基于高分三号SAR数据的城市建筑高分辨率高维成像

毕辉 金双 王潇 李勇 韩冰 洪文

毕辉, 金双, 王潇, 等. 基于高分三号SAR数据的城市建筑高分辨率高维成像[J]. 雷达学报, 2022, 11(1): 40–51. doi: 10.12000/JR21113
引用本文: 毕辉, 金双, 王潇, 等. 基于高分三号SAR数据的城市建筑高分辨率高维成像[J]. 雷达学报, 2022, 11(1): 40–51. doi: 10.12000/JR21113
BI Hui, JIN Shuang, WANG Xiao, et al. High-resolution high-dimensional imaging of urban building based on GaoFen-3 SAR data[J]. Journal of Radars, 2022, 11(1): 40–51. doi: 10.12000/JR21113
Citation: BI Hui, JIN Shuang, WANG Xiao, et al. High-resolution high-dimensional imaging of urban building based on GaoFen-3 SAR data[J]. Journal of Radars, 2022, 11(1): 40–51. doi: 10.12000/JR21113

基于高分三号SAR数据的城市建筑高分辨率高维成像

DOI: 10.12000/JR21113
基金项目: 国家自然科学基金重点国际合作研究项目(61860206013),广东省粤深联合基金重点项目(2020B151520060),国家自然科学基金(61901213, 62001216),中央高校基本科研基金(NE2020004),江苏省自然科学基金(BK20194397),航空科学基金(201920052001),南京市留学人员科技创新项目,江苏省科协青年科技人才支持项目
详细信息
    作者简介:

    毕 辉(1991–),男,籍贯山东,博士,教授,博士生导师。2017年于中国科学院大学获得博士学位,现任南京航空航天大学电子信息工程学院教授。主要研究方向为稀疏微波成像、雷达信号处理、三维四维雷达成像等

    金 双(1998–),女,籍贯辽宁,南京航空航天大学电子信息工程学院硕士研究生。主要研究方向为层析SAR成像和差分层析SAR成像

    王 潇(1991–),女,籍贯江苏,博士,讲师,硕士生导师。2019年于复旦大学获得博士学位,现任南京工业大学计算机科学与技术学院讲师。主要研究方向为极化、干涉、层析合成孔径雷达三维成像、信息提取与参数反演以及电磁目标智能感知与识别等

    李 勇(1977–),男,籍贯河南,博士,副教授,硕士生导师。2005年于南京航空航天大学获得博士学位,现任南京航空航天大学电子信息工程学院副教授。主要研究方向为雷达信号处理、SAR/ISAR成像算法、动目标检测、新体制雷达、电子对抗等

    韩 冰(1980–),女,籍贯北京,博士,研究员,博士生导师。2008年于中国科学院电子学研究所获得博士学位,现任中国科学院空天信息创新研究院研究员。主要研究方向为先进体制星载SAR高精度成像处理、SAR海洋遥感应用、多源遥感数据智能分析等

    洪 文(1968–),女,籍贯上海,博士,研究员,博士生导师。1997年于北京航空航天大学获得博士学位,现任中国科学院空天信息创新研究院研究员。主要研究方向为合成孔径雷达成像与系统及其应用、极化/极化干涉合成孔径雷达数据处理及应用、三维微波成像新概念新体制新方法等

    通讯作者:

    毕辉 bihui@nuaa.edu.cn

  • 责任主编:孙进平 Corresponding Editor: SUN Jinping
  • 中图分类号: TN959

High-resolution High-dimensional Imaging of Urban Building Based on GaoFen-3 SAR Data(in English)

Funds: National Natural Science Foundation Key International Cooperation Research Project (61860206013), Guangdong Basic and Applied Basic Research Foundation (2020B1515120060), National Natural Science Foundation of China (61901213, 62001216), Fundamental Research Funds for the Central Universities (NE2020004), Natural Science Foundation of Jiangsu Province (BK20194397), Aeronautical Science Foundation of China (201920052001), Science and Technology Innovation Project for Overseas Researchers in Nanjing, Young Science and Technology Talent Support Project of Jiangsu Science and Technology Association
More Information
  • 摘要: 传统合成孔径雷达(SAR)只能获取方位-距离二维图像,无法准确反映目标的三维散射结构信息。层析合成孔径雷达(TomoSAR)是一种多基线干涉测量模式,它将合成孔径原理扩展至高程向,除了可对目标进行二维成像之外,还可以准确恢复目标的高度向散射信息,真正实现三维成像。差分层析合成孔径雷达(D-TomoSAR)将合成孔径原理延伸至高程和时间方向,不仅可以获得目标的三维散射结构,还可以高精度获取观测目标的形变速率,实现对目标形变的有效监测。高分三号是我国首颗1 m分辨率C频段多极化SAR卫星。它具有高分辨率、大成像幅宽、多成像模式等特点,对我国高分对地观测技术的发展具有重要意义。目前高分三号数据主要应用于目标识别等图像处理领域,没有充分利用SAR图像的相位信息。而且,由于设计之初未考虑后续高维成像应用,现有高分三号获取的SAR图像存在有一定的空间、时间去相干问题,对应用于后续干涉系列处理产生了一定影响。为解决上述问题,该文基于7景高分三号SAR复图像,开展了对北京雁栖湖周围建筑的三维、四维层析成像研究,在获取了建筑物三维散射结构信息的同时,实现了对建筑物形变的毫米级高精度监测。该初步实验结果证明了高分三号SAR数据的应用潜力,为后续进一步扩展高分三号SAR卫星在城市感知与监测中的应用提供了技术支撑。

