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Proceedings Paper

Overview of algorithms for hyperspectral target detection: theory and practice
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Paper Abstract

The purpose of this paper is to provide an overview of the most useful practical algorithms for the detection of targets with known spectral signatures and anomaly detection. First, we provide an overview of adaptive matched filter and anomaly detectors, including their key theoretical assumptions, design parameters, and computational complexity. The emphasis is on the basic ideas that underline the operation of the different algorithms and the geometrical or statistical concepts explaining their performance limitations. Second, we investigate how effectively the signal models used for the development of detection algorithms characterize the HYDICE data. The accurate modeling of the background is crucial for the development of constant false alarm rate (CFAR) detectors. Third, we look at some practical considerations and how they affect the performance of the various algorithms. Finally, we compare the different algorithms with regard to the following two desirable performance properties: capacity to operate in CFAR mode and target visibility enhancement. Since most of these issues are covered more comprehensively in a special issue of IEEE Signal Processing Magazine on Exploiting Hyperspectral Imagery (January 2002), we limit the coverage of this paper to a conceptual framework and a highlight of some experimental results.

Paper Details

Date Published: 2 August 2002
PDF: 14 pages
Proc. SPIE 4725, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery VIII, (2 August 2002); doi: 10.1117/12.478752
Show Author Affiliations
Dimitris G. Manolakis, MIT Lincoln Lab. (United States)

Published in SPIE Proceedings Vol. 4725:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery VIII
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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