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.