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Proceedings Paper

Algorithms for point target detection in hyperspectral imagery
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Paper Abstract

Two techniques for detecting point targets in hyperspectral imagery are described. The first technique is based on the principal component analysis of hyperspectral data. We combine the information of the first two principal component analysis images; the result is a single image display "summary" of the data cube. The summary frame is used to define image segments. The statistics, means and variances, of each segment for the principal component images is calculated and a covariance matrix is constructed. The local pixel statistics and the segment statistics are then used to evaluate the extent to which each pixel differs from its surroundings. Point target pixels will have abnormally high values. The second technique operates on each band of the hypercube. A local anti-median of each pixel is taken and is weighted by the standard deviation of the local neighborhood. The results of each band are then combined. Results will be shown for visible, SWIR, and MWIR hyperspectral imagery.

Paper Details

Date Published: 8 November 2002
PDF: 16 pages
Proc. SPIE 4816, Imaging Spectrometry VIII, (8 November 2002); doi: 10.1117/12.451543
Show Author Affiliations
Charlene E. Caefer, Air Force Research Lab. (United States)
Stanley R. Rotman, Ben-Gurion Univ. of the Negev (Israel)
Jerry Silverman, Air Force Research Lab. (United States)
Pearl W. Yip, Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 4816:
Imaging Spectrometry VIII
Sylvia S. Shen, Editor(s)

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