SAR image segmentation with combined Bayes' rule, GMTRJ, and EM algorithms
SAR image segmentation with combined Bayes' rule, GMTRJ, and EM algorithms
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结合贝叶斯规则、GMTRJ 和 EM 算法的 SAR 图像分割
DOI：
10.1080/2150704x.2022.2026519
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发表时间：
2022-04-03
影响因子：
2.3
通讯作者：
You，H. T.
中科院分区：
文献类型：
--
作者：
Wang，Y.;You，H. T.
摘要：本研究分析了分割过程中各个类别的作用，旨在提高合成孔径雷达（SAR）图像的分割精度。因此，通过贝叶斯规则和几何分区细分，定义了 SAR 图像所有类别的权重变量并将其合并到分割模型中。此外，还设计了广义多尝试可逆跳跃（GMTRJ）和期望最大化（EM）算法，通过模拟分割模型来获得权重变量的值和最优分割。权重变量的值越大，该类的作用越重要。所提出的方法可以自动确定类别在分割过程中的角色，并准确、快速地分割SAR图像。最后，在SAR图像上对所提出的方法和对比方法进行了测试，结果充分证明了所提出方法的有效性。
ABSTRACT This study analyses the roles of all the classes in the segmentation procedure, and aims to improve the segmentation accuracy of synthetic aperture radar (SAR) images. Accordingly, weight variables of all classes for SAR images are defined and incorporated into the segmentation model by Bayes’ rule and geometric partition tessellation. In addition, the Generalized Multiple-Try Reversible Jump (GMTRJ) and expectation maximization (EM) algorithms are designed to obtain the values of weight variables and the optimal segmentation by simulating the segmentation model. The greater the value of the weight variable, the more important the role of its class. The proposed approach can automatically determine the classes’ roles in the segmentation procedure and segment SAR images accurately and quickly. Finally, the proposed and comparison approaches are tested on SAR images, and the results fully demonstrate the effectiveness of the proposed approach.