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Proceedings Paper

Issues in segmenting hyperspectral imagery from histograms
Author(s): Jerry Silverman; Stanley R Rotman; Karen L Duseau; Charlene E. Caefer
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Paper Abstract

In earlier work, we have shown that starting with the first two or three principal component images, one could form a two or three-dimensional histogram and cluster all pixels on the basis of the proximity to the peaks of the histogram. Here, we discuss two major issues which arise in all classification/segmentation algorithms. The first issue concerns the desired range of segmentation levels. We explore this issue by means of plots of histogram peaks versus the scaling parameter used to map into integer bins. By taking into account the role of Pmin, the minimum definition of a peak in the histogram, we demonstrate the viability of this approach. The second issue is that of devising a merit function for assessing segmentation quality. Our approach is based on statistical tests used in the Automatic Classification of Time Series (ACTS) algorithm and is shown to support and be consistent with the histogram plots.

Paper Details

Date Published: 7 January 2004
PDF: 11 pages
Proc. SPIE 5159, Imaging Spectrometry IX, (7 January 2004); doi: 10.1117/12.506763
Show Author Affiliations
Jerry Silverman, Solid State Scientific Corp. (United States)
Stanley R Rotman, Ben-Gurion Univ. of the Negev (Israel)
Karen L Duseau, Air Force Research Lab. (United States)
Charlene E. Caefer, Air Force Research Lab. (United States)

Published in SPIE Proceedings Vol. 5159:
Imaging Spectrometry IX
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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