SAR Image Segmentation Based on Closeness Degree Cut and Minimum Description Length Criterion

In this paper, a new spectral clustering algorithm is proposed for data clustering and SAR image segmentation. There are two main contributions in this paper. First, a new SAR image segmentation scheme based on the closeness degree cut (CDCut) model is proposed. The closeness degree cut is an improved form and an interpretation from fuzzy mathematics of normalized cut by taking the local information of each node into consideration. The second contribution is the development of the minimum description length criterion for determining the number of clustering in SAR image segmentation. The whole process of SAR image segmentation is composed of three steps. Firstly, the watershed algorithm is used to obtain the over-segmented image, which preserves the discontinuity characteristics of the image. Secondly, a graph is formed using each over-segmented region as a node and the spectral clustering based on the closeness degree cut is applied to the graph. Finally, the minimum description length criterion which takes into account the statistical properties of the speckle noise is used to determine the clustering number. Experimental results with simulated and real-world SAR images demonstrate that the proposed method is effective for SAR image segmentation and provides comparable or better results than the classical graph cut based methods.