基于稀疏和低秩结构的层析SAR成像方法

赵曜 许俊聪 全相印 崔莉 张柘

赵曜, 许俊聪, 全相印, 等. 基于稀疏和低秩结构的层析SAR成像方法[J]. 雷达学报, 2022, 11(1): 52–61. doi: 10.12000/JR21210
引用本文: 赵曜, 许俊聪, 全相印, 等. 基于稀疏和低秩结构的层析SAR成像方法[J]. 雷达学报, 2022, 11(1): 52–61. doi: 10.12000/JR21210
ZHAO Yao, XU Juncong, QUAN Xiangyin, et al. Tomographic SAR imaging method based on sparse and low-rank structures[J]. Journal of Radars, 2022, 11(1): 52–61. doi: 10.12000/JR21210
Citation: ZHAO Yao, XU Juncong, QUAN Xiangyin, et al. Tomographic SAR imaging method based on sparse and low-rank structures[J]. Journal of Radars, 2022, 11(1): 52–61. doi: 10.12000/JR21210

基于稀疏和低秩结构的层析SAR成像方法

DOI: 10.12000/JR21210
基金项目: 国家自然科学基金(61907008, 61991421, 61991420),广东省自然科学基金(2021A1515012009),中科院空天院科学与颠覆性先导基金“结构信号的自适应高效感知理论及在微波成像中的应用”
详细信息
    作者简介:

    赵 曜(1984–),男,江西人,高级工程师,硕士生导师。主要研究方向为信号与信息处理、稀疏微波成像算法

    许俊聪(1999–),男,广东人,硕士研究生。主要研究方向为层析SAR成像

    全相印(1989–),男,吉林人,工程师。主要研究方向为稀疏微波成像、先进微波探测理论与应用等

    崔 莉(1978–),女,安徽人,工程师。主要研究方向为遥感信息处理、遥感卫星任务控制、资源调度

    张 柘(1988–),男,陕西人,博士,中国科学院空天信息研究院、苏州空天信息研究院副研究员,硕士生导师。主要研究方向为稀疏信号处理与合成孔径雷达成像

    通讯作者:

    张柘 zhangzhe01@aircas.ac.cn

  • 责任主编:王彦平 Corresponding Editor: WANG Yanping
  • 中图分类号: TN957.52

Tomographic SAR Imaging Method Based on Sparse and Low-rank Structures

Funds: The National Natural Science Foundation of China (61907008, 61991421, 61991420), The Natural Science Foundation of Guangdong Province (2021A1515012009), AIRCAS grant “Structural sparsity signal high performance adaptive sensing theory and its applications in microwave imaging”
More Information
  • 摘要: 该文提出了一种基于稀疏和低秩结构的层析SAR三维成像方法。传统基于压缩感知的层析SAR成像方法仅仅对给定方位-距离单元的高程向进行稀疏表征和重建。考虑城市和森林等区域中各自的布局分布较为类似,目标在相邻方位-距离单元的高程向分布具有较强相关性。该方法通过引入Karhunen Loeve变换来表征相邻方位-距离单元的高程向的低秩结构特性,构建稀疏和低秩结构相结合的目标区域层析SAR成像模型,采用ADMM算法对层析SAR成像模型进行求解,将复杂的原优化问题分解为若干相对简单的子问题,通过优化变量交替投影的方式进行算法求解,得到层析SAR成像结果。该方法提高了低航过数或低通道数情况下的重建精度,拥有更好的成像性能。仿真和实测数据实验表明,该重建方法能够有效分离散射体并保证重建能量的精度,且在降低航过数或通道数的情况下保持良好的成像效果,有效抑制伪影现象。

     

  • 图  1  算法流程图

    Figure  1.  Algorithm flowchart

    图  2  高程向归一化能量分布

    Figure  2.  Normalized energy distribution in the elevation direction

    图  3  F-SAR观测场景的光学图像

    Figure  3.  Optical image of F-SAR measured scene

    图  4  峨眉区域的图像

    Figure  4.  Image of Emei area

    图  5  航过数为9时各算法获得的高程向后向散射能量分布与仿真结果的对比

    Figure  5.  Comparison of the simulation result of backscattered energy distribution of the elevation obtained by each algorithm with 9 interferograms

    图  6  航过数为6时各算法获得的高程向后向散射能量分布与仿真结果的对比

    Figure  6.  Comparison of the simulation result of backscattered energy distribution of the elevation obtained by each algorithm with 6 interferograms

    图  7  航过数为9时不同信噪比情况下各算法的均方误差

    Figure  7.  Mean square error of each algorithm at different SNR values with 9 interferograms

    图  8  航过数为6时不同信噪比情况下各算法的均方误差

    Figure  8.  Mean square error of each algorithm at different SNR values with 6 interferograms

    图  9  航过数为9时各算法获得的森林区域重建结果

    Figure  9.  Reconstruction results of the forest area by each algorithm with 9 interferograms

    图  10  航过数为6时各算法获得的森林区域重建结果

    Figure  10.  Reconstruction results of the forest area by each algorithm with 6 interferograms

    图  11  通道数为12时各算法的峨眉区域重建结果

    Figure  11.  Reconstruction results of Emei area by each algorithm with 12 channels

    图  12  通道数为8时各算法的峨眉区域重建结果

    Figure  12.  Reconstruction results of Emei area by each algorithm with 8 channels

    图  13  各方法成像结果的距离向切片对比

    Figure  13.  Comparison of range slices of imaging results of each method

    表  1  Ku波段雷达系统参数

    Table  1.   Ku-band radar system parameters

    参数数值
    载波频率(GHz)
    通道数N
    成像场景海拔(m)
    图像最近斜距(m)
    航线海拔(m)
    距离向像素尺寸(m)
    方位向像素尺寸(m)
    基线长度(m)
    14.5
    12
    420
    1861
    2157
    0.1362
    0.1051
    2
    下载: 导出CSV

    表  2  各方法耗时对比

    Table  2.   Time comparison of each method

    方法耗时(s)
    CAPON61.844378
    CS43386.651744
    本文方法72769.757387
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-12-28
  • 修回日期:  2022-01-28
  • 网络出版日期:  2022-02-23
  • 刊出日期:  2022-02-28

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