Characterizing non-Gaussian clutter and detecting weak gaseous plumes in hyperspectral imagery

James Theiler; Bernard R. Foy; Andrew M. Fraser

doi:10.1117/12.604075

1 June 2005 Characterizing non-Gaussian clutter and detecting weak gaseous plumes in hyperspectral imagery

James Theiler, Bernard R. Foy, Andrew M. Fraser

Proceedings Volume 5806, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI; (2005) https://doi.org/10.1117/12.604075
Event: Defense and Security, 2005, Orlando, Florida, United States

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.

Citation Download Citation

James Theiler, Bernard R. Foy, and Andrew M. Fraser "Characterizing non-Gaussian clutter and detecting weak gaseous plumes in hyperspectral imagery", Proc. SPIE 5806, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI, (1 June 2005); https://doi.org/10.1117/12.604075

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