基于地形辅助的无人机载InSAR图像分区配准方法

谢鑫 邓云开 杨志军 田卫明

谢鑫, 邓云开, 杨志军, 等. 基于地形辅助的无人机载InSAR图像分区配准方法[J]. 雷达学报(中英文), 2024, 13(1): 116–133. doi: 10.12000/JR23182
引用本文: 谢鑫, 邓云开, 杨志军, 等. 基于地形辅助的无人机载InSAR图像分区配准方法[J]. 雷达学报(中英文), 2024, 13(1): 116–133. doi: 10.12000/JR23182
XIE Xin, DENG Yunkai, YANG Zhijun, et al. Topography-assisted UAV InSAR image registration method with image partition[J]. Journal of Radars, 2024, 13(1): 116–133. doi: 10.12000/JR23182
Citation: XIE Xin, DENG Yunkai, YANG Zhijun, et al. Topography-assisted UAV InSAR image registration method with image partition[J]. Journal of Radars, 2024, 13(1): 116–133. doi: 10.12000/JR23182

基于地形辅助的无人机载InSAR图像分区配准方法

doi: 10.12000/JR23182
基金项目: 国家重点研发计划(2021YFC3001903),国家自然科学基金(62101036, 61971037),北京理工大学青年教师学术启动计划
详细信息
    作者简介:

    谢 鑫,博士生,主要研究方向为SAR成像与干涉测量技术

    邓云开,博士,预聘助理教授,硕士生导师,主要研究方向为SAR成像与差分干涉测量技术

    杨志军,博士,主要研究方向为SAR/ISAR 成像技术

    田卫明,教授,博士生导师,主要研究方向为SAR系统设计、雷达实时信号处理和差分干涉雷达技术

    通讯作者:

    邓云开 yunkai_bit@bit.edu.cn

  • 责任主编:陈尔学 Corresponding Editor: CHEN Erxue
  • 中图分类号: TN959.3

Topography-assisted UAV InSAR Image Registration Method with Image Partition

Funds: The National Key Research and Development Project (2021YFC3001903), The National Natural Science Foundation of China (62101036, 61971037), Beijing Institute of Technology Research Fund Program for Young Scholars
More Information
  • 摘要: 小型化、轻量化的无人机(UAV)为合成孔径雷达(SAR)提供了更加灵活、机动的观测平台,无人机载干涉SAR (InSAR)逐步应用于干涉测量领域。无人机小而轻,易受气流扰动的影响,采用多航过模式进行干涉测量时,飞行航迹非线性且不平行。非线性、不平行的飞行轨迹导致两幅图像之间存在几何畸变。在复杂地形条件下,无人机载InSAR的干涉图像对之间的偏移量大且具有明显的空变特性,给图像配准带来了很大的技术挑战,常规的基于多项式拟合的配准方法不再适用。该文提出了一种利用地形辅助分区的图像配准方法。首先基于航迹信息生成高程门限,利用外部地形对测量区域进行图像分区处理,然后对区域内的偏移量构建多项式变换模型,对各区域边界处的偏移量施加约束,并进行联合求解,最后获得连续的全局偏移量拟合面,通过对辅图像进行重采样实现精配准。基于P波段无人机载InSAR获取的实测数据,初步验证了该方法的有效性。

     

  • 图  1  常规线性孔径SAR成像几何

    Figure  1.  Conventional linear aperture SAR imaging geometry

    图  2  无人机载SAR成像几何

    Figure  2.  UAV SAR imaging geometry

    图  3  实测航迹运动分量

    Figure  3.  Motion component of the actual trajectory

    图  4  仿真地形高度

    Figure  4.  Simulation topography elevation

    图  5  迭代解与真实投影点位置误差

    Figure  5.  Error between the iteration solution and the real projection point

    图  6  迭代解与初始解位置误差

    Figure  6.  Error between the iterative solution and initial solution

    图  7  带运动误差的投影几何

    Figure  7.  Projection geometry with motion error

    图  8  不同最大运动误差和目标地距下最大斜距误差与目标高程之间的关系

    Figure  8.  Relationship between the maximum slant range error and the target height with different the maximum motion error and the target ground distance