     

  • 图  1  TomoSAR成像几何

    Figure  1.  TomoSAR imaging geometry

    图  2  高分三号数据集时空基线分布图

    Figure  2.  Spatial-temporal baseline distribution of GF-3 dataset

    图  3  高程向两个散射点的TomoSAR成像结果(左图:两个散射点之间的距离为11 m;右图:两个散射点之间的距离为50 m)

    Figure  3.  TomoSAR reconstructed reflectivity profiles of two scattering points along the elevation direction (left image: the distance between two scattering points is 11 m; right image: the distance between two scattering points is 50 m)

    图  4  高程向3个散射点的TomoSAR成像结果(3个散射点之间的间隔为20 m)

    Figure  4.  TomoSAR reconstructed reflectivity profiles of three scattering points along the elevation direction (the distance between three scattering points is 20 m)

    图  5  D-TomoSAR仿真结果(两个散射体高程位置为–10 m, 10 m;散射体形变速率分别为4毫米/年、–7毫米/年)

    Figure  5.  D-TomoSAR simulation results (elevation position of two scatters are –10 m and 10 m; deformation velocity of two scatters are 4 mm/year and –7 mm/year, respectively)

    图  6  生态农业公司

    Figure  6.  Ecological agricultural company

    图  7  生态农业公司高程图及形变速率图

    Figure  7.  Elevation and deformation velocity maps of ecological agricultural company

    图  8  生态农业公司三维点云图

    Figure  8.  3-D point cloud of ecological agricultural company

    图  9  北京雁栖湖国际会展中心

    Figure  9.  Beijing Yanqi lake international convention and exhibition center

    图  10  北京雁栖湖国际会展中心高程图及形变速率图

    Figure  10.  Elevation and deformation velocity maps of Beijing Yanqi lake international convention and exhibition center

    图  11  北京雁栖湖国际会展中心三维点云图

    Figure  11.  3-D point cloud of Beijing Yanqi lake international convention and exhibition center

    图  12  顶秀美泉小镇区域

    Figure  12.  Dingxiumeiquan town

    图  13  顶秀美泉小镇区域的高程图及形变速率图

    Figure  13.  Elevation and deformation velocity maps of Dingxiumeiquan town

    图  1  TomoSAR imaging geometry

    图  2  Spatio-temporal baseline distribution of the GF-3 dataset

    图  3  TomoSAR reconstructed reflectivity profiles of two scattering points along the elevation direction (left image: the distance between two scatterers is 11 m; right image: the distance between two scatters is 50 m)

    图  4  TomoSAR reconstructed reflectivity profiles of three scattering points along the elevation direction (the distance between three scatterers is 20 m)

    图  5  D-TomoSAR simulation results (elevation position of two scatterers are –10 m and 10 m; deformation velocity of two scatterers are 4 mm/year and –7 mm/year, respectively)

    图  6  The Haihuayundu Eco-agriculture Ltd

    图  7  Elevation and deformation velocity maps of the Haihuayundu Eco-agriculture Ltd

    图  8  3-D point cloud of the Haihuayundu Eco-agriculture Ltd

    图  9  Beijing Yanqi Lake International Convention and Exhibition Center

    图  10  Elevation and deformation velocity maps of Beijing Yanqi Lake International Convention and Exhibition Center

    图  11  3-D point cloud of Beijing Yanqi Lake International Convention and Exhibition Center

    图  12  The Dingxiumeiquan Town

    图  13  Elevation and deformation velocity maps of the Dingxiumeiquan Town

    表  1  高分三号数据集参数

    Table  1.   Parameters of GF-3 dataset

    参数名称 数值 参数名称 数值
    空间基线跨度 1417.4 m 数据景数 7景
    时间基线跨度 464 d 方位向分辨率 0.3626 m
    斜距 1052747 m 距离向分辨率 0.765692 m
    波长 0.056 m 高程向理论分辨率 20.6174 m
    入射角 47.2330015° 形变理论分辨率 21.8毫米/年
    下载: 导出CSV

    表  2  高分三号数据集时空基线参数

    Table  2.   Spatial-temporal baseline parameters of GF-3 dataset

    编号 获取时间 空间基线(m) 时间基线(d)
    1 2018.06.13 –459.108 –261
    2 2019.01.31 –628.551 –29
    3 2019.03.01 0 0
    4 2019.03.30 –724.517 29
    5 2019.07.24 692.863 145
    6 2019.08.22 –38.211 174
    7 2019.09.20 –510.491 203
    下载: 导出CSV

    表  1  Parameters of the GF-3 dataset

    Parameter Value Parameter Value
    Spatial baseline span 1417.4 m Number of scenes 7
    Temporal baseline span 464 d Azimuth resolution 0.3626 m
    Slant range 1052747 m Range resolution 0.765692 m
    Wavelength 0.056 m Elevation resolution 20.6174 m
    Incident angle 47.2330015° Information resolution 21.8 mm/year
    下载: 导出CSV

    表  2  Spatio-temporal baseline parameters of the GF-3dataset

    Number Time of acquisition Spatial baseline (m) Temporal baseline (d)
    1 2018.06.13 –459.108 –261
    2 2019.01.31 –628.551 –29
    3 2019.03.01 0 0
    4 2019.03.30 –724.517 29
    5 2019.07.24 692.863 145
    6 2019.08.22 –38.211 174
    7 2019.09.20 –510.491 203
    下载: 导出CSV
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  • 收稿日期:  2021-08-22
  • 修回日期:  2021-09-15
  • 网络出版日期:  2021-09-30
  • 刊出日期:  2022-02-28

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