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

作者： Sarigul, E. Alam, M. S. Univ S Alabama Dept Elect & Comp Engn Mobile AL 36688 USA

ISBN: (纸本)9780819466877

This paper presents an Independent Component Analysis (ICA) based linear unmixing algorithm for target detection application. ICA is a relatively new method that attempts to separate statistically independent sources from a mixed dataset. The developed algorithm contains two steps. In the first step, ICA based linear unmixing is used to discriminate statistically independent sources to determine end-members in a given dataset as well as their corresponding abundance images. In the second step, unmixing results are analyzed to identify abundance images that correspond to the target class. The performance of the developed algorithm has been evaluated with several real life hyperspectral image datasets.

关键词： Independent Component Analysis target detection hyperspectral imagery spectral angle mapping unmixing hyperspectral dataindependent

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A theoretical framework for hyperspectral anomaly detection using spectral and spatial a priori information

A theoretical framework for hyperspectral anomaly detection ...

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

作者： Yver, Brice Marion, Rodolphe State Atom Energy Commiss BP 12 F-91680 Bruyeres Le Chatel France

ISBN: (纸本)9780819466877

This study presents a new theoretical approach for anomaly detection using a priori information about targets. This a priori knowledge deals with the general spectral behavior and the spatial distribution of targets. In this study, we consider subpixel and isolated targets which are spectrally anomalous in one region of the spectrum but not in another. This method is totally different from matched filters which suffer from a relative sensitivity to low errors in the target spectral signature. We incorporate the spectral a priori knowledge in a new detection distance and we propose a Bayesian approach with a markovian regularization to suppress the potential targets that do not respect the spatial a priori. The interest of the method is illustrated on simulated data consisting in realistic anomalies superimposed on a real HyMap hyperspectral image.

关键词： anomaly detection hyperspectral imagery a priori knowledge maximum a posteriori estimation spectral distance spatial regularization

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Target detection in high-dimensional space using a stochastic expectation maximization algorithm

Target detection in high-dimensional space using a stochasti...

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

作者： Karakaya, M. Alam, M. S. Elbakary, M. I. Univ S Alabama Dept Elect & Comp Engn Mobile AL 36688 USA

ISBN: (纸本)9780819466877

This paper presents an object detection algorithm based on stochastic expectation-maximization (SEM) algorithm. SEM algorithm is based on the stochastic, expectation, and maximization steps to iteratively estimate the parameters of the classes in many applications including hyperspectral data cube (HDC). However, the application of SEM algorithm for classification of hyperspectral imagery becomes impractical because of the huge amount of data (e.g. 512 x 512 x 220). To avoid this problem, we proposed a preprocessing step for SEM algorithm to fast classify the data cube formulating an Object Detection algorithm based on SEM for detecting small objects in hyperspectral imagery. In the proposed preprocessing step, we utilize the exponential of Euclidian Distance for rapidly separation of data cube into a potential object of interest class and a background class. Then, SEM algorithm is employed to classify the potential object of interest class further into classes to detect the object of interest class. In the conducted experiments using real hyperspectral imagery, the results of the proposed algorithm show comparatively low false alarm rate even with a challenging scenarios.

关键词： hyperspectral imagery stochastic expectation maximization algorithm object detection Multivariate Gaussian distribution and classification

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Comparative study of state of the art algorithms for hyperspectral image analysis

Comparative study of state of the art algorithms for hypersp...

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

作者： Rivera-Borrero, Carlos Hunt, Shawn D. Univ Puerto Rico Dept Elect & Comp Engn Lab Appl Remote Sensing & Image Proc Mayaguez Campus San Juan PR 00936 USA

ISBN: (纸本)9780819466877

This work studies the end-to-end performance of hyperspectral classification and unmixing systems. Specifically, it compares widely used current state of the art algorithms with those developed at the University of Puerto Rico. These include algorithms for image enhancement, band subset selection, feature extraction, supervised and unsupervised classification, and constrained and unconstrained abundance estimation. The end to end perfortriance for different combinations of algorithms is evaluated. The classification algorithms are compared in terms of percent correct classification. This method, however, cannot be applied to abundance estimation, as the binary evaluation used for supervised and unsupervised classification is not directly applicable to unmixing performance analysis. A procedure to evaluate unmixing performance is described in this paper and tested using coregistered data acquired by various sensors at different spatial resolutions. Performance results are generally specific to the image used. In an effort to try and generalize the results, a formal description of the complexity of the images used for the evaluations is required. Techniques for image complexity analysis currently available for automatic target recognizers are included and adapted to quantify the performance of the classifiers for different image classes.

