Implications and mitigation of model mismatch and covariance contamination for hyperspectral chemical agent detection

Sidi Niu; Steven E. Golowich; Vinay K. Ingle; Dimitris G. Manolakis

doi:10.1117/1.OE.52.2.026202

4 February 2013 Implications and mitigation of model mismatch and covariance contamination for hyperspectral chemical agent detection

Sidi Niu, Steven E. Golowich, Vinay K. Ingle, Dimitris G. Manolakis

Author Affiliations +

Optical Engineering, Vol. 52, Issue 2, 026202 (February 2013). https://doi.org/10.1117/1.OE.52.2.026202

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.

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Sidi Niu, Steven E. Golowich, Vinay K. Ingle, and Dimitris G. Manolakis "Implications and mitigation of model mismatch and covariance contamination for hyperspectral chemical agent detection," Optical Engineering 52(2), 026202 (4 February 2013). https://doi.org/10.1117/1.OE.52.2.026202

Published: 4 February 2013

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