Results 1  10
of
16
Spectral Imaging System Analytical Model for Subpixel Object Detection
 IEEE Trans. on Geosci. and Rem. Sens
, 2002
"... Abstract—Data from multispectral and hyperspectral imaging systems have been used in many applications including land cover classification, surface characterization, material identification, and spatially unresolved object detection. While these optical spectral imaging systems have provided useful ..."
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Cited by 30 (17 self)
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Abstract—Data from multispectral and hyperspectral imaging systems have been used in many applications including land cover classification, surface characterization, material identification, and spatially unresolved object detection. While these optical spectral imaging systems have provided useful data, their design and utility could be further enhanced by better understanding the sensitivities and relative roles of various system attributes; in particular, when application data product accuracy is used as a metric. To study system parameters in the context of land cover classification, an endtoend remote sensing system modeling approach was previously developed. In this paper, we extend this model to subpixel object detection applications by including a linear mixing model for an unresolved object in a background and using object detection algorithms and probability of detection ( ) versus false alarm ( ) curves to characterize performance. Validations with results obtained from airborne hyperspectral data show good agreement between model predictions and the measured data. Examples are presented which show the utility of the modeling approach in understanding the relative importance of various system parameters and the sensitivity of versus curves to changes in the system for a subpixel road detection scenario. Index Terms—Hyperspectral imaging, multispectral imaging, remote sensing system modeling, subpixel object detection.
Overview of Physical Models and Statistical Approaches for Weak Gaseous Plume Detection using Passive Infrared Hyperspectral Imagery
, 2006
"... Abstract: The performance of weak gaseous plumedetection methods in hyperspectral longwave infrared imagery depends on scenespecific conditions such at the ability to properly estimate atmospheric transmission, the accuracy of estimated chemical signatures, and background clutter. This paper revi ..."
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Cited by 6 (1 self)
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Abstract: The performance of weak gaseous plumedetection methods in hyperspectral longwave infrared imagery depends on scenespecific conditions such at the ability to properly estimate atmospheric transmission, the accuracy of estimated chemical signatures, and background clutter. This paper reviews commonlyapplied physical models in the context of weak plume identification and quantification, identifies inherent error sources as well as those introduced by making simplifying assumptions, and indicates research areas.
Characterizing Clutter in the Context of Detecting Weak Gaseous Plumes using the
 SEBASS Sensor. Los Alamos National Laboratory Restricted Release Report
, 2006
"... Abstract: Weak gaseous plume detection in hyperspectral imagery requires that background clutter consisting of a mixture of components such as water, grass, and asphalt be well characterized. The appropriate characterization depends on analysis goals. Although we almost never see clutter as a single ..."
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Cited by 5 (1 self)
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Abstract: Weak gaseous plume detection in hyperspectral imagery requires that background clutter consisting of a mixture of components such as water, grass, and asphalt be well characterized. The appropriate characterization depends on analysis goals. Although we almost never see clutter as a singlecomponent multivariate Gaussian (SCMG), alternatives such as various mixture distributions that have been proposed might not be necessary for modeling clutter in the context of plume detection when the chemical targets that could be present are known at least approximately. Our goal is to show to what extent the generalized least squares (GLS) approach applied to real data to look for evidence of known chemical targets leads to chemical concentration estimates and to chemical probability estimates (arising from repeated application of the GLS approach) that are similar to corresponding estimates arising from simulated SCMG data. In some cases, approximations to decision thresholds or confidence estimates based on assuming the clutter has a SCMG distribution will not be sufficiently accurate. Therefore, we also describe a strategy that uses a scenespecific reference distribution to estimate decision thresholds for plume detection and associated confidence measures.
ECGLRT: Detecting weak plumes in nonGaussian hyperspectral clutter using an ellipticallycontoured generalized likelihood ratio test
 Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), p. I:221
, 2008
"... We investigate the behavior of a detector for weak gaseous plumes in hyperspectral imagery that can be derived in terms of a generalized likelihood ratio test (GLRT) applied to an ellipticallycontoured (EC) model for the distribution of background clutter. Two limiting cases of this ECGLRT detect ..."
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Cited by 3 (3 self)
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We investigate the behavior of a detector for weak gaseous plumes in hyperspectral imagery that can be derived in terms of a generalized likelihood ratio test (GLRT) applied to an ellipticallycontoured (EC) model for the distribution of background clutter. Two limiting cases of this ECGLRT detector are the adaptive matched filter (AMF) and the adaptive coherence estimator (ACE). While the general ECGLRT detector does not share the specific optimality or invariance properties exhibited by these limiting cases, it provides an inbetween model that can be competitive with both of them over a broad range of scenarios. Index Terms — hyperspectral imagery, chemical plume, matched filter, generalized likelihood ratio test, nonGaussian distribution 1.
