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.

Date of creation, presentation, or exhibit



Copyright 2005 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.

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Document Type

Conference Paper

Department, Program, or Center

Chester F. Carlson Center for Imaging Science (COS)


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