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检索条件"任意字段=Conference on Algorithms for Multispectral and Hyperspectral Imagery IV"

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Crop classification using temporal stacks of multispectral satellite imagery 23

Crop classification using temporal stacks of multispectral s...

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23rd SPIE conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery

作者： Moody, Daniela I. Brumby, Steven P. Chartrand, Rick Keisler, Ryan Longbotham, Nathan Mertes, Carly Skillman, Samuel W. Warren, Michael S. Descartes Labs Inc Los Alamos NM 87544 USA

ISBN: (数字)9781510608986

ISBN: (纸本)9781510608979;9781510608986

The increase in performance, availability, and coverage of multispectral satellite sensor constellations has led to a drastic increase in data volume and data rate. Multi-decadal remote sensing datasets at the petabyte scale are now available in commercial clouds, with new satellite constellations generating petabytes/year of daily high-resolution global coverage imagery. The data analysis capability, however, has lagged behind storage and compute developments, and has traditionally focused on individual scene processing. We present results from an ongoing effort to develop satellite imagery analysis tools that aggregate temporal, spatial, and spectral information and can scale with the high-rate and dimensionality of imagery being collected. We investigate and compare the performance of pixel-level crop identification using tree-based classifiers and its dependence on both temporal and spectral features. Classification performance is assessed using as ground-truth Cropland Data Layer (CDL) crop masks generated by the US Department of Agriculture (USDA). The CDL maps contain 30m spatial resolution, pixel-level labels for around 200 categories of land cover, but are however only available post-growing season. The analysis focuses on McCook county in South Dakota and shows crop classification using a temporal stack of Landsat 8 (L8) imagery over the growing season, from April through October. Specifically, we consider the temporal L8 stack depth, as well as different normalized band difference indices, and evaluate their contribution to crop identification. We also show an extension of our algorithm to map corn and soy crops in the state of Mato Grosso, Brazil.

关键词： multispectral classification crop classification temporal imagery stacks Landsat 8 classification normalized band difference index classification precision agriculture.

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EVALUATION OF DIMENSIONALITY REDUCTION TECHNIQUES IN hyperspectral imagery AND THEIR APPLICATION FOR THE CLASSIFICATION OF TERRESTRIAL ECOSYSTEMS 23

EVALUATION OF DIMENSIONALITY REDUCTION TECHNIQUES IN HYPERSP...

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conference on Image and Signal Processing for Remote Sensing XXIII

作者： Ibarrola-Ulzurrun, Edurne Marcello, Javier Gonzalo-Martin, Consuelo ULPGC Inst Oceanog & Cambio Global IOCAG Parque Cient Tecnol Marino Taliarte S-N Telde 35214 Las Palmas Spain Univ Politecn Madrid Ctr Biomed Technol Campus Montegancedo Madrid 28223 Spain

ISBN: (数字)9781510613195

ISBN: (纸本)9781510613195;9781510613188

The hyperspectral imagery is formed by a several narrow and continuous bands covering different regions of the electromagnetic spectrum, such as spectral bands of the visible, near infrared and far infrared. hyperspectral imagery provides extremely higher spectral resolution than high spatial resolution multispectral imagery, improving the detection capability of terrestrial objects. The greatest difficulty found in the hyperspectral processing is the high dimensionality of these data, which brings out the 'Hughes' phenomenon. This phenomenon specifies that the size of training set required for a given classification increases exponentially with the number of spectral bands. Therefore, the dimensionality of the hyperspectral data is an important drawback when applying traditional classification or pattern recognition approaches to this hyperspectral imagery. In our context, the dimensionality reduction is necessary to obtain accurate thematic maps of natural protected areas. Dimensionality reduction can be divided into the feature-selection algorithms and feature-extraction algorithms. We focus the study in the feature-extraction algorithms like Principal Component Analysis (PCA), Minimum Noise Fraction (MNF) and Independent Component Analysis (ICA). After a review of the state-of-art, it has been observed a lack of a comparative study on the techniques used in the hyperspectral imagery dimensionality reduction. In this context, our objective was to perform a comparative study of the traditional techniques of dimensionality reduction (PCA, MNF and ICA) to evaluate their performance in the classification of high spatial resolution imagery of the CASI (Compact Airborne Spectrographic Imager) sensor.

