Zhang Qiang, Wu Yan. Wavelet Markov Random Field Based on Context and Hidden Class Label for SAR Image Segmentation[J]. Journal of Electronics & Information Technology, 2008, 30(1): 211-215. doi: 10.3724/SP.J.1146.2006.00877
Citation:
Zhang Qiang, Wu Yan. Wavelet Markov Random Field Based on Context and Hidden Class Label for SAR Image Segmentation[J]. Journal of Electronics & Information Technology, 2008, 30(1): 211-215. doi: 10.3724/SP.J.1146.2006.00877
Zhang Qiang, Wu Yan. Wavelet Markov Random Field Based on Context and Hidden Class Label for SAR Image Segmentation[J]. Journal of Electronics & Information Technology, 2008, 30(1): 211-215. doi: 10.3724/SP.J.1146.2006.00877
Citation:
Zhang Qiang, Wu Yan. Wavelet Markov Random Field Based on Context and Hidden Class Label for SAR Image Segmentation[J]. Journal of Electronics & Information Technology, 2008, 30(1): 211-215. doi: 10.3724/SP.J.1146.2006.00877
Because of the property that Synthetic Aperture Radar (SAR) images include plenty of multiplicative speckle noise, an effective image segmentation algorithm is proposed based on the wavelet hidden-class-label Markov Random Field (MRF) to suppress the affect of speckle. To consider the clustering and persistence of wavelet, the hidden-class-label MRF is extended to the wavelet domain with a new wavelet model for segmented image named hidden-class-label mixture heavy-tailed model, and interscale transition probability is estimated with improved context, then a new Maximum A Posteriori (MAP) classification is obtained. The experimental results show that the method suppresses the affect of noise effectively to achieve exact and robust segmentation result.
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