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Proceedings Paper

Automated clustering/segmentation of hyperspectral images based on histogram thresholding
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Paper Abstract

A very simple and fast technique for clustering/segmenting hyperspectral images is described. The technique is based on the histogram of divergence images; namely, single image reductions of the hyperspectral data cube whose values reflect spectral differences. Multi-value thresholds are set from the local extrema of such a histogram. Two methods are identified for combining the information of a pair of divergence images: a dual method of combining thresholds generated from 1D histograms; and a true 2D histogram method. These histogram-based segmentations have a built-in fine to coarse clustering depending on the extent of smoothing of the histogram before determining the extrema. The technique is useful at the fine scale as a powerful single image display summary of a data cube or at the coarser scales as a quick unsupervised classification or a good starting point for an operator-controlled supervised classification. Results will be shown for visible, SWIR, and MWIR hyperspectral imagery.

Paper Details

Date Published: 17 January 2002
PDF: 11 pages
Proc. SPIE 4480, Imaging Spectrometry VII, (17 January 2002); doi: 10.1117/12.453367
Show Author Affiliations
Jerry Silverman, Air Force Research Lab. (United States)
Charlene E. Caefer, Air Force Research Lab. (United States)
Jonathan Martin Mooney, Air Force Research Lab. (United States)
Melanie M. Weeks, Air Force Research Lab. (United States)
Pearl Yip, Air Force Research Lab. (United States)

Published in SPIE Proceedings Vol. 4480:
Imaging Spectrometry VII
Michael R. Descour; Sylvia S. Shen, Editor(s)

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