An Efficient SAR Image Segmentation Framework Using Transformed Nonlocal Mean and Multi-Objective Clustering in Kernel Space

Algorithms

Dongdong Yang³  Hui Yang²  Rong Fei³  Liu Chen-Chung¹  Chen Wen-Yuan¹ 

[1] id="af1-algorithms-08-00032">1Institute of Intelligent Computing and Image Processing, Mail box 666, No. 5 South JinHua Road, Xi’an University of Technology, China, Xi’an 710048, ChinaChina;Institute of Intelligent Computing and Image Processing, Mail box 666, No. 5 South JinHua Road, Xi’an University of Technology, China, Xi’an 710048, China

关键词: SAR image segmentation;    artificial immune system;    clonal selection algorithm;    nonlocal mean filter;    principal component analysis;   

DOI  :  10.3390/a8010032

来源: mdpi

【 摘 要 】

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

【 授权许可】

CC BY   
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