Baseline distribution optimization and missing data completion in wavelet-based CS-Tomo SAR

作     者：Hui BI Jianguo LIU Bingchen ZHANG Wen HONG 

作者机构：Science and Technology on Microwave Imaging Laboratory Institute of ElectronicsChinese Academy of Sciences University of Chinese Academy of Sciences Department of Earth Science and Engineering Imperial College London 

出 版 物：《Science China(Information Sciences)》 (中国科学:信息科学(英文版))

年 卷 期：2018年第61卷第4期

页      面：164-172页

核心收录：

学科分类：080904[工学-电磁场与微波技术] 0810[工学-信息与通信工程] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by Chinese Academy of Sciences/State Administration of Foreign Experts Affairs International Partnership Program Creative Research Team and National Natural Science Foundation of China(Grant No.61571419) 

主　　题：tomographic synthetic aperture radar imaging(Tomo SAR) compressive sensing(CS) baseline distribution optimization coherence of measurement matrix 

摘      要：In this paper, we propose a coherence of measurement matrix-based baseline distribution optimization criterion, together with an L1 regularization missing data completion method for unobserved baselines(not belonging to the actual baseline distribution), to facilitate wavelet-based compressive sensingtomographic synthetic aperture radar imaging(CS-Tomo SAR) in forested areas. Using M actual baselines,we first estimate the optimal baseline distribution with N baselines(N M), including N-M unobserved baselines, via the proposed coherence criterion. We then use the geometric relationship between the actual and unobserved baseline distributions to reconstruct the transformation matrix by solving an L1 regularization problem, and calculate the unobserved baseline data using the measurements of actual baselines and the estimated transformation matrix. Finally, we exploit the wavelet-based CS technique to reconstruct the elevation via the completed data of N baselines. Compared to results obtained using only the data of actual baselines, the recovered image based on the dataset obtained by our proposed method shows higher elevation recovery accuracy and better super-resolution ability. Experimental results based on simulated and real data validated the effectiveness of the proposed method.