安道祥,,
陈乐平,
周智敏
国防科技大学电子科学学院 长沙 410073
基金项目:湖南省自然科学基金(2020JJ5661)，国家自然科学基金(61571447)，装备预研基金(61404130304, 61404130311, 61404130309)
详细信息
作者简介:谭向程(1996–)，男，四川广安人，现为国防科技大学电子科学学院硕士生，主要研究方向为SAR图像解译
安道祥(1982–)，男，吉林东丰人，博士，现为国防科技大学电子科学学院副教授，主要研究方向为机载低频单/双站超宽带SAR成像、机载CSAR成像、视频SAR成像、重轨InSAR和SAR图像解译等
陈乐平(1988–)，男，福建福州人，博士，现为国防科技大学电子科学学院讲师，主要研究方向为高分辨率合成孔径雷达成像
周智敏(1957–)，男，河北易县人，现为国防科技大学电子科学学院教授，主要研究方向为超宽带雷达技术
通讯作者:安道祥 daoxiangan@nudt.edu.cn
责任主编：杨健 Corresponding Editor: YANG Jian中图分类号:TP701
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出版历程
收稿日期:2020-08-20
修回日期:2020-10-29
网络出版日期:2020-11-17
刊出日期:2021-06-28
A Land Bridge Extraction Method Based on Polarized Circular Synthetic Aperture Radar Images
TAN Xiangcheng,AN Daoxiang,,
CHEN Leping,
ZHOU Zhimin
College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China
Funds:The Natural Science Foundation of Hunan Province (2020JJ5661), The National Natural Science Foundationof China (61571447), The Equipment Pre-Research Foundation (61404130304, 61404130311, 61404130309)
More Information
Corresponding author:AN Daoxiang, daoxiangan@nudt.edu.cn
摘要
摘要:桥梁作为重要的人造目标，一直都是合成孔径雷达(SAR)图像解译的重要对象之一。目前针对桥梁检测问题已开展了较多研究，核心思想是：首先提取出河流水体，然后再根据河流与桥梁的位置关系检测桥梁。然而，已有的桥梁检测方法依赖于河流提取，很难实现陆上桥梁检测。因为陆上桥梁下方的背景不再是河流，而是陆地，其散射特性、形状分布与河流不同，不能采用传统的水体提取方法来检测陆地背景，进而无法利用桥梁的位置先验知识定位桥梁。针对该问题，该文提出了一种基于极化圆周SAR(CSAR)图像的陆上桥梁检测方法。首先，利用观测场景的圆周极化熵(CPE)实现疑似桥梁目标与陆地背景的分离(该实验中桥梁的CPE均值为0.4018，陆地背景的CPE均值为0.7819，两者具有明差别)；然后，根据地物目标的极化熵方差特征和桥梁尺寸特性，抑制虚假目标；最后，根据桥梁的几何特征实现陆上桥梁的准确提取。该文所提方法解决了传统桥梁检测方法需要基于河流提取结果才能实现桥梁检测的问题。机载L波段极化CSAR实测数据处理结果证明了所提方法的正确性、有效性和实用性。
关键词:极化CSAR/
桥梁检测/
陆上桥梁检测/
全方位散射特征/
圆周极化熵/
极化熵方差
Abstract:As important man-made targets, bridges have been a major focus of Synthetic Aperture Radar (SAR) image interpretation, and many researchers have developed methods for bridge detection. The core frameworks of these methods are analogical, a river is first extracted, and a water bridge is detected based on the positional relationship between the river and bridge. However, existing bridge detection methods relying on river extraction; cannot be utilized detect land bridges. This is because the background environment under a bridge is land, not river, which has different scattering characteristics and shape layouts. As such, the traditional method for extracting rivers is not suitable for extracting land background, and it is impossible to locate a bridge based on prior knowledge of its location of. To resolve this problem, in this study, we propose a land bridge detection method based on polarized Circular SAR (CSAR) images. In our proposed method, the Circular Polarization Entropy (CPE) of an observed scene is introduced to separate possible bridge targets from a land background (In our experiment, the average CPE of the bridge is 0.4018, and that of the land background is 0.7819; thus there is a clear difference between the bridge and background). False targets are removed based on the difference in the polarization entropy variance features of the bridges and other ground objects; and the size characteristics of the bridges. Finally, accurate extractions of land bridges are obtained based on the geometric characteristics of the bridges. Experimental results based on real airborne L-band polarized CSAR data verify the correctness of the theoretical analysis and effectiveness of the proposed method.
Key words:Polarization Circular SAR (CSAR)/
Bridge detection/
Land bridge detection/
Omnidirectional scattering feature/
Circular Polarization Entropy (CPE)/
Polarization entropy variance
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