Implications and mitigation of model mismatch and covariance contamination for hyperspectral chemical agent detection
Name
Niu-2013-Implications and mit.pdf
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2.66 MB
Format
Adobe PDF
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Author(s)
Niu, Sidi
Golowich, Steven E.
Ingle, Vinay K.
Manolakis, Dimitris G.
Date Issued
February 2013
Journal
Optical Engineering
Publisher
SPIE
Citation
Niu, Sidi. “Implications and Mitigation of Model Mismatch and Covariance Contamination for Hyperspectral Chemical Agent Detection.” Optical Engineering 52.2 (2013): 026202.
© 2013 Society of Photo-Optical Instrumentation Engineers
Version
Final published version
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.
MIT Department
Lincoln Laboratory
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1117/1.oe.52.2.026202