关键词： hyperspectral imaging Image classification Independent component analysis Principal component analysis Detection and tracking algorithms Ecosystems Sensors Spatial resolution Electromagnetism Remote sensing

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Ensemble Learning and Model Averaging for Material Identification in hyperspectral imagery 23

Ensemble Learning and Model Averaging for Material Identific...

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23rd SPIE conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery

作者： Basener, William F. Univ Virginia Spectral Solut Fac Charlottesville VA 22903 USA Rochester Inst Technol Rochester NY 14623 USA

ISBN: (数字)9781510608986

ISBN: (纸本)9781510608979;9781510608986

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.

关键词： hyperspectral remote sensing target detection ATR material identification Bayesian Information BIC machine learning model averaging ensemble learning

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A Pigment Analysis Tool for hyperspectral Images of Cultural Heritage Artifacts 23

A Pigment Analysis Tool for Hyperspectral Images of Cultural...

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23rd SPIE conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery

作者： Bai, Di Messinger, David W. Howell, David Rochester Inst Technol Chester F Carlson Ctr Imaging Sci Rochester NY 14623 USA Univ Oxford Bodleian Lib Broad St Oxford OX1 3BG England

ISBN: (数字)9781510608986

ISBN: (纸本)9781510608979;9781510608986

The Gough Map, in the collection at the Bodleian Library, Oxford University, is one of the earliest surviving maps of Britain. Previous research deemed that it was likely created over the 15th century and afterwards it was extensively revised more than once. In 2015, the Gough Map was imaged using a hyperspectral imaging system at the Bodleian Library. The collection of the hyperspectral image (HSI) data was aimed at faded text enhancement for reading and pigment analysis for the material diversity of its composition and potentially the timeline of its creation. In this research, we introduce several methods to analyze the green pigments in the Gough Map, especially the number and spatial distribution of distinct green pigments. One approach, called the Gram Matrix, has been used to estimate the material diversity in a scene (i.e., endmember selection and dimensionality estimation). Here, we use the Gram Matrix technique to study the within-material differences of pigments in the Gough map with common visual color. We develop a pigment analysis tool that extracts visually common pixels, green pigments in this case, from the Gough Map and estimates its material diversity. It reveals that the Gough Map consists of at least six kinds of dominant green pigments. Both historical geographers and cartographic historians will benefit from this work to analyze the pigment diversity using HSI of cultural heritage artifacts.

关键词： hyperspectral imagery pigment analysis dimensionality estimation

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Peculiarities of use of ECOC and AdaBoost based classifiers for thematic processing of hyperspectral data 8

Peculiarities of use of ECOC and AdaBoost based classifiers ...

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17th SPIE conference on Earth Resources and Environmental Remote Sensing/GIS Applications

作者： Dementyev, A. O. Dmitriev, E. V. Kozoderov, V. V. Egorov, V. D. Russian Acad Sci Inst Numer Math Ul Gubkina 8 Moscow 119333 Russia Moscow Inst Phys & Technol Inst Skiy Per 9 Dolgoprudnyi 141700 Moscow Region Russia Moscow MV Lomonosov State Univ Leninskiye Gory 1 Moscow 119991 Russia

ISBN: (纸本)9781510613218;9781510613201

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.

关键词： hyperspectral imagery pattern recognition classification of the forest vegetation forest inventory parameters

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On the creation of high spatial resolution imaging spectroscopy data from multi-temporal low spatial resolution imagery 23

On the creation of high spatial resolution imaging spectrosc...

