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.

We propose an algorithm for standoff quantification of chemical vapor plumes from hyperspectral imagery. The approach is based on the observation that the quantification problem can be easily solved in each pixel with the use of just a single spectral band if the radiance of the pixel in the absence of the plume is known. This plume-absent radiance may, in turn, be recovered from the radiance of the subset of spectral bands in which the gas species is transparent. This "selected-band" algorithm is most effective when applied to gases with narrow spectral features, and are therefore transparent over many bands. We also demonstrate an iterative version that expands the range of applicability. Simulations show that the new algorithm attains the accuracy of existing nonlinear algorithms, while its computational efficiency is comparable to that of linear algorithms.

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