期刊
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
卷 11, 期 7, 页码 1290-1294出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2013.2292820
关键词
Fuzzy C-means (FCM) clustering; synthetic aperture radar (SAR) image segmentation; wavelet decomposition; weighted fuzzy factor
类别
资金
- National Natural Science Foundation of China [61171135]
The presence of multiplicative noise in synthetic aperture radar (SAR) images makes segmentation and classification difficult to handle. Although a fuzzy C-means (FCM) algorithm and its variants (e.g., the FCM_S, the fast generalized FCM, the fuzzy local information C-means, etc.) can achieve satisfactory segmentation results and are robust to Gaussian noise, uniform noise, and salt and pepper noise, they are not adaptable to SAR image speckle. This letter presents a kernel FCM algorithm with pixel intensity and location information for SAR image segmentation. We incorporate a weighted fuzzy factor into the objective function, which considers the spatial and intensity distances of all neighboring pixels simultaneously. In addition, the energy measures of SAR image wavelet decomposition are used to represent the texture information, and a kernel metric is adopted to measure the feature similarity. The weighted fuzzy factor and the kernel distance measure are both robust to speckle. Experimental results on synthetic and real SAR images demonstrate that the proposed algorithm is effective for SAR image segmentation.
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