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Optical Engineering

Improved covariance matrices for point target detection in hyperspectral data
Author(s): Charlene E. Caefer; Jerry Silverman; O. Orthal; D. Antonelli; Y. Sharoni; Stanley R. Rotman
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Paper Abstract

Our goals in hyperspectral point target detection have been to develop a methodology for algorithm comparison and to advance point target detection algorithms through the fundamental understanding of spatial and spectral statistics. In this paper, we review our methodology as well as present new metrics. We demonstrate improved performance by making better estimates of the covariance matrix. We have found that the use of covariance matrices of statistical stationary segments in the matched-filter algorithm improves the receiver operating characteristic curves; proper segment selection for each pixel should be based on its neighboring pixels. We develop a new type of local covariance matrix, which can be implemented in principal-component space and which also shows improved performance based on our metrics. Finally, methods of fusing the segmentation approach with the local covariance matrix dramatically improve performance at low false-alarm rates while maintaining performance at higher false-alarm rates.

Paper Details

Date Published: 1 July 2008
PDF: 13 pages
Opt. Eng. 47(7) 076402 doi: 10.1117/1.2965814
Published in: Optical Engineering Volume 47, Issue 7
Show Author Affiliations
Charlene E. Caefer, Air Force Research Lab. (United States)
Jerry Silverman, Solid State Scientific Corp. (United States)
O. Orthal, Ben-Gurion Univ. of the Negev (Israel)
D. Antonelli, Ben-Gurion Univ. of the Negev (Israel)
Y. Sharoni, Ben-Gurion Univ. of the Negev (Israel)
Stanley R. Rotman, Ben-Gurion Univ. of the Negev (Israel)

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