequence. In this paper, we report a support vector machine (SVM) approach to predict lipocalins from protein sequence using sequence-derived properties. LipoPred was trained using a dataset consisting of 325 lipocalin proteins and 325 non-lipocalin proteins, and evaluated by an independent set of 140 lipocalin proteins and 21,447 non-lipocalin proteins. LipoPred achieved 88.61% accuracy with 89.26% sensitivity, 85.27% specificity and 0.74 Matthew's correlation coefficient (MCC). When applied on the test dataset, LipoPred achieved 84.25% accuracy with 88.57% sensitivity, 84.22% specificity and MCC of 0.16. LipoPred achieved better performance rate when compared with PSI-BLAST, HMM and SVM-Prot methods. Out of 218 lipocalins, LipoPred correctly predicted 194 proteins including 39 lipocalins that are non-homologous to any protein in the SWISSPROT database. This result shows that LipoPred is potentially useful for predicting the lipocalin proteins that have no sequence homologs in the sequence databases. Further, successful prediction of nine hypothetical lipocalin proteins and five new members of lipocalin family prove that LipoPred can be efficiently used to identify and annotate the new lipocalin proteins from sequence databases. The LipoPred software and dataset are available at http://www3.ntu.edu.sg/home/EPNSugan/index_files/lipopred.htm.

  2. 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

  3. Sofradir SWIR hyperspectral detectors for space applications

    Science.gov (United States)

    Nowicki-Bringuier, Yoanna-Reine; Chorier, Philippe

    2009-09-01

    The field of SWIR detectors for space applications is strongly growing those last years, mainly because of the increasing need for environmental missions in the SWIR detection range. For now more than 10 years, Sofradir is involved in that field, developing and improving its SWIR detectors technology, leading to a mature technology that enable to address most of missions needs in term of performances, but also with respect to hard environmental constraints. SWIR detection range at Sofradir has been qualified for space applications thanks to various programs already run (APEX or Bepi-Colombo programs) or currently running (Sentinel 2, PRISMA mission). For Sentinel 2, a 1280x3 with a 15μm pitch in the SWIR range (CTIA) has been developed and is currently being validated. 1000x256 or 500x256 arrays 30 μm pitch (called Saturn or Neptune detectors) have already been validated in terms of irradiation behavior, thermal cycling, and ageing. Specific package designs have been validated in terms of high levels of shocks and vibrations. In particular, for both Sentinel 2 and PRISMA programs, Sofradir has developed reliable packaging compatible with passive cooling. Recently, for PRISMA mission, Sofradir is extending its VISible to Short wave Infra-Red technology, called VISIR, to 1000x256 hyperspectral arrays. This technology has the huge advantage to enable detection in both visible and short wave detection range (0.4μm up to 2.5μm), thus limiting the number of needed channels for hyperspectral applications but also outshining the classical limitation of Silicon Visible detectors, for which the sensitivity is dramatically dropping above 0.9 μm. In this paper, we will focus on hyperspectral detectors available at Sofradir. Main general performances will be first described, with emphasize on the VISIR technology that has been recently developed and which enable to cover simultaneously the Visible and SWIR ranges [0.4-2.5μm] with a single detector. Then some complete

  4. 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.

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

    Science.gov (United States)

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

    2005-01-01

    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. We studied a sample of 151 stroke survivors and their primary informal caregivers. We collected data through patient and caregiver interviews 6 months after stroke. Both the level of subjective burden and the condition of feeling substantially burdened were associated with both caregiver's and patient's health-related quality of life, patient's age, and the number of caregiving tasks performed. These conditions can be used in clinical practice to identify potentially vulnerable caregivers in need of support and at risk of adverse health effects. Monitoring stroke survivors as well as their family caregivers at discharge may help to prevent or alleviate caregiver burden.

  6. Joint identification of contaminant source and barrier information in a sandbox experiment via ensemble kalman filter

    Science.gov (United States)

    Chen, Zi; Zanini, Andrea; Gómez-Hernández, J. Jaime; Xu, Teng; Cupola, Fausto

    2017-04-01

    In this work , the ensemble Kalman filter(EnKF) is employed to identify the contaminant source and barrier information in a laboratory sandbox experiment. A typical single point pollution experiment was performed in the sandbox with a barrier by using sodium fluorescein as the tracer.The movement of the contaminant was recorded by a digital camera and the contaminant concentration was obtained by the analysis of the luminosity of the pictures. The capability of the EnKF is tested through the experiment data. With a vague prior speculation of the contaminant source and barrier information, EnKF is applied to simultaneously identify these parameters through assimilating the concentration observations. The updated parameters match the actually sandbox parameters quite well implying that EnKF is an effective approach to identify the source location, barrier position, contaminant concentration and releasing history.

