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Optical Engineering

Implications and mitigation of model mismatch and covariance contamination for hyperspectral chemical agent detection
Author(s): Sidi Niu; Steven E. Golowich; Vinay K. Ingle; Dimitris G. Manolakis
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Paper Abstract

Most chemical gas detection algorithms for long-wave infrared hyperspectral images assume a gas with a perfectly known spectral signature. In practice, the chemical signature is either imperfectly measured and/or exhibits spectral variability due to temperature variations and Beers law. The performance of these detection algorithms degrades further as a result of unavoidable contamination of the background covariance by the plume signal. The objective of this work is to explore robust matched filters that take the uncertainty and/or variability of the target signatures into account and mitigate performance loss resulting from different factors. We introduce various techniques that control the selectivity of the matched filter and we evaluate their performance in standoff LWIR hyperspectral chemical gas detection applications.

Paper Details

Date Published: 4 February 2013
PDF: 20 pages
Opt. Eng. 52(2) 026202 doi: 10.1117/1.OE.52.2.026202
Published in: Optical Engineering Volume 52, Issue 2
Show Author Affiliations
Sidi Niu, Northeastern Univ. (United States)
Steven E. Golowich, MIT Lincoln Lab. (United States)
Vinay K. Ingle, Northeastern Univ. (United States)
Dimitris G. Manolakis, MIT Lincoln Lab. (United States)

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