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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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A novel method for change detection in spectral imagery

A novel method for change detection in spectral imagery

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conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XVI

作者： Schlamm, Ariel Messinger, David Basener, William Rochester Inst Technol Ctr Imaging Sci Digital Imaging & Remote Sensing Lab 54 Lomb Mem Dr Rochester NY 14623 USA Rochester Inst Technol Sch Math Sci Rochester NY 14623 USA

ISBN: (纸本)9780819481597

A new method for change detection of two large area scenes based on the point density of the pixel distribution in the hyperspace is presented. This method is derived from the point density approach to hyperspectral analysis, originally developed for material discrimination based on inherent dimension estimation. In this method, two registered large area scenes are tiled for individual scoring and comparison. The point density tail length is estimated for each tile in both scenes. The difference between this value for corresponding tiles indicates whether change has likely occurred in a tile and how significant the change is relative to other changes in the image. The method does not identify changes in individual pixels, but uses a tiling approach to identify changes in small sub-regions of the image. Preliminary results of this methodology are presented for multiple images and changing scene phenomenology.

关键词： hyperspectral change detection search

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Sparse Subspace Target Detection for hyperspectral imagery

Sparse Subspace Target Detection for Hyperspectral Imagery

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conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XVI

作者： Chen, Yi Nasrabadi, Nasser M. Tran, Trac D. Johns Hopkins Univ Dept Elect & Comp Engn 3400 N Charles St Baltimore MD 21218 USA US Army Res Lab Adelphi MD 20783 USA

ISBN: (纸本)9780819481597

In this paper, we propose a new sparsity-based algorithm for automatic target detection in hyperspectral images (HSI). This algorithm is based on the concept that a pixel in HSI lies in a low-dimensional subspace and thus can be represented by a sparse linear combination of the training samples. The sparse representation (a sparse vector representing the selected training samples) of the test sample can be recovered by solving an l(0)-norm minimization problem. With the recent development of Compressed Sensing theories, the minimization problem can be recast as a linear programming or solved efficiently by a greedy pursuit algorithm. Once the sparse vector is obtained, the class of the test sample can be directly determined by the behavior of the vector on reconstruction. In addition to the constraints on sparsity and reconstruction accuracy, we also exploit the fact that HSI are usually smooth in that neighboring pixels have a similar spectral characteristic. In our proposed algorithm, a smoothness constraint is also imposed by forcing the Laplacian of the reconstructed image to be zero in the minimization process. The proposed sparsity-based algorithm is applied to several hyperspectral images to detect targets of interest. Simulation results show that our algorithm outperforms the other HSI target detection algorithms, including the popular spectral matched filters, matched subspace detectors, and adaptive subspace detectors.

关键词： Target detection sparse representation l(0)-norm minimization

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Detection and Characterization of Chemical Vapor Fugitive Emissions from hyperspectral Infrared imagery by Nonlinear Optimal Estimation

Detection and Characterization of Chemical Vapor Fugitive Em...

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conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XVI

作者： Gittins, Christopher M. Phys Sci Inc Andover MA 01810 USA

ISBN: (纸本)9780819481597

The clutter-matched filter (CMF) and the Adaptive Cosine Estimator (ACE) have become established metrics for detecting chemical vapor plumes from hyperspectral infrared imagery. Both metrics follow from the presumption of a linear additive signal model. However, examination of the underlying radiative transfer equation (RTE) indicates that while the use of a linear additive signal model is a reasonable approximation when considering an optically-thin plume viewed against blackbody background the RTE is in fact nonlinear. Unfortunately, presumption of a linear additive signal model can significantly degrade plume detection statistics and results in significant errors in estimated chemical vapor column density when plumes are not optically-thin or are viewed against spectrally-complex backgrounds. This paper describes a nonlinear estimation approach which integrates a parameterized signal model based on the RTE with a statistical model for the infrared background. We show results obtained by applying the nonlinear estimation approach to background-only hyperspectral imagery augmented with synthetic chemical vapor plumes and compare them with results obtained presuming a linear additive signal model. As plumes become optically-thick, nonlinear estimation yields significantly more accurate estimates of chemical vapor column density and significantly more favorable plume detection statistics than clutter-matched-filter-based and adaptive-subspace-detector-based plume characterization and detection.

