On the other side, effective and efficient segmentation schemes are also necessary to manage different kinds of features. Researchers have proposed many kinds of segmentation algorithms for SAR images, which include threshold methods,3,17–19 spectral clustering (SC) algorithms,20,21 statistic model-based methods,1,14,15,22–25 artificial intelligence methods,26–30 support vector machine (SVM),6,31 region growing methods,15,32–35 and so on. Among these algorithms, cluster-based algorithms form one popular and representative family, whose main idea is to group pixels in such a way that the pixels in the same group are more similar to each other than those in other groups. The key point in these algorithms is to define an objective function (or a criterion) that computes the overall similarity (or dissimilarity) of clusters (segments), which thus decides the final image segmentation. One direct solution to improve the accuracy and robustness of the objective function is to extract more information from SAR images. Such considerations have driven the emergence of a large amount of literatures5–9,20,27,29,36,37 concerning the texture classification of SAR images. Clausi9 carefully compared and integrated different texture features into the classification task of SAR ice images. Kandaswamy et al.8 proposed a statistical occupancy model to analyze the efficiency of different texture features in SAR image classification. However, so far as we know, little research has been done to combine texture and brightness together to describe SAR images, which are two different and complementary features for SAR image interpretation. This is mainly because of the different structures of texture and brightness, which will be further discussed in Sec. 2.2. Another solution for improvement of the objective function is to take advantage of some data mining technologies on the extracted features so as to accurately compute the similarity (or dissimilarity) between operation elements (e.g., pixels). Using this train of thought, Zhang et al.20 applied SC based on eigenvector decomposition to SAR image segmentation, which can recognize the clusters of unusual shapes and obtain the global optimal solutions in a relaxed continuous domain. In order to accurately characterize the structure of clusters, Yang et al.29 adopted two conflicting and complementary objective functions, and they proposed a multiobjective optimization algorithm for texture classification of SAR images. Some machine learning techniques, such as SVM,6,31,37 have also been modified for SAR image texture classification.