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Proceedings Paper

SAR image segmentation with entropy ranking based adaptive semi-supervised spectral clustering
Author(s): Xiangrong Zhang; Jie Yang; Biao Hou; Licheng Jiao
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Paper Abstract

Spectral clustering has become one of the most popular modern clustering algorithms in recent years. In this paper, a new algorithm named entropy ranking based adaptive semi-supervised spectral clustering for SAR image segmentation is proposed. We focus not only on finding a suitable scaling parameter but also determining automatically the cluster number with the entropy ranking theory. Also, two kinds of constrains must-link and cannot-link based semi-supervised spectral clustering is applied to gain better segmentation results. Experimental results on SAR images show that the proposed method outperforms other spectral clustering algorithms.

Paper Details

Date Published: 22 October 2010
PDF: 8 pages
Proc. SPIE 7829, SAR Image Analysis, Modeling, and Techniques X, 78290L (22 October 2010);
Show Author Affiliations
Xiangrong Zhang, Xidian Univ. (China)
Jie Yang, Xidian Univ. (China)
Biao Hou, Xidian Univ. (China)
Licheng Jiao, Xidian Univ. (China)


Published in SPIE Proceedings Vol. 7829:
SAR Image Analysis, Modeling, and Techniques X
Claudia Notarnicola, Editor(s)

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