关键词： nonlinear estimation hyperspectral remote sensing spectroscopy Gauss-Newton

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Improved Feature Extraction from High-Resolution Remotely Sensed imagery using Object Geometry

Improved Feature Extraction from High-Resolution Remotely Se...

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conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XVI

作者： Momm, H. G. Gunter, Bryan Easson, Greg Univ Mississippi Geoinformat Ctr University MS 38677 USA

ISBN: (纸本)9780819481597

Information extraction from high spatial resolution imagery is sometimes hampered by the limited number of spectral channels available from these systems. Standard supervised classification algorithms found in commercial software packages may misclassify different features with similar spectral characteristics;leading to a high occurrence of false positives. An additional step in the information extraction process was developed incorporating the concept of object geometry. Objects are defined as a contiguous group of pixels identified as belonging to a single class in the spectral classification. Using results from the spectral classification, a supervised approach was developed using genetic programming to select and mathematically combine feature-specific shape descriptors from a larger set of shape descriptors, to form a new classifier. This investigation focused on extraction of residential housing from QuickBird and IKONOS imagery of the Mississippi Gulf Coast before and immediately after hurricane Katrina. Use of genetic programming significantly reduced false positives caused by asphalt pavement and isolated roofing material scattered throughout the image.

关键词： Image shape descriptors Remote sensing Genetic programming

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A Scalable Approach to Modeling Nonlinear Structure in hyperspectral imagery and Other High-Dimensional Data Using Manifold Coordinate Representations

A Scalable Approach to Modeling Nonlinear Structure in Hyper...

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conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XVI

作者： Bachmann, Charles M. Ainsworth, Thomas L. Fusina, Robert A. USN Res Lab Remote Sensing DivCoastal Sci & Interpretat Sect Coastal & Ocean Remote Sensing Branch Code 72324555 Overlook Ave SW Washington DC 20375 USA Naval Res Lab Remote Sensing Div Image Sci Sect Washington DC 20375 USA

ISBN: (纸本)9780819481597

In the past we have presented a framework for deriving a set of intrinsic manifold coordinates that directly parameterize high-dimensional data, such as that found in hyperspectral imagery(1234567).(10) In these previous works, we have described the potential utility of these representations for such diverse problems as land-cover mapping and in-water retrievals such as bathymetry.(10) Because the manifold coordinates are intrinsic, they offer the potential for significant compression of the data, and are furthermore very useful for displaying data structure that can not be seen by linear image processing representations when the data is inherently nonlinear. This is especially true, for example, when the data are known to contain strong nonlinearities, such as in the reflectance data obtained from hyperspectral imaging sensors over the water, where the medium itself is attenuating(235).(7) These representations are also potentially useful in such applications as anomaly finding(2).(3) A number of other researchers have looked at different aspects of the manifold coordinate representations such as the best way to exploit these representations through the backend classifier,(15) while others have examined alternative manifold coordinate models.(14) In this paper, we provide an overview of our scalable algorithm for deriving manifold coordinate representations of high-dimensional data such as hyperspectral imagery, describe some of our recent work to improve the local estimation of spectral neighborhood size, and demonstrate the benefits for problems such as anomaly finding.

关键词： manifold coordinates manifold learning hyperspectral imagery neighborhood local intrinsic dimensionality nonlinear anomaly finding

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Atmospheric Compensation of Thermal Infrared hyperspectral imagery with the Emissive Empirical Line Method and the In-Scene Atmospheric Compensation algorithms: A Comparison

Atmospheric Compensation of Thermal Infrared Hyperspectral I...

