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Proceedings Paper

Contextual unsupervised classification of remotely sensed imagery with mixels
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Paper Abstract

We propose a contextual unsupervised classification method of geostatistical data based on combination of Ward clustering method and Markov random fields (MRF). Image is clustered into classes by using not only spectrum of pixels but also spatial information. For the classification of remote sensing data of low spatial resolution, the treatment of mixed pixel is importance. From the knowledge that the most of mixed pixels locate in boundaries of land-covers, we first detect edge pixels and remove them from the image. We here introduce a new measure of spatial adjacency of the classes. Spatial adjacency is used to MRF-based update of the classes. Clustering of edge pixels are processed as final step. It is shown that the proposed method gives higher accuracy than conventional clustering method does.

Paper Details

Date Published: 29 September 2006
PDF: 9 pages
Proc. SPIE 6365, Image and Signal Processing for Remote Sensing XII, 63650R (29 September 2006); doi: 10.1117/12.689566
Show Author Affiliations
Shuji Kawaguchi, Kyushu Univ. (Japan)
Ryuei Nishii, Kyushu Univ. (Japan)

Published in SPIE Proceedings Vol. 6365:
Image and Signal Processing for Remote Sensing XII
Lorenzo Bruzzone, Editor(s)

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