关键词： hyperspectral imagery end-to-end performance image complexity analysis

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New developments in target detection in hyperspectral imagery using spectral metrics and spectra extraction

American Society for Photogrammetry and Remote Sensing - ASP...

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American Society for Photogrammetry and Remote Sensing - ASPRS Annual conference 2007: Identifying Geospatial Solutions 2007年 2卷 604-614页

作者： Robila, Stefan Department of Computer Science Center for Imaging and Optics Montclair State University Richardson Hall 301 Upper Montclair NJ 07043 United States

ISBN: (纸本)9781604232240

An important problem in processing multispectral / hyperspectral imagery consists in the design of methods for the detection of targets. In this paper we investigate a class of detection filters based on band and spectral selection. Band selection (band screening) refers to searching for the spectral bands that would yield the largest separation between target and background. Spectral selection (spectral screening) is a technique used in reducing the multispectral / hyperspectral data to a representative subset of spectra. The subset is formed such that any two spectra in it are dissimilar and, for any spectrum in the original image cube, there is a similar spectrum in the subset. Spectral screening is performed in a sequential manner, at each step, the subset being increased with a spectrum dissimilar from all the spectra already selected. We modified the spectral screening algorithm such that at the selection step the spectrum with the largest distance from the set is selected. While not introducing additional computational complexity, the Maximum Spectral Screening (MSS) algorithm ensures that the overlap among the representatives is minimized. The detection filters were obtained as the classification projector matrices based on the spectral subset. The developed algorithms were tested on HYDICE hyperspectral data using the spectral angle and the spectral information divergence. The results indicate that regular MSS outperforms band selection followed by MSS, both classes outperforming regular spectral screening.

关键词： Set theory

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Anomaly detection in hyperspectral imagery: A comparison of methods using seasonal data

Anomaly detection in hyperspectral imagery: A comparison of ...

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

作者： Hytla, Patrick Hardie, Russell C. Eismann, Michael T. Meola, Joseph Univ Dayton Dept Elect & Comp Engn 300 Coll Pk Dr Dayton OH 45469 USA Air Force Res Lab Wright Patterson AFB OH 45433 USA

ISBN: (纸本)9780819466877

The use of hyperspectral imaging (HSI) technology to support a variety of civilian, commercial, and military remote sensing applications, is growing. The rich spectral information present in HSI allows for more accurate ground cover identification and classification than with panchromatic or multispectral imagery. One class of problems where hyperspectral images can be exploited, even when no a priori information about a particular ground cover class is available, is anomaly detection. Here spectral outliers (anomalies) are detected based on how well each hyperpixel (spectral irradiance vector for a given pixel position) fits within some background statistical model. Spectral anomalies may correspond to areas of interest in a given scene. In this work, we compare several anomaly detectors found in the literature in novel experiments. In particular, we study the performance of the anomaly detectors in detecting several man-made painted panels in a natural background using visible/near-infrared hyperspectral imagery. The data have been collected over the course of a nine month period, allowing us to test the robustness of the anomaly detectors with seasonal change. The detectors considered include the simple Gaussian anomaly detector, a Gaussian mixture model (GMM) anomaly detector, and the cluster-based anomaly detector (CBAD). We examine the effect of the number of components for the GMM and the number of clusters for the CBAD. Our preliminary results suggest that the use of a CBAD yields the best results for our data.

关键词： anomaly detection hyperspectral imagery Gaussian mixture model

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Detecting changes in scenes using autonomous smart cameras

Detecting changes in scenes using autonomous smart cameras

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

作者： Jaenisch, Holger M. Handley, James W. Hicklen, Michael L. Dtech Syst Inc POB 18924 Huntsville AL 35804 USA Axiom Corp Two piedmont Ctr Atlanta GA 30305 USA

ISBN: (纸本)9780819466877

This paper describes a novel smart camera algorithm capable of detecting important changes in scenes. These changes take the form of observed crowd dynamics, group behavior, and mounted and unmounted traffic flow. Using webcarn imagery of a football game, these cameras successfully exhibited scene understanding and detected anomalies without prior training or examples. Our algorithm and results are summarized in this paper.

