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This paper contributes two novel techniques in the context of synthetic aperture radar (SAR) image segmentation by cooperative learning and multi-objective clustering in kernel mapping thereof. First, we introduce an efficient implementation of cooperative evolution by using two parallel implemented populations, which are divided by the Pareto domination and local density dynamic information. Second, in order to obtain the better performance of algorithm in suppressing speckle noise in SAR image, another novelty of the study is introducing the kernel distance measure to the two objective functions. Finally, the proposed algorithm is tested on two complicated SAR images. Compared with four other state-of-the-art algorithms and our method achieve comparable results in terms of convergence, diversity metrics, and computational time.

SAR Image Segmentation by Cooperative Populations and Multi-objective Kernel Clustering Indices