Uncorrelated versus independent ellipticallycontoured distributions for anomalous change detection in hyperspectral imagery
 Proc. SPIE 7246
"... The detection of actual changes in a pair of images is confounded by the inadvertent but pervasive di®erences that inevitably arise whenever two pictures are taken of the same scene, but at di®erent times and under di®erent conditions. These di®erences include e®ects due to illumination, calibration ..."
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Cited by 2 (2 self)
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The detection of actual changes in a pair of images is confounded by the inadvertent but pervasive di®erences that inevitably arise whenever two pictures are taken of the same scene, but at di®erent times and under di®erent conditions. These di®erences include e®ects due to illumination, calibration, misregistration, etc. If the actual changes are assumed to be rare, then one can \learn " what the pervasive di®erences are, and can identify the deviations from this pattern as the anomalous changes. A recently proposed framework for anomalous change detection recasts the problem as one of binary classi¯cation between pixel pairs in the data and pixel pairs that are independently chosen from the two images. When an ellipticallycontoured (EC) distribution is assumed for the data, then analytical expressions can be derived for the measure of anomalousness of change. However, these expression are only available for a limited class of EC distributions. By replacing independent pixel pairs with uncorrelated pixel pairs, an approximate solution can be found for a much broader class of EC distributions. The performance of this approximation is investigated analytically and empirically, and includes experiments comparing the detection of real changes in real data.
Formulation for minmax clairvoyant fusion based on monotonic recalibration of statistics,” Optical Engineering 51
, 2012
"... A formulation for minmax clairvoyant fusion (also known as continuum fusion) is developed that exploits the invariance of hypothesis testing statistics to monotonic transformations. In addition to generalizing an earlier formulation based on manipulated thresholds, the new formulation leads to effi ..."
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Cited by 1 (1 self)
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A formulation for minmax clairvoyant fusion (also known as continuum fusion) is developed that exploits the invariance of hypothesis testing statistics to monotonic transformations. In addition to generalizing an earlier formulation based on manipulated thresholds, the new formulation leads to efficient algorithms for two of the most widely advocated fusion “flavors: ” one based on combining detectors with the same false alarm rate, and one based on constant detection rate. These algorithms are used to investigate and compare the performance of different detectors for a class of problems that arises from detecting small or weak targets in hyperspectral imagery. The experiments are performed on simulated data from welldefined distributions so as to isolate the effect of different flavors of fusion from the effects of model mismatch.
Ellipsoids for anomaly detection in remote sensing imagery
 Proc. SPIE 9472
, 2015
"... For many target and anomaly detection algorithms, a key step is the estimation of a centroid (relatively easy) and a covariance matrix (somewhat harder) that characterize the background clutter. For a background that can be modeled as a multivariate Gaussian, the centroid and covariance lead to an e ..."
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Cited by 1 (1 self)
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For many target and anomaly detection algorithms, a key step is the estimation of a centroid (relatively easy) and a covariance matrix (somewhat harder) that characterize the background clutter. For a background that can be modeled as a multivariate Gaussian, the centroid and covariance lead to an explicit probability density function that can be used in likelihood ratio tests for optimal detection statistics. But ellipsoidal contours can characterize a much larger class of multivariate density function, and the ellipsoids that characterize the outer periphery of the distribution are most appropriate for detection in the low false alarm rate regime. Traditionally the sample mean and sample covariance are used to estimate ellipsoid location and shape, but these quantities are confounded both by large leverarm outliers and nonGaussian distributions within the ellipsoid of interest. This paper compares a variety of centroid and covariance estimation schemes with the aim of characterizing the periphery of the background distribution. In particular, we will consider a robust variant of the Khachiyan algorithm for minimumvolume enclosing ellipsoid. The performance of these different approaches is evaluated on multispectral and hyperspectral remote sensing imagery using coverage plots of ellipsoid volume versus false alarm rate.
Change Detection for Hyperspectral Sensing in a Transformed Lowdimensional Space
, 2010
"... Change detection in hyperspectral imagery is the process of comparing two spectral images of the same scene acquired at different times, and finding a small set of pixels that has the largest apparent spectral change. We present an approach that operates in a twodimensional space rather than in the ..."
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Cited by 1 (0 self)
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Change detection in hyperspectral imagery is the process of comparing two spectral images of the same scene acquired at different times, and finding a small set of pixels that has the largest apparent spectral change. We present an approach that operates in a twodimensional space rather than in the original highdimensional space of the images, which can be greater than 100 spectral channels. The coordinates in the 2D space are related to Mahalanobis distances for the combined (“stacked”) data and the individual hyperspectral scenes. Several previously developed change detection algorithms can be represented as straight lines in this space, including the hyperbolic anomalous change detector, based on Gaussian scene clutter, and the ECuncorrelated detector based on heavytailed (elliptically contoured) clutter. We show that adaptive machine learning methods can produce new change detectors with good performance that can avoid problems associated with the curse of dimensionality. We
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 1
"... Effect of signal contamination in matchedfilter detection of the signal on a cluttered background ..."
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Effect of signal contamination in matchedfilter detection of the signal on a cluttered background