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Algorithms 2015, 8(1), 32-45; doi:10.3390/a8010032

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

1,* , 2,† and 1,†
1
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
2
The Fourth Engineering Design and Research Institute of Engineer Corps, China PLA General Political Department, Beijing 100850, China
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editors: Liu Chen-Chung, Chen Wen-Yuan and Chen Ruey-Maw
Received: 28 November 2014 / Revised: 13 January 2015 / Accepted: 30 January 2015 / Published: 9 February 2015
(This article belongs to the Special Issue Advanced Data Processing Algorithms in Engineering)
View Full-Text   |   Download PDF [1019 KB, uploaded 9 February 2015]   |  

Abstract

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. View Full-Text
Keywords: SAR image segmentation; artificial immune system; clonal selection algorithm; nonlocal mean filter; principal component analysis SAR image segmentation; artificial immune system; clonal selection algorithm; nonlocal mean filter; principal component analysis
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Yang, D.; Yang, H.; Fei, R. An Efficient SAR Image Segmentation Framework Using Transformed Nonlocal Mean and Multi-Objective Clustering in Kernel Space. Algorithms 2015, 8, 32-45.

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