  7. Automatic identification of comparative effectiveness research from Medline citations to support clinicians’ treatment information needs

    Science.gov (United States)

    Zhang, Mingyuan; Fiol, Guilherme Del; Grout, Randall W.; Jonnalagadda, Siddhartha; Medlin, Richard; Mishra, Rashmi; Weir, Charlene; Liu, Hongfang; Mostafa, Javed; Fiszman, Marcelo

    2014-01-01

    Online knowledge resources such as Medline can address most clinicians’ patient care information needs. Yet, significant barriers, notably lack of time, limit the use of these sources at the point of care. The most common information needs raised by clinicians are treatment-related. Comparative effectiveness studies allow clinicians to consider multiple treatment alternatives for a particular problem. Still, solutions are needed to enable efficient and effective consumption of comparative effectiveness research at the point of care. Objective Design and assess an algorithm for automatically identifying comparative effectiveness studies and extracting the interventions investigated in these studies. Methods The algorithm combines semantic natural language processing, Medline citation metadata, and machine learning techniques. We assessed the algorithm in a case study of treatment alternatives for depression. Results Both precision and recall for identifying comparative studies was 0.83. A total of 86% of the interventions extracted perfectly or partially matched the gold standard. Conclusion Overall, the algorithm achieved reasonable performance. The method provides building blocks for the automatic summarization of comparative effectiveness research to inform point of care decision-making. PMID:23920677

  8. Automatic identification of comparative effectiveness research from medline citations to support clinicians' treatment information needs.

    Science.gov (United States)

    Zhang, Mingyuan; Del Fiol, Guilherme; Grout, Randall W; Jonnalagadda, Siddhartha; Medlin, Richard; Mishra, Rashmi; Weir, Charlene; Liu, Hongfang; Mostafa, Javed; Fiszman, Marcelo

    2013-01-01

    Online knowledge resources such as Medline can address most clinicians' patient care information needs. Yet, significant barriers, notably lack of time, limit the use of these sources at the point of care. The most common information needs raised by clinicians are treatment-related. Comparative effectiveness studies allow clinicians to consider multiple treatment alternatives for a particular problem. Still, solutions are needed to enable efficient and effective consumption of comparative effectiveness research at the point of care. Design and assess an algorithm for automatically identifying comparative effectiveness studies and extracting the interventions investigated in these studies. The algorithm combines semantic natural language processing, Medline citation metadata, and machine learning techniques. We assessed the algorithm in a case study of treatment alternatives for depression. Both precision and recall for identifying comparative studies was 0.83. A total of 86% of the interventions extracted perfectly or partially matched the gold standard. Overall, the algorithm achieved reasonable performance. The method provides building blocks for the automatic summarization of comparative effectiveness research to inform point of care decision-making.

  9. Absorption Spectrum of Phytoplankton Pigments Derived from Hyperspectral Remote-Sensing Reflectance

    National Research Council Canada - National Science Library

    Lee, ZhongPing

    2004-01-01

    ... to 11.3 mg/cubic meter, hyperspectral absorption spectra of phytoplankton pigments were independently inverted from hyperspectral remote-sensing reflectance using a newly developed ocean-color algorithm...

  10. Hyperspectral Longwave Infrared Focal Plane Array and Camera Based on Quantum Well Infrared Photodetectors Project

    Data.gov (United States)

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

  11. Hyperspectral Longwave Infrared Focal Plane Array and Camera Based on Quantum Well Infrared Photodetectors Project

    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...

  12. Analysis of pork and poultry meat and bone meal mixture using hyperspectral imaging

    Science.gov (United States)

    Oh, Mirae; Lee, Hoonsoo; Torres, Irina; Garrido Varo, Ana; Pérez Marín, Dolores; Kim, Moon S.

