QIAN Qian1,2,3, WANG Bing-nan1,2, XIANG Mao-sheng1,2, FU Xi-kai1,2,3, JIANG Shuai1,2,3
（1. National Key Laboratory of Science and Technology on Microwave Imaging, Beijing 100190, China；2. Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China；3. University of Chinese Academy of Sciences, Beijing 100049, China）
Abstract： Building segmentation from high-resolution synthetic aperture radar (SAR) images has always been one of the important research issues. Due to the existence of speckle noise and multipath effect, the pixel values change drastically, causing the large intensity differences in pixels of building areas. Moreover, the geometric structure of buildings can cause strong scattering spots, which brings difficulties to the segmentation and extraction of buildings. To solve of these problems, this paper presents a coherence-coefficient-based Markov random field (CCMRF) approach for building segmentation from high-resolution SAR images. The method introduces the coherence coefficient of interferometric synthetic aperture radar (InSAR) into the neighborhood energy based on traditional Markov random field (MRF), which makes interferometric and spatial contextual information more fully used in SAR image segmentation. According to the Hammersley-Clifford theorem, the problem of maximum a posteriori (MAP) for image segmentation is transformed into the solution of minimizing the sum of likelihood energy and neighborhood energy. Finally, the iterative condition model (ICM) is used to find the optimal solution. The experimental results demonstrate that the proposed method can segment SAR building effectively and obtain more accurate results than the traditional MRF method and K-means clustering.
Key words： building segmentation; high-resolution synthetic aperture rader (SAR) image; Markov random field (MRF); coherence coefficient
CLD number： TN911.73; TN958 Document code： A
Article ID： 1674-8042（2019）03-0226-010 doi： 103969/jissn1674-8042201903005
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基于相干系数-马尔可夫随机场的高分辨率SAR图像建筑物分割算法千倩1,2,3，
汪丙南1,2， 向茂生1,2， 付希凯1,2,3， 蒋帅1,2,3
（1. 微波成像技术国家重点实验室， 北京 100190；2. 中国科学院电子学研究所， 北京 100190；3. 中国科学院大学, 北京 100049）
摘要： 高分辨率合成孔径雷达（Synthetic aperture radar,SAR）图像的建筑物分割问题一直是重要的研究课题之一。 由于斑点噪声和多路径效应的存在以及建筑物几何结构的影响， 建筑物区域内部会产生强散射斑点， 像素强度值的差异较大， 给建筑物分割和提取带来了困难。 针对这个问题， 本文提出了一种基于相干系数-马尔科夫随机场（coherence-coefficient-based Markov random field,CCMRF）的高分辨率SAR建筑物分割算法， 该方法将干涉合成孔径雷达（interferometric synthetic aperture radar,InSAR）的相干系数引入到传统马尔可夫随机场（Markov random field,MRF）的邻域能量中， 使得相干信息和空间上下文信息得到更充分的利用。 根据Hammersley-Clifford定理， 图像分割的最大后验（Maximum a posteriori,MAP）问题被转化为最小化似然能量和邻域能量之和的问题， 最后采用迭代条件模型（Iterative condition model, ICM）得到最优解。 实验结果表明， 该方法与传统的马尔可夫方法和K均值聚类方法相比， 可以有效地对SAR建筑物进行分割并获得更准确的结果。
关键词： 建筑物分割; 高分辨率SAR图像； 马尔可夫随机场; 相干系数
引用格式：QIAN Qian, WANG Bing-nan, XIANG Mao-sheng, et al. Coherence-coefficient-based Markov random field approach for building segmentation from high-resolution SAR images. Journal of Measurement Science and Instrumentation, 2019, 10(3)： 226-235. ［doi： 103969／jissn1674-80422019-03-005］
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