Algorithms exploiting hyperspectral imagery for target detection have continually evolved to provide improved detection results. Adaptive matched filters, which may be derived in many different fields, can be used to locate spectral targets by modeling the scene background as either structured (i.e., geometric, with endmembers or basis vectors) or unstructured (i.e., stochastic, with a mean and covariance matrix). In unstructured techniques, various methods of calculating the background covariance matrix have been developed, each involving either the removal of target signatures from the background model or the appropriate choice of a subset of the image to be used as training data based on spatial or spectral characteristics. The objective of these methods is to derive a background which matches the source of mixture interference for the detection of sub-pixel targets, or matches the source of false alarms in the scene for the detection of fully resolved targets. In addition, these techniques may increase the multivariate normality of the data from which the background is characterized, thus increasing adherence to the normality assumption inherent in the matched filter and ultimately improving target detection results. Such techniques for improved background characterization are widely practiced but not well understood or documented. This work describes in detail several methods for unstructured background characterization, both new methods and those taken from the literature. A comparison of methods across a common set of targets is presented. Training data are chosen either spatially, spectrally, or both prior to estimation of the background statistics. Evaluation of the methods indicates that consistent improvements over scene-wide statistics can be achieved through such spatial or spectral subsetting of the image data. However, results are not conclusive as to the overall "best" method for all targets in the test set.
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 endmembers (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 upon scene-wide methods have been developed, each involving either the removal of target signatures from the background model or the segmentation 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 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, and introduces a new technique which combines these methods. The analysis provides insight into the merit of employing unstructured background characterization techniques, as well as limitations for their practical application.