    图  9  无人机载InSAR干涉测量投影几何

    Figure  9.  UAV InSAR interferometric projection geometry

    图  10  匹配窗和搜索窗示意图

    Figure  10.  Schematic diagram of match window and search window

    图  11  基于地形辅助的无人机载InSAR图像分区配准方法流程图

    Figure  11.  Flowchart of topography-assisted UAV InSAR registration method with image partition

    图  12  P波段无人机载SAR原理样机

    Figure  12.  P-band UAV SAR system

    图  13  金海湖机场实验场景

    Figure  13.  Jinhai Lake airport experiment scene

    图  14  金海湖场景两次航过航迹运动分量

    Figure  14.  Motion components of two trajectories in Jinhai Lake

    图  15  金海湖场景SRTM-DEM结果

    Figure  15.  DEM results from SRTM of Jinhai Lake

    图  16  金海湖场景成像结果

    Figure  16.  The imaging result of Jinhai Lake

    图  17  金海湖场景12 m基线配准前相干系数图和干涉相位图

    Figure  17.  Coherence coefficient diagram and interferogram with 12 m baseline before registration of Jinhai Lake

    图  18  金海湖场景高程门限

    Figure  18.  Threshold results of Jinhai Lake

    图  19  金海湖场景图像分区结果

    Figure  19.  The segmentation result of Jinhai Lake

    图  20  金海湖场景地形分区结果

    Figure  20.  Comparison between the segmentation results of Jinhai Lake

    图  21  金海湖场景本文所提方法的精配准结果

    Figure  21.  Fine registration results of the proposed method of Jinhai Lake

    图  22  金海湖场景常见InSAR方法配准后的相干系数图和干涉相位图

    Figure  22.  Coherence coefficient diagrams and interferograms after registration of common InSAR methods of Jinhai Lake

    图  23  金海湖场景不同方法相干系数分布曲线

    Figure  23.  Coherence coefficient distribution with different methods of Jinhai Lake

    图  24  所提方法反演的地形结果

    Figure  24.  Topography result of the proposed method

    图  25  老林沟实验场景

    Figure  25.  Laolin Gou experiment scene

    图  26  老林沟场景两次航过航迹运动分量

    Figure  26.  Motion components of two trajectories in Laolin Gou

    图  27  老林沟场景SRTM-DEM结果

    Figure  27.  DEM results from SRTM of Laolin Gou

    图  28  老林沟场景成像结果

    Figure  28.  The imaging result of Laolin Gou

    图  29  老林沟场景13 m基线配准前相干系数图和干涉相位图

    Figure  29.  Coherence coefficient diagram and interferogram with 13 m baseline before registration of Laolin Gou

    图  30  老林沟场景高程门限

    Figure  30.  Threshold results of Laolin Gou

    图  31  老林沟场景图像分区结果

    Figure  31.  Segmentation result of Laolin Gou

    图  32  老林沟场景本文所提方法的精配准结果

    Figure  32.  Fine registration results of the proposed method of Laolin Gou

    图  33  老林沟场景常见InSAR方法配准后的相干系数图和干涉相位图

    Figure  33.  Coherence coefficient diagrams and interferograms after registration of common InSAR methods of Laolin Gou

    图  34  老林沟场景不同方法相干系数分布曲线

    Figure  34.  Coherence coefficient distribution with different methods of Laolin Gou

    表  1  系统参数

    Table  1.   System parameters

    参数 数值
    中心频率 400 MHz
    信号带宽 60 MHz
    脉冲宽度 2 μs
    脉冲重复周期 200 μs
    下载: 导出CSV

    表  2  金海湖场景干涉图评价

    Table  2.   Evaluation of interferograms of Jinhai Lake

    方法 平均相干系数 残差点占比(%)
    配准前 0.44 23.60
    AFF 0.48 19.88
    MSF 0.47 20.35
    MCC 0.52 19.66
    所提方法 0.58 15.33
    下载: 导出CSV

    表  3  老林沟场景干涉图评价

    Table  3.   Evaluation of interferograms of Laolin Gou

    方法 平均相干系数 残差点占比(%)
    配准前 0.30 35
    AFF 0.31 35
    MSF 0.32 34
    MCC 0.35 32
    所提方法 0.38 28
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-10-04
  • 修回日期:  2024-01-02
  • 网络出版日期:  2024-01-11
  • 刊出日期:  2024-02-28

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