20 May 2011 Implications of model mismatch and covariance contamination on chemical detection algorithms
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Abstract
The detection of gaseous chemical plumes in long-wave infrared hyperspectral images is often accomplished with algorithms derived from linear radiance models, such as the matched filter. While such algorithms can be highly effective, deviations of the physical radiative transfer process from the idealized linear model can reduce performance. In particular, the steering vector employed in the matched filter will never exactly match the observed plume signature, the estimated background covariance matrix will often suffer some contamination by the plume signature, and the plume and background will typically be spatially correlated to some extent. In combination, these effects can be worse than they are individually. In this paper, we systematically vary these factors to study their impact on detection using a data set of synthetic plumes embedded into measured background data.
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Sidi Niu, Sidi Niu, Steven E. Golowich, Steven E. Golowich, Vinay K. Ingle, Vinay K. Ingle, Dimitris G. Manolakis, Dimitris G. Manolakis, } "Implications of model mismatch and covariance contamination on chemical detection algorithms", Proc. SPIE 8048, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII, 80481E (20 May 2011); doi: 10.1117/12.883400; https://doi.org/10.1117/12.883400
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