基于混合稀疏表示的二维压缩感知SAR成像  

2-D compressed sensing SAR imaging based on mixed sparse representation

作　　者：熊世超 倪嘉成[1] 张群[1,2] 罗迎[1,2] 王岩松 XIONG Shichao;NI Jiacheng;ZHANG Qun;LUO Ying;WANG Yansong(Information and Navigation College,Air Force Engineering University,Xi’an 710077,China;Key Laboratory of Wave Scattering and Remote Sensing Information,Fudan University,Shanghai 200433,China;93303 Unit of PLA,Shenyang 110000,China)

机构地区：[1]空军工程大学信息与导航学院,西安710077 [2]复旦大学波散射与遥感信息国家教育部重点实验室,上海200433 [3]中国人民解放军93303部队,沈阳110000

出　　处：《北京航空航天大学学报》2022年第11期2314-2324,共11页Journal of Beijing University of Aeronautics and Astronautics

基　　金：国家自然科学基金(62001508,61871396);陕西省自然科学基础研究计划(2020JQ-480,2020JM-348)。

摘　　要：压缩感知(CS)理论在合成孔径雷达(SAR)成像中应用广泛。针对包含城市、河流等区域的非稀疏场景压缩感知SAR成像,提出基于近似观测模型的混合稀疏表示(MSR)压缩感知SAR成像方法。该方法将复杂的SAR图像分解成点、线、面,并将线、面分别通过离散余弦变换和曲波变换转换到稀疏域,使压缩感知的稀疏性条件得以满足,通过求解基于近似观测模型的二维压缩感知优化问题重建非稀疏场景的SAR图像。所提方法能够实现降采样率条件下对包含城市、河流等非稀疏场景区域的成像,仿真场景和实测场景成像结果表明了所提方法的有效性。Compressed sensing(CS) theory has been applied in synthetic aperture radar(SAR) in the recent years.A 2-D CS SAR imaging method is proposed using mixed sparse representation(MSR) based on approximate observation model in non-sparse scene compressed sensing SAR imaging.Firstly,non-sparse scene with complicated ground features is decomposed into point-like,edges and smooth components.Then,edges and smooth components are transformed into sparse domain by discrete cosine transform and curvelet transform respectively.And based on approximate observation model,SAR images are derived from 2-D CS optimization problem.Owing to the sparse representation method of non-sparse scene,the proposed method can realize high quality SAR imaging for non-sparse scene.Compared to the existing method,the proposed method has better reconstruction quality for region containing distinct edges and lines,such as city and river.Both the simulation scene and real scene experiments demonstrate the effectiveness of the proposed method.

关 键 词：合成孔径雷达 压缩感知 稀疏表示 近似观测 曲波变换 

分 类 号：V243.2[航空宇航科学与技术—飞行器设计] TN957.5[电子电信—信号与信息处理]