SAR image segmentation with entropy ranking based adaptive semi-supervised spectral clustering

Xiangrong Zhang; Jie Yang; Biao Hou; Licheng Jiao

doi:10.1117/12.864880

22 October 2010 SAR image segmentation with entropy ranking based adaptive semi-supervised spectral clustering

Xiangrong Zhang, Jie Yang, Biao Hou, Licheng Jiao

Proceedings Volume 7829, SAR Image Analysis, Modeling, and Techniques X; 78290L (2010) https://doi.org/10.1117/12.864880
Event: SPIE Remote Sensing, 2010, Toulouse, France

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.

Citation Download Citation

Xiangrong Zhang, Jie Yang, Biao Hou, and Licheng Jiao "SAR image segmentation with entropy ranking based adaptive semi-supervised spectral clustering", Proc. SPIE 7829, SAR Image Analysis, Modeling, and Techniques X, 78290L (22 October 2010); https://doi.org/10.1117/12.864880

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