Issues in segmenting hyperspectral imagery from histograms

Jerry Silverman; Stanley R Rotman; Karen L Duseau; Charlene E. Caefer

doi:10.1117/12.506763

7 January 2004 Issues in segmenting hyperspectral imagery from histograms

Jerry Silverman, Stanley R Rotman, Karen L Duseau, Charlene E. Caefer

Proceedings Volume 5159, Imaging Spectrometry IX; (2004) https://doi.org/10.1117/12.506763
Event: Optical Science and Technology, SPIE's 48th Annual Meeting, 2003, San Diego, California, United States

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 P_min, 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.

Citation Download Citation

Jerry Silverman, Stanley R Rotman, Karen L Duseau, and Charlene E. Caefer "Issues in segmenting hyperspectral imagery from histograms", Proc. SPIE 5159, Imaging Spectrometry IX, (7 January 2004); https://doi.org/10.1117/12.506763

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