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23rd SPIE conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery

作者： Yao, Wei van Aardt, Jan Messinger, David Rochester Inst Technol Chester F Carlson Ctr Imaging Sci 54 Lomb Mem Dr Rochester NY 14623 USA

ISBN: (数字)9781510608986

ISBN: (纸本)9781510608979;9781510608986

benefit of especially ecosystem studies. The onboard spectrometer will collect radiance spectra from the visible to short wave infrared (VSWIR) regions (400-2500 nm). The mission calls for fine spectral resolution (10 nm band width) and as such will enable scientists to perform material characterization, species classification, and even sub-pixel mapping. However, the global coverage requirement results in a relatively low spatial resolution (GSD 30m), which restricts applications to objects of similar scales. We therefore have focused on the assessment of sub pixel vegetation structure from spectroscopy data in past studies. In this study, we investigate the development or reconstruction of higher spatial resolution imaging spectroscopy data via fusion of multi-temporal data sets to address the drawbacks implicit in low spatial resolution imagery. The projected temporal resolution of the HyspIRI VSWIR instrument is 15 days, which implies that we have access to as many as six data sets for an area over the course of a growth season. Previous studies have shown that select vegetation structural parameters, e.g., leaf area index (LAI) and gross ecosystem production (GEP), are relatively constant in summer and winter for temperate forests;we therefore consider the data sets collected in summer to be from a similar, stable forest structure. The first step, prior to fusion, involves registration of the multi-temporal data. A data fusion algorithm then can be applied to the pre-processed data sets. The approach hinges on an algorithm that has been widely applied to fuse RGB images. Ideally, if we have four images of a scene which all meet the following requirements- i) they are captured with the same camera configurations;ii) the pixel size of each image is x;and iii) at least r(2) images are aligned on a grid of x/r- then a high-resolution image, with a pixel size of x/r, can be reconstructed from the multi-temporal set. The algorithm was applied to data from NASA's clas

关键词： Fusion super-resolution hyperspectral image imaging spectroscopy data HyspIRI AVIRIS

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Comparison of crop discrimination using AVIRIS-NG and liss-iv data over heterogeneous agricultural patches 38

Comparison of crop discrimination using AVIRIS-NG and liss-I...

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38th Asian conference on Remote Sensing - Space Applications: Touching Human Lives, ACRS 2017

作者： Kot, Rajsi Nigam, Rahul Shukla, Alpana Bhattacharya, Bimal K M.G. Science Institute Gujarat University Ahmedabad380009 India Ahmedabad380015 India

Crop mapping and discrimination provide an important basis for many agricultural applications such as acreage, biomass, yield, crop rotation and soil productivity. Remote sensing data, methods and approaches provide the best options for large area agricultural cropland characterization for precision agricultural management practices by accurately mapping of crop type and yield indicators. Traditional multispectral broadband sensor data have known limitations of sensor saturation and absence of specific narrow bands to target and highlight specific biophysical and biochemical characteristics according to crop type. These factors lead to significant uncertainties in the discrimination of crop type. Recent advances in hyperspectral remote sensing technology provide the opportunity to measure the response of different crop type in terms of morphological and physiological characteristics. The specific narrow bands have a capability to perform crop discrimination over homogeneous and heterogeneous agricultural areas. The continuous band spectrum from imaging spectroscopy have opened up new avenues in the field of classification. In this study, crop discrimination has been carried out using principal component analysis and supervised classification techniques such as maximum likelihood classification (MLC) and spectral angle mapper (SAM) algorithms. In this study, AVIRIS-NG airborne hyperspectral data acquired on Maddur, Karnataka and equivalent multispectral LISS-iv data convolved through three broadband regions (Green: 0.52-0.59 nm, red: 0.62-0.68 nm, near-infrared: 0.77-0.86 nm) using spectral response function of Resourcesat-2 (RS-2) LISS-iv, were used over mixed and heterogeneous agricultural area of Maddur, Karnataka in Berambadi watershed located in Kabini river basin. The dominant soil types were red and black soils. Data dimensional reduction has been carried out using principal component analysis. In situ crop information were used to perform SAM and MLC-based cl

关键词： Remote sensing

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Biased Normalized Cuts for Target Detection in hyperspectral imagery 22

Biased Normalized Cuts for Target Detection in Hyperspectral...