关键词： smart camera webcam imagery anomaly detection scene understanding change detection crowd dynamics

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Atmospheric compensation of extreme off-nadir hyperspectral imagery from Hyperion

Atmospheric compensation of extreme off-nadir hyperspectral ...

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

作者： Adler-Golden, Steven M. Bernstein, Lawrence S. Matthew, Michael W. Sundberg, Robert L. Ratkowski, Anthony J. Spectral Sci Inc 4 Fourth Ave Burlington MA 01803 USA Air Force Res Lab Hanscom AFB MA 01731 USA

ISBN: (纸本)9780819466877

Compared to nadir viewing, off-nadir viewing of the ground from a high-altitude platform provides opportunities to increase area coverage and to reduce revisit times, although at the expense of spatial resolution. In this study, the ability to atmospherically compensate off-nadir hyperspectral imagery taken from a space platform was evaluated for a worst-case viewing geometry, using EO-1 Hyperion data collected with an off-nadir angle of 63 degrees at the sensor, corresponding to six air masses along the line of sight. Reasonable reflectance spectra were obtained using both first-principles (FLAASH) and empirical (QUAC) atmospheric-compensation methods. Some refinements to FLAASH that enable visibility retrievals with highly off-nadir imagery, and also improve accuracy in nadir viewing, were developed and are described.

关键词： Reflectivity Visibility Vegetation Aerosols Sensors Atmospheric modeling Atmospheric particles Earth observing sensors Landsat hyperspectral imaging

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Random sampling statistical analysis for adaptive target-scale-invariant hyperspectral anomaly detection

Random sampling statistical analysis for adaptive target-sca...

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

作者： Romano, Joao M. Rosario, Dalton USA Armament Res & Dev Ctr Picatinny Arsenal NJ 07806 USA USA Res Lab Adelphi MD 20783 USA

ISBN: (纸本)9780819466877

Ground to ground, sensor to object viewing perspective presents a major challenge for autonomous window based object detection, since object scales at this viewing perspective cannot be approximated. In this paper, we present a fully autonomous parallel approach to address this challenge. Using hyperspectral (HS) imagery as input, the approach features a random sampling stage, which does not require secondary information (range) about the targets;a parallel process is introduced to mitigate the inclusion by chance of target samples into clutter background classes during random sampling;and a fusion of results. The probability of sampling targets by chance within the parallel processes is modeled by the binomial distribution family, which can assist on tradeoff decisions. Since this approach relies on the effectiveness of its core algorithmic detection technique, we also propose a compact test statistic for anomaly detection, which is based on a principle of indirect comparison. This detection technique has shown to preserve meaningful detections (genuine anomalies in the scene) while significantly reducing the number of false positives (e.g. transitions of background regions). To capture the influence of parametric changes using both the binomial distribution family and actual HS imagery.. we conducted a series of rigid statistical experiments and present the results in this paper.

关键词： parallel anomaly detection hyperspectral imagery and random sampling

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Beyond the adaptive matched filter: Nonlinear detectors for weak signals in high-dimensional clutter

Beyond the adaptive matched filter: Nonlinear detectors for ...

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

作者： Theiler, James Foy, Bernard R. Fraser, Andrew M. Los Alamos Natl Lab Space & Remote Sensing Sci Grp Los Alamos NM 87545 USA Los Alamos Natl Lab Phys Chem & Appl Spect Grp Los Alamos NM USA

ISBN: (纸本)9780819466877

For known signals that are linearly superimposed on gaussian backgrounds, the linear adaptive matched filter (AMF) is well-known to be the optimal detector. The AMF has furthermore proved to be remarkably effective in a broad range of circumstances where it is not optimal, and for which the optimal detector is not linear. In these cases. nonlinear detectors are theoretically superior, but direct estimation of nonlinear detectors in high-dimensional spaces often leads to flagrant overfitting and poor out-of-sample performance. Despite this difficulty in the general case, we will describe several situations in which nonlinearity can be effectively combined with the AMF to detect weak signals. This allows improvement over AMF performance while avoiding the full force of dimensionality's curse.

关键词： matched filter hyperspectral imagery adaptive signal detection

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