Synthetic aperture radar (SAR) image segmentation usually involves two\ncrucial issues: suitable speckle noise removing technique and effective image segmentation\nmethodology. Here, an efficient SAR image segmentation method considering both of the\ntwo aspects is presented. As for the first issue, the famous nonlocal mean (NLM) filter\nis introduced in this study to suppress the multiplicative speckle noise in SAR image.\nFurthermore, to achieve a higher denoising accuracy, the local neighboring pixels in the\nsearching window are projected into a lower dimensional subspace by principal component\nanalysis (PCA). Thus, the nonlocal mean filter is implemented in the subspace. Afterwards,\na multi-objective clustering algorithm is proposed using the principals of artificial immune\nsystem (AIS) and kernel-induced distance measures. The multi-objective clustering has\nbeen shown to discover the data distribution with different characteristics and the kernel\nmethods can improve its robustness to noise and outliers. Experiments demonstrate that the\nproposed method is able to partition the SAR image robustly and accurately than the\nconventional approaches.
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