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22nd SPIE conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XXII

作者： Zhang, Xuewen Dorado-Munoz, Leidy P. Messinger, David W. Cahill, Nathan D. Rochester Inst Technol Chester F Carlson Ctr Imaging Sci Rochester NY 14623 USA Rochester Inst Technol Sch Math Sci Image Comp & Anal Lab Rochester NY 14623 USA

ISBN: (纸本)9781510600812

The Biased Normalized Cuts (BNC) algorithm is a useful technique for detecting targets or objects in RGB imagery. In this paper, we propose modifying BNC for the purpose of target detection in hyperspectral imagery. As opposed to other target detection algorithms that typically encode target information prior to dimensionality reduction, our proposed algorithm encodes target information after dimensionality reduction, enabling a user to detect different targets in interactive mode. To assess the proposed BNC algorithm, we utilize hyperspectral imagery (HSI) from the SHARE 2012 data campaign, and we explore the relationship between the number and the position of expert-provided target labels and the precision/recall of the remaining targets in the scene.

关键词： Biased Normalized Cuts Dimensionality Reduction Laplacian Eigenmaps Schroedinger Eigenmaps Target Detection

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Anomaly detection in hyperspectral imagery: statistics- vs. graph-based algorithms 22

Anomaly detection in hyperspectral imagery: statistics- vs. ...

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22nd SPIE conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XXII

作者： Berkson, Emily E. Messinger, David W. Rochester Inst Technol Chester F Carlson Ctr Imaging Sci 54 Lomb Mem Dr Rochester NY 14623 USA

ISBN: (纸本)9781510600812

Anomaly detection (AD) algorithms are frequently applied to hyperspectral imagery, but different algorithms produce different outlier results depending on the image scene content and the assumed background model. This work provides the first comparison of anomaly score distributions between common statistics-based anomaly detection algorithms (RX and subspace-RX) and the graph-based Topological Anomaly Detector (TAD). Anomaly scores in statistical AD algorithms should theoretically approximate a chi-squared distribution;however, this is rarely the case with real hyperspectral imagery. The expected distribution of scores found with graph-based methods remains unclear. We also look for general trends in algorithm performance with varied scene content. Three separate scenes were extracted from the hyperspectral MegaScene image taken over downtown Rochester, NY with the VIS-NIR-SWIR ProSpecTIR instrument. In order of most to least cluttered, we study an urban, suburban, and rural scene. The three AD algorithms were applied to each scene, and the distributions of the most anomalous 5% of pixels were compared. We find that subspace-RX performs better than RX, because the data becomes more normal when the highest variance principal components are removed. We also see that compared to statistical detectors, anomalies detected by TAD are easier to separate from the background. Due to their different underlying assumptions, the statistical and graph-based algorithms highlighted different anomalies within the urban scene. These results will lead to a deeper understanding of these algorithms and their applicability across different types of imagery.

关键词： anomaly detection topological anomaly detector (TAD) Reed-Xiaoli (RX) subspace-RX (SSRX) statistical graph-based scene clutter

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A nonlinear modeling framework for the detection of underwater objects in hyperspectral imagery 22

A nonlinear modeling framework for the detection of underwat...

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22nd SPIE conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XXII

作者： Gillis, David B. Naval Res Lab 4555 Overlook Ave SW Washington DC 20375 USA

ISBN: (纸本)9781510600812

The detection of underwater objects of interest (or targets) in hyperspectral imagery is a challenging problem, with a number of complications that are not present in land-based hyperspectral target detection. The main challenge in underwater detection is that, in contrast to land, where the observed spectrum of an associated target is largely independent of the surrounding background (e.g. the signature of a tank looks more or less the same whether it is on a road or in a field of grass), the observed spectrum of an underwater target is a highly nonlinear function of the background that is, the optical properties of the water body that the object is submerged in, as well as the depth of the target. As a result, the same object in different types of water and/or at different depths will in general have very different observed spectral signatures. In this work, we present a general overview of the various challenges involved in underwater detection, and present a novel approach that fuses forward radiative-transfer modeling, ocean color predictions, and nonlinear mathematical techniques (manifold learning) to model both the background and target signature(s) and perform detection over a wide range of environmental conditions and depths.

关键词： hyperspectral underwater target detection manifold learning

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