22 October 2010 SAR image segmentation with entropy ranking based adaptive semi-supervised spectral clustering
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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.
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Xiangrong Zhang, Xiangrong Zhang, Jie Yang, Jie Yang, Biao Hou, Biao Hou, Licheng Jiao, 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); doi: 10.1117/12.864880; https://doi.org/10.1117/12.864880
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