杨子渊1,,
蒋燕妮1,,
高贵2,,
1.海军工程大学电子工程学院 武汉 430033
2.西南交通大学地球科学与环境工程学院 成都 611756
基金项目:国家自然科学基金(61771483)
详细信息
作者简介:刘涛：刘 涛(1978–)，男，山东人，博士，海军工程大学教授，博士生导师。主要研究方向为雷达极化统计理论、极化信息处理、雷达极化检测与识别、电子战系统建模与仿真等。E-mail: liutao1018@sina.com
杨子渊(1997–)，男，湖北人，海军工程大学博士研究生。主要研究方向为雷达极化信息处理、合成孔径雷达运动目标检测、新体制雷达等。E-mail: yzy_199702@sina.com
蒋燕妮(1987–)，女，湖北人，海军工程大学博士研究生。主要研究方向为雷达极化信息处理、合成孔径雷达舰船目标尾迹检测、高频雷达海态信息反演等。E-mail: asiajiang2005@163.com
高贵：高 贵(1981–)，内蒙古人，博士，西南交通大学地球科学与环境工程学院副院长，教授，博士生导师。主要研究方向为遥感信息处理、人工智能、新体制雷达系统工程研制。E-mail: dellar@126.com
通讯作者:刘涛 liutao1018@sina.com
高贵 dellar@126.com
责任主编：陈思伟 Corresponding Editor: CHEN Siwei中图分类号:TN95
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出版历程
收稿日期:2020-12-31
修回日期:2021-02-02
Review of Ship Detection in Polarimetric Synthetic Aperture Imagery (in English)
LIU Tao1,,,YANG Ziyuan1,,
JIANG Yanni1,,
GAO Gui2,,
1. School of Electronic Engineering, Naval University of Engineering, Wuhan 430033, China
2. Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China
Funds:The National Natural Science Foundation of China (61771483)
More Information
Author Bio:LIU Tao received his B.S. and Ph.D. degrees from the National University of Defense Technology (NUDT), Changsha, China, in 2001 and 2007, respectively. Since 2007, he has been with the School of Electronic Engineering, Naval University of Engineering (NUE), Wuhan, China, where he is currently a professor. His research interests include the statistical theory of radar polarization, polarization information processing, Synthetic Aperture Radar (SAR) automatic target recognition, statistical modeling of SAR image, SAR ship detection, Interferometric SAR (InSAR), SAR Ground Moving Target Indication (GMTI), and artificial intelligence
YANG Ziyuan received his B.S. degree in radar engineering from NUE, Wuhan, China, in 2019. He is currently pursuing his Ph.D. degree in information and communication engineering at NUE. His research interests include radar polarization information processing, electronic warfare system modeling, and SAR-GMTI
JIANG Yanni received her Master’s degree in radio physics from Wuhan University, Wuhan, China, in 2011. She is currently teaching, as well as studying for a Doctoral degree in communication and information systems, in NUE, Wuhan, China. Her research interests include radar polarization information processing and high-frequency radar signal processing
GAO Gui received his B.S. degree in information engineering and M.S. and Ph.D. degrees in remote sensing information processing from NUDT, Changsha, China, in 2002, 2003, and 2007, respectively. From 2017, he was with Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, China, where he is currently a professor. His current research interests include radar signal processing, InSAR, target detection, marine environment, and SAR-GMTI
Corresponding author:LIU Tao, liutao1018@sina.com;GAO Gui, dellar@126.com
摘要
摘要:极化合成孔径雷达(PolSAR)使用二维脉冲压缩技术获取高分辨力极化信息图像，目前已广泛应用在军事侦察、地形测绘、环境与自然灾害监视、海上舰船目标检测等领域。如何解决复杂海杂波的建模与参数估计、慢小目标检测、密集目标检测等问题仍然是当前PolSAR图像舰船目标检测的难点。该文归纳梳理了PolSAR图像舰船目标检测的4类主流方法：极化特征目标检测方法、慢速运动目标检测方法、舰船目标尾迹检测方法以及基于深度学习的目标检测方法等，同时给出了各类方法所存在的问题以及可能的解决方法，并预测了其未来研究重点和发展趋势。
关键词:极化SAR/
舰船目标检测/
性能对比/
发展历程/
未来趋势
Abstract:Polarimetric Synthetic Aperture Radar (PolSAR) uses two-dimensional pulse compression to obtain high-resolution images containing polarimetric information. PolSAR has been widely used in military reconnaissance, topographic mapping, environmental and natural disaster monitoring, marine ship detection, and related fields. The problems associated with sea clutter modeling and parameter estimation, slow and small target detection, dense target detection, and other issues remain a challenge in PolSAR ship detection that needs to be addressed. In this paper, four main classes of PolSAR ship detection, namely, target polarimetric characteristic detection, slow and small target detection, ship wake detection, and target detection based on deep learning, are summarized. Moreover, the possible solutions to the existing problems in each class are provided, and their future development trends, which can provide some valuable suggestions for interested researchers, are predicted.
Key words:Polarimetric SAR/
Ship detection/
Performance comparison/
Development history/
Future trend
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