An Automatic SAR Image Segmentation Framework by Multi-objective Clustering and Artificial Immune Learning

Abstract

Though several algorithms inspired by theoretical immunology have been applied to the domain of pattern classification, little focus has been placed on the issues that simultaneously optimize more than one objective-functions. Here, an efficient multi-objective automatic segmentation framework (MASF) is formulated and applied to SAR image unsupervised classification. In the framework, four important issues are presented: 1) two reasonable image preprocessing techniques are discussed at the initial stage; 2)then, an efficient immune multiobjective optimization algorithm is proposed; 3) besides, a locusbased adjacency representation in individual encoding is introduced; 4) two very simple, but very efficient conflicting clustering validity indices are incorporated into the framework and simultaneously optimized. Both simulated data and real images are used to quantitatively validate its effectiveness. In addition, four other state-of-the-art image segmentation methods are employed for comparison. Experimental results show that the proposed framework is efficient and effective for SAR image segmentation.