Region-Based Classification of Polarimetric SAR Images Using Wishart MRF
Region-Based Classification of Polarimetric SAR Images Using Wishart MRF
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DOI：
10.1109/lgrs.2008.2002263
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发表时间：
2008-10-01
影响因子：
4.8
通讯作者：
Su, Yi
中科院分区：
文献类型：
--
作者：
Wu, Yonghui;Ji, Kefeng;Su, Yi
极化SAR图像中单个像元的散射测量值会受到相干斑的影响，从而影响以单个像元为单元的分类方法的性能。通过引入相邻像素之间的空间关系，提出了一种基于马尔可夫随机场（MRF）的以区域为元素的图像分类方法。该方法首先将图像过分割成大量的矩形区域。然后，为了充分利用数据的统计先验知识和相邻像素的空间关系，将Wishart分布与MRF相结合，提出了一种Wishart MRF模型，并采用迭代条件模式算法对过分割结果进行调整，使所有区域的形状更好地与地面真实值相匹配。最后，基于Wishart的最大似然，基于区域，用于获得分类图。实验中使用了真实的偏振图像。与其他三种常用的方法相比，使用该方法，观察到更高的精度，分类图与初始地面图更好地一致。
The scattering measurements of individual pixels in polarimetric SAR images are affected by speckle; hence, the performance of classification approaches, taking individual pixels as elements, would be damaged. By introducing the spatial relation between adjacent pixels, a novel classification method, taking regions as elements, is proposed using a Markov random field (MRF). In this method, an image is oversegmented into a large amount of rectangular regions first. Then, to use fully the statistical a priori knowledge of the data and the spatial relation of neighboring pixels, a Wishart MRF model, combining the Wishart distribution with the MRF, is proposed, and an iterative conditional mode algorithm is adopted to adjust oversegmentation results so that the shapes of all regions match the ground truth better. Finally, a Wishart-based maximum likelihood, based on regions, is used to obtain a classification map. Real polarimetric images are used in experiments. Compared with the other three frequently used methods, higher accuracy is observed, and classification maps are in better agreement with the initial ground maps, using the proposed method.