    2017-05-01

    Meat and bone meal (MBM) has been banned as animal feed for ruminants since 2001 because it is the source of bovine spongiform encephalopathy (BSE). Moreover, many countries have banned the use of MBM as animal feed for not only ruminants but other farm animals as well, to prevent potential outbreak of BSE. Recently, the EU has introduced use of some MBM in feeds for different animal species, such as poultry MBM for swine feed and pork MBM for poultry feed, for economic reasons. In order to authenticate the MBM species origin, species-specific MBM identification methods are needed. Various spectroscopic and spectral imaging techniques have allowed rapid and non-destructive quality assessments of foods and animal feeds. The objective of this study was to develop rapid and accurate methods to differentiate pork MBM from poultry MBM using short-wave infrared (SWIR) hyperspectral imaging techniques. Results from a preliminary investigation of hyperspectral imaging for assessing pork and poultry MBM characteristics and quantitative analysis of poultry-pork MBM mixtures are presented in this paper.

  13. First Use of an Airborne Thermal Infrared Hyperspectral Scanner for Compositional Mapping

    Science.gov (United States)

    Kirkland, Laurel; Herr, Kenneth; Keim, Eric; Adams, Paul; Salisbury, John; Hackwell, John; Treiman, Allan

    2002-01-01

    In May 1999, the airborne thermal infrared hyperspectral imaging system, Spatially Enhanced Broadband Array Spectrograph System (SEBASS), was flown over Mon-non Mesa, NV, to provide the first test of such a system for geological mapping. Several types of carbonate deposits were identified using the 11.25 microns band. However, massive calcrete outcrops exhibited weak spectral contrast, which was confirmed by field and laboratory measurements. Because the weathered calcrete surface appeared relatively smooth in hand specimen, this weak spectral contrast was unexpected. Here we show that microscopic roughness not readily apparent to the eye has introduced both a cavity effect and volume scattering to reduce spectral contrast. The macroroughness of crevices and cobbles may also have a significant cavity effect. The diminished spectral contrast is important because it places higher signal-to-noise ratio (SNR) requirements for spectroscopic detection and identification. This effect should be factored into instrumentation planning and interpretations, especially interpretations without benefit of ground truth. SEBASS had the required high SNR and spectral resolution to allow us to demonstrate for the first time the ability of an airborne hyperspectral thermal infrared scanner to detect and identify spectrally subtle materials.

  14. Non-destructive assessment of the internal quality of intact persimmon using colour and VIS/NIR hyperspectral imaging

    OpenAIRE

    Munera-Picazo, S.; Besada Ferreiro, Cristina María; Aleixos Borrás, María Nuria; Talens Oliag, Pau; Salvador, Alejandra; Sun, Da-Wen; Cubero-García, Sergio; BLASCO IVARS, JOSE

    2017-01-01

    The internal quality of intact persimmon cv. Rojo Brillante was assessed trough visible and near infrared hyperspectral imaging. Fruits at three stages of commercial maturity were exposed to different treatments with CO2 to obtain fruit with different ripeness and level of astringency (soluble tannin content). Spectral and spatial information were used for building classification models to predict ripeness and astringency trough multivariate analysis techniques like linear and quadratic dis...

  15. 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.

  16. Hyperspectral processing in graphical processing units

    Science.gov (United States)

    Winter, Michael E.; Winter, Edwin M.

    2011-06-01

    With the advent of the commercial 3D video card in the mid 1990s, we have seen an order of magnitude performance increase with each generation of new video cards. While these cards were designed primarily for visualization and video games, it became apparent after a short while that they could be used for scientific purposes. These Graphical Processing Units (GPUs) are rapidly being incorporated into data processing tasks usually reserved for general purpose computers. It has been found that many image processing problems scale well to modern GPU systems. We have implemented four popular hyperspectral processing algorithms (N-FINDR, linear unmixing, Principal Components, and the RX anomaly detection algorithm). These algorithms show an across the board speedup of at least a factor of 10, with some special cases showing extreme speedups of a hundred times or more.

