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Proceedings Paper

Characterizing non-Gaussian clutter and detecting weak gaseous plumes in hyperspectral imagery
Author(s): James Theiler; Bernard R. Foy; Andrew M. Fraser
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Paper Abstract

To detect weak signals on cluttered backgrounds in high dimensional spaces (such as gaseous plumes in hyperspectral imagery) without excessive false alarms requires that the background clutter be effectively characterized. If the clutter is Gaussian, the well-known linear matched filter optimizes the sensitivity to a given plume signal while suppressing the effect of the background clutter. In practice, the background clutter is rarely Gaussian. Here we illustrate non-linear corrections to the matched filter that are optimal for two non-Gaussian clutter models and we report on parametric and nonparametric characterizations of background clutter.

Paper Details

Date Published: 1 June 2005
PDF: 12 pages
Proc. SPIE 5806, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI, (1 June 2005); doi: 10.1117/12.604075
Show Author Affiliations
James Theiler, Los Alamos National Lab. (United States)
Bernard R. Foy, Los Alamos National Lab. (United States)
Andrew M. Fraser, Los Alamos National Lab. (United States)


Published in SPIE Proceedings Vol. 5806:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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