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7 October 2019 Hyperspectral target detection using cluster-based probability models implemented in a generalized likelihood ratio test
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Abstract
We present an algorithm for sub-pixel target detection in hyperspectral images, considering both the common additive target model, and a replacement target model where the target’s spectrum partially replaces that of the background. We implement an LRT based decision rule, estimating the underlying distributions using cluster detection in feature subsets of a decorrelated image. We select these subsets in subspaces corresponding to sets of consecutive eigenvalues of the data’s empiric covariance. The densities are approximated using products of lower dimensional Gaussian mixture models. We utilize the estimated density functions to compute maximum likelihood estimates of the target’s relative portion of the observed pixel spectrum, and obtain a GLRT based test statistic. Performance analysis of this proposed detector shows promising results when compared to the detection capabilities of the matched filter, which is used as a benchmark.
Conference Presentation
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Itan Levin, Tomer Hershkovitz, and Stanley Rotman "Hyperspectral target detection using cluster-based probability models implemented in a generalized likelihood ratio test", Proc. SPIE 11155, Image and Signal Processing for Remote Sensing XXV, 111550L (7 October 2019); https://doi.org/10.1117/12.2532787
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