  17. QUEST Hierarchy for Hyperspectral Face Recognition

    Directory of Open Access Journals (Sweden)

    David M. Ryer

    2012-01-01

    Full Text Available A qualia exploitation of sensor technology (QUEST motivated architecture using algorithm fusion and adaptive feedback loops for face recognition for hyperspectral imagery (HSI is presented. QUEST seeks to develop a general purpose computational intelligence system that captures the beneficial engineering aspects of qualia-based solutions. Qualia-based approaches are constructed from subjective representations and have the ability to detect, distinguish, and characterize entities in the environment Adaptive feedback loops are implemented that enhance performance by reducing candidate subjects in the gallery and by injecting additional probe images during the matching process. The architecture presented provides a framework for exploring more advanced integration strategies beyond those presented. Algorithmic results and performance improvements are presented as spatial, spectral, and temporal effects are utilized; additionally, a Matlab-based graphical user interface (GUI is developed to aid processing, track performance, and to display results.

  18. 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.

  19. SEGMENTATION OF HYPERSPECTRAL IMAGE USING JSEG BASED ON UNSUPERVISED CLUSTERING ALGORITHMS

    OpenAIRE

    V. Saravana Kumar; E.R. Naganathan

    2016-01-01

    Hyperspectral image analysis is a complicated and challenging task due to the inherent nature of the image. The main aim of this work is to segment the object in hyperspectral scene using image processing technique. This paper address a novel approach entitled as Segmentation of hyperspectral image using JSEG based on unsupervised cluster methods. In the preprocessing part, single band is picked out from the hyperspectral image and then converts into false color image. The JSEG algorithm is s...

  20. 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

  1. [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.

  2. Application of neural networks and information theory to the identification of E. coli transcriptional promoters

    Energy Technology Data Exchange (ETDEWEB)

    Abremski, K. (Du Pont Merck Pharmaceutical Co., Wilmington, DE (USA). Experimental Station); Sirotkin, K. (National Center for Biotechnology Information, Bethesda, MD (USA)); Lapedes, A. (Los Alamos National Lab., NM (USA))

    1991-01-01

    The Humane Genome Project has as its eventual goal the determination of the entire DNA sequence of man, which comprises approximately 3 billion base pairs. An important aspect of this project will be the analysis of the sequence to locate regions of biological importance. New computer methods will be needed to automate and facilitate this task. In this paper, we have investigated use of neural networks for the recognition of functional patterns in biological sequences. The prediction of Escherichia coli transcriptional promoters was chosen as a model system for these studies. Two approaches were employed. In the fist method, a mutual information analysis of promoter and nonpromoter sequences was carried out to demonstrate the informative base positions that help to distinguish promoter sequences from non-promoter sequences. These base positions were than used to train a Perceptron to predict new promoter sequences. In the second method, the experimental knowledge of promoters was used to indicate the important base positions in the sequence. These base positions were used to train a back propagation network with hidden units which represented regions of sequence conservation found in promoters. With both types of networks, prediction of new promoter sequences was greater than 96.9%. 12 refs., 1 fig., 4 tabs.

  3. 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

  4. Geographic information system-based identification of suitable cultivation sites for wood-cultivated ginseng.

    Science.gov (United States)

    Beon, Mu Sup; Park, Jun Ho; Kang, Hag Mo; Cho, Sung Jong; Kim, Hyun

    2013-10-01

    Wood-cultivated ginseng, including roots in its dried form, is produced in forest land without using artificial facilities such as light barriers. To identify suitable sites for the propagation of wood-cultivated ginseng, factor combination technique (FCT) and linear combination technique (LCT) were used with geographic information system and the results were superimposed onto an actual wood-cultivated ginseng plantation. The LCT more extensively searched for suitable sites of cultivation than that by the FCT; further, the LCT probed wide areas considering the predominance of precipitous mountains in Korea. In addition, the LCT showed the much higher degree of overlap with the actual cultivation sites; therefore, the LCT more comprehensively reflects the cultivator's intention for site selection. On the other hand, the inclusion of additional factors for the selection of suitable cultivation sites and experts' opinions may enhance the effectiveness and accuracy of the LCT for site application.

  5. Spectral-Spatial Shared Linear Regression for Hyperspectral Image Classification.

    Science.gov (United States)

    Haoliang Yuan; Yuan Yan Tang

    2017-04-01

    Classification of the pixels in hyperspectral image (HSI) is an important task and has been popularly applied in many practical applications. Its major challenge is the high-dimensional small-sized problem. To deal with this problem, lots of subspace learning (SL) methods are developed to reduce the dimension of the pixels while preserving the important discriminant information. Motivated by ridge linear regression (RLR) framework for SL, we propose a spectral-spatial shared linear regression method (SSSLR) for extracting the feature representation. Comparing with RLR, our proposed SSSLR has the following two advantages. First, we utilize a convex set to explore the spatial structure for computing the linear projection matrix. Second, we utilize a shared structure learning model, which is formed by original data space and a hidden feature space, to learn a more discriminant linear projection matrix for classification. To optimize our proposed method, an efficient iterative algorithm is proposed. Experimental results on two popular HSI data sets, i.e., Indian Pines and Salinas demonstrate that our proposed methods outperform many SL methods.

