Design and Evaluation of Robust Matched Filters for Chemical Agent Detection

摘要

Most chemical gas detection algorithms for hyperspectral imaging applications 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 Beer's law. The objective of this work is to explore robust matched niters that take the uncertainty and/or variability of the target signatures into account. 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.