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4 May 2006 Nonlinear signal contamination effects for gaseous plume detection in hyperspectral imagery
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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.
© (2006) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
James Theiler, Bernard R. Foy, and Andrew M. Fraser "Nonlinear signal contamination effects for gaseous plume detection in hyperspectral imagery", Proc. SPIE 6233, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII, 62331U (4 May 2006); https://doi.org/10.1117/12.665608
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