  6. 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.

  7. Determination of pasture quality using airborne hyperspectral imaging

    Science.gov (United States)

    Pullanagari, R. R.; Kereszturi, G.; Yule, Ian J.; Irwin, M. E.

    2015-10-01

    Pasture quality is a critical determinant which influences animal performance (live weight gain, milk and meat production) and animal health. Assessment of pasture quality is therefore required to assist farmers with grazing planning and management, benchmarking between seasons and years. Traditionally, pasture quality is determined by field sampling which is laborious, expensive and time consuming, and the information is not available in real-time. Hyperspectral remote sensing has potential to accurately quantify biochemical composition of pasture over wide areas in great spatial detail. In this study an airborne imaging spectrometer (AisaFENIX, Specim) was used with a spectral range of 380-2500 nm with 448 spectral bands. A case study of a 600 ha hill country farm in New Zealand is used to illustrate the use of the system. Radiometric and atmospheric corrections, along with automatized georectification of the imagery using Digital Elevation Model (DEM), were applied to the raw images to convert into geocoded reflectance images. Then a multivariate statistical method, partial least squares (PLS), was applied to estimate pasture quality such as crude protein (CP) and metabolisable energy (ME) from canopy reflectance. The results from this study revealed that estimates of CP and ME had a R2 of 0.77 and 0.79, and RMSECV of 2.97 and 0.81 respectively. By utilizing these regression models, spatial maps were created over the imaged area. These pasture quality maps can be used for adopting precision agriculture practices which improves farm profitability and environmental sustainability.

  8. 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.

  9. Above ground biomass estimation from lidar and hyperspectral airbone data in West African moist forests.

    Science.gov (United States)

    Vaglio Laurin, Gaia; Chen, Qi; Lindsell, Jeremy; Coomes, David; Cazzolla-Gatti, Roberto; Grieco, Elisa; Valentini, Riccardo

    2013-04-01

    The development of sound methods for the estimation of forest parameters such as Above Ground Biomass (AGB) and the need of data for different world regions and ecosystems, are widely recognized issues due to their relevance for both carbon cycle modeling and conservation and policy initiatives, such as the UN REDD+ program (Gibbs et al., 2007). The moist forests of the Upper Guinean Belt are poorly studied ecosystems (Vaglio Laurin et al. 2013) but their role is important due to the drier condition expected along the West African coasts according to future climate change scenarios (Gonzales, 2001). Remote sensing has proven to be an effective tool for AGB retrieval when coupled with field data. Lidar, with its ability to penetrate the canopy provides 3D information and best results. Nevertheless very limited research has been conducted in Africa tropical forests with lidar and none to our knowledge in West Africa. Hyperspectral sensors also offer promising data, being able to evidence very fine radiometric differences in vegetation reflectance. Their usefulness in estimating forest parameters is still under evaluation with contrasting findings (Andersen et al. 2008, Latifi et al. 2012), and additional studies are especially relevant in view of forthcoming satellite hyperspectral missions. In the framework of the EU ERC Africa GHG grant #247349, an airborne campaign collecting lidar and hyperspectral data has been conducted in March 2012 over forests reserves in Sierra Leone and Ghana, characterized by different logging histories and rainfall patterns, and including Gola Rainforest National Park, Ankasa National Park, Bia and Boin Forest Reserves. An Optech Gemini sensor collected the lidar dataset, while an AISA Eagle sensor collected hyperspectral data over 244 VIS-NIR bands. The lidar dataset, with a point density >10 ppm was processed using the TIFFS software (Toolbox for LiDAR Data Filtering and Forest Studies)(Chen 2007). The hyperspectral dataset, geo

  10. Assessing the performance of multiple spectral-spatial features of a hyperspectral image for classification of urban land cover classes using support vector machines and artificial neural network

    Science.gov (United States)