引用

conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XVI

作者： DiStasio, Robert J., Jr. Resmini, Ronald G. Mitre Corp Mclean VA 22102 USA

ISBN: (纸本)9780819481597

The in-scene atmospheric compensation (ISAC) algorithm of Young et al. (2002) [14] (and as implemented in the ENVI (R) software system [16] as 'Thermal Atm Correction') is commonly applied to thermal infrared multi-and hyperspectral imagery (MSI and HSI, respectively). ISAC estimates atmospheric transmissivity and upwelling radiance using only the scene data. The ISAC-derived transmissivity and upwelling radiance are compared to those derived from the emissive empirical line method (EELM), another in-scene atmospheric compensation algorithm for thermal infrared MSI and HSI data. EELM is based on the presence of calibration targets (e. g., panels, water pools) captured in the spectral image data for which the emissivity and temperature are well known at the moment of MSI/HSI data acquisition. EELM is similar in concept to the empirical line method (ELM) algorithm commonly applied to visible/near-infrared to shortwave infrared (VNIR/SWIR) spectral imagery and is implemented as a custom ENVI (R) plug-in application. Both ISAC and EELM are in-scene methods and do not require radiative transfer modeling. ISAC and EELM have been applied to airborne longwave infrared (LWIR;similar to 7.5 mu m to similar to 13.5 mu m) HSI data. Captured in the imagery are calibration panels and/or water pools maintained at different temperatures facilitating the application of EELM. Overall, the atmospheric compensation parameters derived from the two methods are in close agreement: the EELM-derived ground-leaving radiance spectra generally contain fewer residual atmospheric spectral features, although ISAC sometimes produces smoother ground-leaving radiance spectra. Nonetheless, the agreement is viewed as validation of ISAC. ISAC is an effective atmospheric compensation algorithm that is readily available to the remote sensing community in the ENVI (R) software system. Thus studies such as the present testing and comparing ISAC to other methods are important. The ISAC and EELM algorithms a

关键词： ISAC emissive empirical line method EELM longwave infrared LWIR thermal infrared hyperspectral HSI atmospheric compensation atmospheric correction radiance temperature emissivity

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Sub-Pixel Radiometry: A three-part study in generating synthetic imagery that incorporates sub-pixel variation

Sub-Pixel Radiometry: A three-part study in generating synth...

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conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XVI

作者： Paul, Sarah E. Goodenough, Adam A. Brown, Scott D. Salvaggio, Carl Rochester Inst Technol Ctr Imaging Sci Digital Imaging Remote Sensing Lab Rochester NY 14623 USA

ISBN: (纸本)9780819481597

A pixel represents the limit of spatial knowledge that can be represented in an image. It is represented as a single (perhaps spectral) digital count value that represents the energy propagating from a spatial portion of a scene. In any captured image, that single value is the result of many factors including the composition of scene optical properties within the projected pixel, the characteristic point spread function (or, equivalently, modulation transfer function) of the system, and the sensitivity of the detector element itself. This presentation examines the importance of sub-pixel variability in the context of generating synthetic imagery for remote sensing applications. The study was performed using the Digital Imaging and Remote Sensing Image Generation (DIRSIG) tool, an established ray-tracing based synthetic modeling system whose approach to sub-pixel computations was updated during this study. The paper examines three aspects of sub-pixel variability of interest to the remote sensing community. The first study simply looks at sampling frequency relative to structural frequency in a scene and the effects of aliasing on an image. The second considers the task of modeling a sub-pixel target whose signature would be mixed with background clutter, such as a small, hot target in a thermal image. The final study looks at capturing the inherent spectral variation in a single class of material, such as grass in hyperspectral imagery. Through each study we demonstrate in a quantitative fashion, the improved capabilities of DIRSIG's sub-pixel rendering algorithms.

关键词： sampling DIRSIG PSF small target radiometry sub-pixel synthetic imagery clutter hyperspectral thermal aliasing ray-tracing

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Iterative Convex Hull Volume Estimation in hyperspectral imagery for Change Detection

Iterative Convex Hull Volume Estimation in Hyperspectral Ima...

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conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XVI

作者： Ziemann, Amanda K. Messinger, David W. Basener, William F. Rochester Inst Technol Sch Math Sci Rochester NY 14623 USA Rochester Inst Technol Chester F Carlson Ctr Imaging Sci Rochester NY 14623 USA

