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http://dx.doi.org/10.7780/kjrs.2006.22.2.111

A Study on the Hyperspectral Image Classification with the Iterative Self-Organizing Unsupervised Spectral Angle Classification  

Jo Hyun-Gee (School of Civil, Urban & Geosystem Engineering, Seoul National University)
Kim Dae-Sung (School of Civil, Urban & Geosystem Engineering, Seoul National University)
Yu Ki-Yun (School of Civil, Urban & Geosystem Engineering, Seoul National University)
Kim Yong-Il (School of Civil, Urban & Geosystem Engineering, Seoul National University)
Publication Information
Korean Journal of Remote Sensing / v.22, no.2, 2006 , pp. 111-121 More about this Journal
Abstract
The classification using spectral angle is a new approach based on the fact that the spectra of the same type of surface objects in RS data are approximately linearly scaled variations of one another due to atmospheric and topographic effects. There are many researches on the unsupervised classification using spectral angle recently. Nevertheless, there are only a few which consider the characteristics of Hyperspectral data. On this study, we propose the ISOMUSAC(Iterative Self-Organizing Modified Unsupervised Spectral Angle Classification) which can supplement the defects of previous unsupervised spectral angle classification. ISOMUSAC uses the Angle Division for the selection of seed points and calculates the center of clusters using spectral angle. In addition, ISOMUSAC perform the iterative merging and splitting clusters. As a result, the proposed algorithm can reduce the time of processing and generate better classification result than previous unsupervised classification algorithms by visual and quantitative analysis. For the comparison with previous unsupervised spectral angle classification by quantitative analysis, we propose Validity Index using spectral angle.
Keywords
Hyperspectral Remote Sensing; Unsupervised Classification; Spectral Angle Classification; Seed Point Selection; Validity Index;
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