Jason West


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



Proceedings of Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI 5806 (2005) 1-12 Proceedings of the Defense Security Symposium. International Society of Optical Engineers. Held at Gaylord Palms Resort: Orlando, Florida: 28 March - 1 April 2005. Copyright 2005 Society of Photo-Optical Instrumentation Engineers. This paper was published in Proceedings of Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI, SPIE vol. 5806 and is made available as an electronic reprint with permission of SPIE. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. This work was funded under the Office of Naval Research Multi-disciplinary University Research Initiative "Modelbased Hyperspectral Exploitation Algorithm Development" #N00014-01-1-0867. The author would also like to thank his family for their support, without which this work would not be possible.Note: imported from RIT’s Digital Media Library running on DSpace to RIT Scholar Works in February 2014.

Document Type

Conference Proceeding

Department, Program, or Center

Chester F. Carlson Center for Imaging Science (COS)


RIT – Main Campus