Abstract: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.
张 强; 吴 艳. 基于上下文和隐类属的小波域马尔可夫随机场SAR图像分割[J]. 电子与信息学报, 2008, 30(1): 211-215 .
Zhang Qiang①;Wu Yan①②. Wavelet Markov Random Field Based on Context and Hidden Class Label for SAR Image Segmentation. , 2008, 30(1): 211-215 .