7 January 2004 Issues in segmenting hyperspectral imagery from histograms
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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.
© (2004) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jerry Silverman, Jerry Silverman, Stanley R Rotman, Stanley R Rotman, Karen L Duseau, Karen L Duseau, Charlene E. Caefer, Charlene E. Caefer, } "Issues in segmenting hyperspectral imagery from histograms", Proc. SPIE 5159, Imaging Spectrometry IX, (7 January 2004); doi: 10.1117/12.506763; https://doi.org/10.1117/12.506763


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