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Proceedings Paper

Nonlinear signal contamination effects for gaseous plume detection in hyperspectral imagery
Author(s): James Theiler; Bernard R. Foy; Andrew M. Fraser
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Paper Abstract

When a matched filter is used for detecting a weak target in a cluttered background (such as a gaseous plume in a hyperspectral image), it is important that the background clutter be well-characterized. A statistical characterization can be obtained from the off-plume pixels of a hyperspectral image, but if on-plume pixels are inadvertently included, then that background characterization will be contaminated. In broad area search scenarios, where detection is the central aim, it is by definition unknown which pixels in the scene are off-plume, so some contamination is inevitable. In general, the contaminated background degrades the ability of the matched-filter to detect that signal. This could be a practical problem in plume detection. A linear analysis suggests that the effect is limited, and actually vanishes in some cases. In this study, we take into account the Beer's Law nonlinearity of plume absorption, and we investigate the effect of that nonlinearity on the signal contamination.

Paper Details

Date Published: 4 May 2006
PDF: 12 pages
Proc. SPIE 6233, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII, 62331U (4 May 2006); doi: 10.1117/12.665608
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. 6233:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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