稀疏微波SAR图像特征分析与目标检测研究

张增辉; 郁文贤

doi:10.12000/JR15097

稀疏微波SAR图像特征分析与目标检测研究

DOI: 10.12000/JR15097

张增辉,
郁文贤^,

(上海交通大学电子信息与电气工程学院智能探测与识别上海市重点实验室上海 200240)

基金项目:

国家自然科学基金(61331015), 973课题(2010CB731904)

详细信息

作者简介:
张增辉(1980-),男,山东金乡人,博士,副研究员,分别于2001年、2003年和2008年获国防科技大学应用数学、计算数学和信息与通信工程专业学士、硕士和博士学位。2008年6月,任国防科大理学院数学与系统科学系讲师;2014年2月,任上海交通大学电子信息与电气工程学院副研究员。主要从事新体制雷达系统、雷达信号处理、压缩感知理论等方面的研究。E-mail:zenghui.zhang@sjtu.edu.cn郁文贤(1964-),男,上海松江人,博士,教授,博士生导师,上海交通大学讲席教授。中国第2代卫星导航系统重大专项测试评估与试验验证专家组专家,高分辨率对地观测系统重大专项专家委员会地面系统组专家,“十二五”总装备部卫星应用技术专业组顾问,总装备部上海市“北斗导航与位置服务”共建重点实验室主任,上海交通大学学术委员会委员,雷达信号处理国防科技重点实验室学术委员会委员,“十一五”国家863计划信息获取与处理技术主题第一、第二届专家组组长,“十一五”总装备部雷达探测技术专业组专家,主要研究方向为先进探测技术和多维信号与信息处理,研究内容包括新型成像系统、微波图像处理和解译、信息融合、目标识别等。E-mail:wxyu@sjtu.edu.cn

通讯作者:
郁文贤wxyu@sjtu.edu.cn

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出版历程
- 收稿日期: 2015-08-15
- 修回日期: 2015-10-19
- 网络出版日期: 2016-02-28

Feature Understanding and Target Detection for Sparse Microwave Synthetic Aperture Radar Images

Zhang Zenghui,
Yu Wenxian^,

Shanghai Key Laboratory of Intelligent Sensing and Recognition, School of Electronic, Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Funds:

The National Natural Science Foundation of China (61331015), The National Basic Research Program of China (2010CB731904)

摘要

摘要: 稀疏微波成像利用观测场景在空时频极化等表示域上的稀疏先验,通过线性综合测量方式获得比传统Nyquist采样低得多的回波数据,使用优化重构算法恢复观测场景微波图像,相对于传统微波成像体制具有诸多优势。在稀疏微波成像体制下,图像的获取和表征均发生了变化,需要在雷达图像理解现有理论和方法的基础上,研究新的特征分析和认知解译理论与方法。该文分析了稀疏SAR图像的统计特性以及点、线、面等特征的变化情况,对于使用空域稀疏模型重构的SAR图像,统计分布退化,适当降低采样率不影响点、线目标的提取精度。在此基础之上,研究了稀疏SAR图像海上舰船目标检测方法,得益于较弱的背景噪声,稀疏SAR图像的目标检测使用简单的阈值处理即可获得较好的检测效果。
- 稀疏表示 /
- 合成孔径雷达 /
- 压缩感知 /
- 特征提取 /
- 目标检测
Abstract: Sparse microwave imaging using sparse priors of observed scenes in space, time, frequency, or polarization domain and echo data with sampling rate smaller than the traditional Nyquist rate as well as optimization algorithms for reconstructing the microwave images of observed scenes has many advantages over traditional microwave imaging systems. In sparse microwave imaging, image acquisition and representation vary; therefore, new feature analysis and cognitive interpretation theories and methods should be developed based on current research results. In this study, we analyze the statistical properties of sparse Synthetic Aperture Radar (SAR) images and changes in point, line and regional features induced by sparse reconstruction. For SAR images recovered by the spatial sparse model, the statistical distribution degrades, whereas points and lines can be accurately extracted by low sampling rates. Furthermore, the target detection method based on sparse SAR images is studied. Owing to a weak background noise, target detection is easier using sparse SAR images than traditional ones.
- Sparse representation /
- Synthetic Aperture Radar (SAR) /
- Compressive Sensing (CS) /
- Feature extraction /
- Target detection

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