Share Email Print
cover

Proceedings Paper

SAR image segmentation with entropy ranking based adaptive semi-supervised spectral clustering
Author(s): Xiangrong Zhang; Jie Yang; Biao Hou; Licheng Jiao
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

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); doi: 10.1117/12.864880
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)

© SPIE. Terms of Use
Back to Top