ISBN: (纸本)9780819481597

Historically in change detection, statistically based methods have been used. However, as the spatial resolution of spectral images imporves, the data no longer maintain a Gaussian distribution, and some assumptions about the data - and subsequently all algorithms based upon those statistical assumptions - fail. Here we present the Simplex Volume Estimation algorithm (SVE), which avoids these potential hindrances by taking a geometrical approach. In particular, we employ the linear mixture model to approximate the convex hull enclosing the data through identification of the simplex verticles (known as endmembers). SVE begins by processing an image and tiling it into squared. Next, SVE iterates through the tiles and for each set of pixels it identifies the number of corner (as vectors) that define the simplex of that set of data. For each tile, it then iterates through the increasing dimensionality, or number of endmembers, while every time calculating the volume of the simplex that is defined by that number of endmembers. When the volume is calculated in a dimension that is higher than that of the inherent dimensionality of the data, the volume will theoretically drop to zero. This value is indicative of the inherent dimensionality of the data as represented by the convex hull. Further, the volume of the simplex will flucutate when a new material is introduced to the dataset, indicating a change in the image. The algorithm then analyzes the volume function associated with each tile and assigns the tile a metric value based on that function. the values of these metrics will be compared by using hyperspectral imagery collected from different platforms over experimental setups with known changes between flights. Results from these tests will be presented along with a path forward for future research.

关键词： change detection hyperspectral

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Orthogonal Subspace Projection Approach to Finding Signal Sources in hyperspectral imagery

Orthogonal Subspace Projection Approach to Finding Signal So...

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conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XVI

作者： Jiao, Xiaoli Chang, Chein-, I Du, Yingzi Univ Maryland Baltimore Cty Dept Comp Sci & Elect Engn 1000 Hilltop Circle Baltimore MD 21250 USA Indiana Univ Purdue Univ Purdue Sch Engn & Technol Dept Elect & Comp Engn Indianapolis IN 46202 USA

ISBN: (纸本)9780819481597

The usefulness of orthogonal subspace projection (OSP) has been demonstrated in many applications. Automatic Target Generation Process (ATGP) was previously developed for automatic target recognition for hyperspectral imagery by implementing a successive OSP. However, ATGP itself does not provide a stopping rule to determine how many signal sources present and need to be extracted in the image. This paper presents a new application of ATGP in determining the number of signal sources and finding these signal sources in the image at the same time. The idea is to categorize signal sources into target classes and background classes in terms of their inter-sample spectral correlation (ISSC). Two separate algorithms, unsupervised target sample generation (UTSG) and unsupervised background sample generation (UBSG) are developed for this purpose. The UTSG implements a sequence of successive OSP in the sphered hyperspectral data to determine the number of target signal sources whose ISSC are characterized by high order statistics (HOS) and find the target signal sources to at the same time. It is then followed by the UBSG which operates the ATGP on a space orthogonal to the subspace generated by the target samples to determine and find background signal sources. Both UTSG and UBSG are terminated by an effective stopping rule which can be used to estimate the virtual dimensionality (VD). Two data sets, synthetic image data and real image scenes are used for experiments. Experimental results demonstrate that the UTSG and UBSG are effective in extracting signal sources in various applications.

关键词： Virtual Dimensionality (VD) Orthogonal Subspace Projection (OSP) Unsupervised Target Sample Generation (UTSG) Unsupervised Background Sample Generation (UBSG)

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Vision Inspired Spatial Engine (VISE) - Automated Object Registration for Multi-Source Fusion

Vision Inspired Spatial Engine (VISE) - Automated Object Reg...

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conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XVI

作者： Lewis, Derek Edwards, Dan Hufnagel, Joseph Kim, Melissa Natl Geospatial Intelligence Agcy NGA 12310 Sunrise Valley Dr Reston VA 20191 USA

ISBN: (纸本)9780819481597

Automated registration has been pursued for decades within academia, government, and commercial sectors as a fundamental enabling technology to support improved positioning, automated change detection, target recognition, and multi-source fusion. The focus of previous and current research has largely been on automated image-to-image registration tools. Comparatively little attention has been paid to automated registration of non-raster data (e. g., vector) stored within Geographical Information Systems (GIS) or other types of databases. The Vision Inspired Spatial Engine (VISE) is an innovative approach to automated registration. Rather than focusing on automated registration of a specific data source such as imagery, VISE uses a novel object-matching paradigm which is independent of data source. VISE assesses the fuzzy spatial similarity between two or more object patterns that can be of different shape or size by use of a top-down multiple resolution approach that simultaneously optimizes both edge and area match between vector-represented spatial features. As a by-product of the VISE pattern-matching process, object-to-object and object-to-image registration between different data sources are possible. This paper demonstrates the VISE technology applied toward the automated registration and object-level correlation of hyperspectral (HSI), LIDAR and Electro-Optical (EO) imagery and derived objects, and other GIS data sources.

关键词： automated registration vector hyperspectral GIS fusion imagery

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