一种高性能SAR图像边缘点特征匹配方法

陈天泽; 李燕

doi:10.3724/SP.J.1004.2013.02051

一种高性能SAR图像边缘点特征匹配方法

doi: 10.3724/SP.J.1004.2013.02051

陈天泽¹,
李燕²

1.
国防科学技术大学电子科学与工程学院长沙 410073;
2.
炮兵防空兵装备技术研究所北京 100012

基金项目:

国家自然科学基金（61002023）资助

详细信息

作者简介:
陈天泽国防科学技术大学讲师，博士.主要研究方向为遥感信息处理，图像特征匹配，自动目标识别和SAR 图像解译.本文通信作者.E-mail：tzchen77@126.com

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出版历程
- 收稿日期: 2012-10-30
- 修回日期: 2013-08-19
- 刊出日期: 2013-12-20

A High Performance Edge Point Feature Match Method of SAR Images

CHEN Tian-Ze¹,
LI Yan²

1.
College of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073;
2.
Artillery and Air Defense Corps Research Institute of Equipment and Technologies, Beijing 100012

Funds:

Supported by National Natural Science Foundation of China (61002023)

摘要

摘要: 针对合成孔径雷达（Synthetic aperture radar，SAR）图像特征匹配中特征提取的不稳定性和相似度优化搜索的复杂性问题，提出了一种精确高效稳健的SAR图像边缘点集匹配方法. 首先，分析了仿射变换模型在遥感图像匹配中的适应性，并对仿射变换模型进行了参数分解；其次，提出了基于方向模板的SAR图像边缘检测算子，并利用SAR图像边缘的梯度和方向特征，建立了基于像素迁移的多源SAR边缘点集相似性匹配准则，以及图像匹配的联合相似度-联合特征均方和（Square summation joint feature，SSJF）；然后，利用改进的遗传算法（Genetic algorithm，GA）来进行相似度的全局极值优化搜索，获取变换模型参数和边缘点集的对应关系；最后，从理论上分析了本文方法的性能，并利用多幅SAR图像的匹配实验以及与原有方法的对比分析，对本文方法的性能进行了验证.
- 合成孔径雷达图像匹配 /
- 仿射变换模型 /
- 参数分解 /
- 像素迁移 /
- 联合相似测度 /
- 遗传算法
Abstract: A precise, efficient and robust edge point set matching method of synthetic aperture radar (SAR) image is presented. First, the adaptability of the affine transform model used in the remote sensing image matching is analyzed, and the parameters of the affine transform model are decomposed. Next, a modified ratio of exponentially weighted averages (ROEWA) edge detector is used to get the strength and direction of each edge point with the eight directional templates, the matching similarity criterion and the joint similarity-square summation joint feature (SSJF) are constructed based on the strength and direction of the edge point in images. Then, parameters of the transform model between the matching SAR images are determined with the modified genetic algorithms (GA) which is used to obtain the global optimum extremum of the joint similarity. Finally, the performance of the method is analyzed in theory and validated with SAR images matching experiments.
- Synthetic aperture radar (SAR) images matching /
- affine transform model /
- parameters decomposition /
- pixel migration /
- joint similarity /
- genetic algorithm (GA)

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