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OALib Journal期刊
ISSN: 2333-9721
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Wavelet Markov Random Field Based on Context and Hidden Class Label for SAR Image Segmentation
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

Keywords: SAR image segmentation,Multiscale segmentation,Wavelet mixture heavy-tailed model,Hidden-class-label MRF,Context model
SAR图像分割
,多尺度分割,小波域混合长拖尾模型,隐类属马尔可夫随机场,上下文模型,基于上下文,类属,小波域模型,马尔可夫随机场,图像分割算法,SAR,Image,Segmentation,Label,Class,Hidden,Context,Based,Markov,Random,Field,仿真结果,鲁棒性能,公式,Maximum,最大后验,转移概率,尺度,估计

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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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