Matched Filter Stochastic Background Characterization for Hyperspectral Target Detection

摘要

Algorithms exploiting hyperspectral imagery for target detection have continually evolved to provide improved detection results. Adaptive matched filters can be used to locate spectral targets by modeling scene background as either structured (geometric) with a set of end members (basis vectors) or as unstructured (stochastic) with a covariance or correlation matrix. These matrices are often calculated using all available pixels in a data set. In unstructured background research, various techniques for improving over the scene-wide method have been developed, each involving either the removal of target signatures from the background model or the segmenting of image data into spatial or spectral subsets. Each of these methods increase the detection signal to background ratio (SBR) and the multivariate normality (MVN) of the data from which background statistics are calculated, thus increasing separation between target and non-target species in the matched filter detection statistic and ultimately improving thresholded target detection results. Such techniques for improved background characterization are widely practiced but not well documented or compared. This paper provides a review and comparison of methods in target exclusion, spatial subsetting, and spectral pre- clustering using preliminary matched filter detection results from a larger study. The analysis provides insight into the merit of employing unstructured background characterization techniques, as well as the limitations for their practical application.