    Pullanagari, Reddy; Kereszturi, Gábor; Yule, Ian J.; Ghamisi, Pedram

    2017-04-01

    Accurate and spatially detailed mapping of complex urban environments is essential for land managers. Classifying high spectral and spatial resolution hyperspectral images is a challenging task because of its data abundance and computational complexity. Approaches with a combination of spectral and spatial information in a single classification framework have attracted special attention because of their potential to improve the classification accuracy. We extracted multiple features from spectral and spatial domains of hyperspectral images and evaluated them with two supervised classification algorithms; support vector machines (SVM) and an artificial neural network. The spatial features considered are produced by a gray level co-occurrence matrix and extended multiattribute profiles. All of these features were stacked, and the most informative features were selected using a genetic algorithm-based SVM. After selecting the most informative features, the classification model was integrated with a segmentation map derived using a hidden Markov random field. We tested the proposed method on a real application of a hyperspectral image acquired from AisaFENIX and on widely used hyperspectral images. From the results, it can be concluded that the proposed framework significantly improves the results with different spectral and spatial resolutions over different instrumentation.

  11. 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

  12. Hyperspectral Imaging Analysis for the Classification of Soil Types and the Determination of Soil Total Nitrogen

    Directory of Open Access Journals (Sweden)

    Shengyao Jia

    2017-09-01

    Full Text Available Soil is an important environment for crop growth. Quick and accurately access to soil nutrient content information is a prerequisite for scientific fertilization. In this work, hyperspectral imaging (HSI technology was applied for the classification of soil types and the measurement of soil total nitrogen (TN content. A total of 183 soil samples collected from Shangyu City (People’s Republic of China, were scanned by a near-infrared hyperspectral imaging system with a wavelength range of 874–1734 nm. The soil samples belonged to three major soil types typical of this area, including paddy soil, red soil and seashore saline soil. The successive projections algorithm (SPA method was utilized to select effective wavelengths from the full spectrum. Pattern texture features (energy, contrast, homogeneity and entropy were extracted from the gray-scale images at the effective wavelengths. The support vector machines (SVM and partial least squares regression (PLSR methods were used to establish classification and prediction models, respectively. The results showed that by using the combined data sets of effective wavelengths and texture features for modelling an optimal correct classification rate of 91.8%. could be achieved. The soil samples were first classified, then the local models were established for soil TN according to soil types, which achieved better prediction results than the general models. The overall results indicated that hyperspectral imaging technology could be used for soil type classification and soil TN determination, and data fusion combining spectral and image texture information showed advantages for the classification of soil types.

  13. 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.

  14. Characterization of cirrus clouds and atmospheric state using a new hyper-spectral optimal estimation retrieval

    Science.gov (United States)

    Veglio, P.; Holz, R.

    2016-12-01

    The importance of cirrus clouds as regulators of Earth's climate and radiation budget has been widely demonstrated, but still their characterization remains challenging. In order to derive cirrus properties, many retrieval techniques rely on prior assumptions on the atmospheric state or on the ice microphysics, either because the computational cost is too high or because the measurements do not have enough information, as in the case of broadband sensors. In this work we present a novel infrared hyper-spectral optimal estimation retrieval capable of simultaneously deriving cirrus cloud parameters (optical depth, effective radius, cloud top height) and atmospheric state (temperature and water vapor profiles) with their associated uncertainties by using a fast forward radiative transfer code. The use of hyperspectral data help overcoming the problem of the information content while the computational cost can be addressed by using a fast radiative transfer model. The tradeoff of this choice is an increasing in the complexity of the problem. Also, it is important to consider that by using a fast, approximate radiative transfer model, the uncertainties must be carefully evaluated in order to prevent or minimize any biases that could negatively affect the results. For this application data from the HS3 field campaign are used, which provide high quality hyper-spectral measurements from Scanning HIS along with CPL and possibly also dropsonde data and GDAS reanalysis to help validate the results. The future of this work will be to move from aircraft to satellite observations, and the natural choice is AIRS and CALIOP that offer a similar setup to what is currently used for this study.

  15. 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.

