| 基于梯度的极化SAR图像超像素分割 |
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| 引用本文： | 王涛,殷君君,刘希韫,黄晨霞,杨健. 基于梯度的极化SAR图像超像素分割[J]. 电波科学学报, 2019, 34(6): 761-770. DOI: 10.13443/j.cjors.2019043005 |
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| 作者姓名： | 王涛 殷君君 刘希韫 黄晨霞 杨健 |
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| 作者单位： | 北京科技大学计算机与通信工程学院,北京100083;清华大学电子工程系,北京100084 |
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| 基金项目： | 国家自然科学基金61771043中央高校基本科研业务费FRF-TP-18-013A2USTB-NTUT学术合作专题TW2019010 |
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| 摘 要： | 超像素分割在图像分割领域以其优异的性能表现被广泛应用，准确性和高效性是评价分割性能的重要指标.简单线性迭代聚类（simple linear iterative clustering，SLIC）方法在光学图像上表现出了优异的性能，在极化合成孔径雷达（synthetic aperture radar，SAR）图像中也被广泛应用，然而SLIC方法中的初始化步骤不能准确地定位类中心，需要多次的迭代纠正误差.改进的分水岭方法（spatial constrained watershed，SCoW）是一种基于梯度阈值区分的简单且高效的分割方法，但是不能直接用于极化SAR图像.本文受SCoW的启发，提出一种对SLIC进行预处理的分割方法，通过横虚警（constant false alarm rate，CFAR）边缘检测器计算得到极化SAR图像的梯度信息，并将梯度信息用于初始化分割.基于两幅实测极化SAR图像，将本文提出方法与其他三种方法对比.实验表明本文方法可以减少整个算法的迭代次数，得到更加符合图像信息、贴合图像边界的分割结果.
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| 关 键 词： | 极化SAR图像 超像素分割 边缘检测 迭代聚类 分水岭 |
| 收稿时间： | 2019-04-30 |
Gradient-based hyperpixel segmentation for polarimetric SAR images |
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| Affiliation: | 1.School of Computer and Communication Engineering, University of Science and Technology, Beijing 100083, China2.Department of Electronic Engineering, Tsinghua University, Beijing 100084, China |
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| Abstract: | Superpixel segmentation has been widely used in the field of image segmentation due to its excellent performance. The simple linear iterative clustering (SLIC) method shows excellent performance in optical images and has been widely used in polarimetric SAR images. However, the initialization step of the SLIC method cannot accurately locate the class center, requiring multiple iterations to correct the errors. The improved watershed method (SCoW) is a simple and efficient segmentation method based on gradient thresholding discrimination, but it cannot be directly used in polarizing SAR images. Inspired by SCoW, in this paper, we propose a segmentation method which serves for the SLIC preprocessing step. First the edge information is detected by a CFAR edge detector, and then the edge information is used to initiate the segmentation. Experiments using real PolSAR data show that this method can reduce the number of iterations. The segmentation result is more consistent with the image boundaries in comparison with other three superpixel segmentation algorithms. |
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