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
doi: 10.3724/SP.J.1146.2006.00877
Wavelet Markov Random Field Based on Context and Hidden Class Label for SAR Image Segmentation
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摘要: 该文针对合成孔径雷达(Synthetic Aperture Radar, SAR)图像含有大量的乘性斑点噪声的特点，提出了一种小波域隐类属的马尔可夫随机场(Markov Random Field, MRF)图像分割算法来抑制噪声的影响。考虑到小波的聚集性和持续性，该算法重新构造了待分图像小波域模型以类属为隐状态的混合长拖尾模型，将隐类属的马尔可夫随机场推广到小波域上，并用改进的上下文模型估计尺度间转移概率，最后推导出了新的最大后验(Maximum A Posteriori, MAP)分割公式。仿真结果证明，该算法具有鲁棒性能够有效地抑制噪声对图像的影响，得到准确的分割结果。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.
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