  16. A proteomic approach for the rapid, multi-informative and reliable identification of blood.

    Science.gov (United States)

    Patel, E; Cicatiello, P; Deininger, L; Clench, M R; Marino, G; Giardina, P; Langenburg, G; West, A; Marshall, P; Sears, V; Francese, S

    2016-01-07

    Blood evidence is frequently encountered at the scene of violent crimes and can provide valuable intelligence in the forensic investigation of serious offences. Because many of the current enhancement methods used by crime scene investigators are presumptive, the visualisation of blood is not always reliable nor does it bear additional information. In the work presented here, two methods employing a shotgun bottom up proteomic approach for the detection of blood are reported; the developed protocols employ both an in solution digestion method and a recently proposed procedure involving immobilization of trypsin on hydrophobin Vmh2 coated MALDI sample plate. The methods are complementary as whilst one yields more identifiable proteins (as biomolecular signatures), the other is extremely rapid (5 minutes). Additionally, data demonstrate the opportunity to discriminate blood provenance even when two different blood sources are present in a mixture. This approach is also suitable for old bloodstains which had been previously chemically enhanced, as experiments conducted on a 9-year-old bloodstain deposited on a ceramic tile demonstrate.

  17. 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.

  18. 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.

  19. How Cities Breathe: Ground-Referenced, Airborne Hyperspectral Imaging Precursor Measurements To Space-Based Monitoring

    Science.gov (United States)

    Leifer, Ira; Tratt, David; Quattrochi, Dale; Bovensmann, Heinrich; Gerilowski, Konstantin; Buchwitz, Michael; Burrows, John

    2013-01-01

    Methane's (CH4) large global warming potential (Shindell et al., 2012) and likely increasing future emissions due to global warming feedbacks emphasize its importance to anthropogenic greenhouse warming (IPCC, 2007). Furthermore, CH4 regulation has far greater near-term climate change mitigation potential versus carbon dioxide CO2, the other major anthropogenic Greenhouse Gas (GHG) (Shindell et al., 2009). Uncertainties in CH4 budgets arise from the poor state of knowledge of CH4 sources - in part from a lack of sufficiently accurate assessments of the temporal and spatial emissions and controlling factors of highly variable anthropogenic and natural CH4 surface fluxes (IPCC, 2007) and the lack of global-scale (satellite) data at sufficiently high spatial resolution to resolve sources. Many important methane (and other trace gases) sources arise from urban and mega-urban landscapes where anthropogenic activities are centered - most of humanity lives in urban areas. Studying these complex landscape tapestries is challenged by a wide and varied range of activities at small spatial scale, and difficulty in obtaining up-to-date landuse data in the developed world - a key desire of policy makers towards development of effective regulations. In the developing world, challenges are multiplied with additional political access challenges. As high spatial resolution satellite and airborne data has become available, activity mapping applications have blossomed - i.e., Google maps; however, tap a minute fraction of remote sensing capabilities due to limited (three band) spectral information. Next generation approaches that incorporate high spatial resolution hyperspectral and ultraspectral data will allow detangling of the highly heterogeneous usage megacity patterns by providing diagnostic identification of chemical composition from solids (refs) to gases (refs). To properly enable these next generation technologies for megacity include atmospheric radiative transfer modeling

  20. A mutual information approach to automate identification of neuronal clusters in Drosophila brain images

    Directory of Open Access Journals (Sweden)

    Nicolas Yvan Masse

    2012-06-01

    Full Text Available Mapping neural circuits can be accomplished by labeling a small number of neural structures per brain, and then combining these structures across multiple brains. This sparse labeling method has been particularly effective in Drosophila melanogaster, where clonally related clusters of neurons derived from the same neural stem cell (neuroblast clones are functionally related and morphologically highly stereotyped across animals. However identifying these neuroblast clones (approximately 180 per central brain hemisphere manually remains challenging and time consuming. Here, we take advantage of the stereotyped nature of neural circuits in Drosophila to automatically identify clones, requiring manual annotation of only an initial, smaller set of images. Our procedure starts by filtering the images to accentuate neural projections and cell bodies, and then skeletonises the projections with a dimension reduction algorithm. Images are then registered onto a common template brain, allowing us to determine which projections and cell bodies are shared across different brains. We then determine whether the presence of a cell body or projection is associated with the presence of a clone, allowing us identify the neural structures that can reliably indicate whether a brain contains a specific clone. This enables us to detect the presence of clones in novel images by mapping their cell bodies and projections and matching them against these informative neural structures. The approach is not limited to a specific labeling strategy and can be used to identify partial (e.g. individual neurons as well as complete matches. Furthermore this approach could be generalised to studies of neural circuits in other organisms.