A Supervised Classification Method Based on Conditional Random Fields With Multiscale Region Connection Calculus Model for SAR Image
A Supervised Classification Method Based on Conditional Random Fields With Multiscale Region Connection Calculus Model for SAR Image
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DOI：
10.1109/lgrs.2010.2089427
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发表时间：
2011-05
影响因子：
4.8
通讯作者：
Xin Su;Chu He;Qian Feng;Xinping Deng;Hong Sun
中科院分区：
文献类型：
--
作者：
Xin Su;Chu He;Qian Feng;Xinping Deng;Hong Sun
提出了一种基于多尺度区域连接演算(RCC)和条件随机场(CRF)的合成孔径雷达图像监督分类方法。该方法首先通过图像金字塔将一幅SAR图像过度分割成多个超像素；然后我们使用多尺度RCC模型来描述这些超像素之间的空间逻辑关系。为了完成这一过程，在CRF推理框架下学习和推理多尺度RCC关系。该方法采用迭代策略进行CRF推理，以获得更好的分类结果细节。我们通过在DLR ESAR图像上进行的实验来说明所提出的方法。结果表明，该算法具有较高的性能。
This letter presents a supervised classification method for synthetic aperture radar (SAR) images based on multiscale region connection calculus (RCC) and conditional random fields (CRF). Using this method, first, a SAR image is oversegmented into multisuperpixels via the image pyramid. We then use the multiscale RCC model to describe the spatial logic relationships among these superpixels. To complete the process, multiscale RCC relationships are learned and reasoned under the CRF reasoning framework. This method employs iteration strategy for CRF reasoning to get better details in the classification results as well. We illustrate the proposed method by experiments conducted on DLR ESAR image. The results reveal efficient performance.