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Proceedings Paper

Segmentation of hyperspectral images based on histograms of principal components
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Paper Abstract

Further refinements are presented on a simple and fast way to cluster/segment hyperspectral imagery. In earlier work, it was shown that, starting with the first 2 principal component images, one could form a 2-dimensional histogram and cluster all pixels on the basis of the proximity to the peaks. Issues that we analyzed this year are the proper weighting of the different principal components as a function of the peak shape and automatic methods based on an entropy measure to control the number of clusters and the segmentation of the data to produce the most meaningful results. Examples from both visible and infrared hyperspectral data will be shown.

Paper Details

Date Published: 8 November 2002
PDF: 8 pages
Proc. SPIE 4816, Imaging Spectrometry VIII, (8 November 2002); doi: 10.1117/12.451537
Show Author Affiliations
Jerry Silverman, Air Force Research Lab. (United States)
Stanley R. Rotman, Air Force Research Lab. (United States)
Ben-Gurion Univ. of the Negev (Israel)
Charlene E. Caefer, Air Force Research Lab. (United States)


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

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