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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。
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关键词:
- 无人机载干涉合成孔径雷达 /
- 图像配准 /
- 复杂地形 /
- 多项式模型 /
- 图像分区
Abstract: Miniaturized and lightweight Unmanned Aerial Vehicles (UAV) provide a flexible platform for Synthetic Aperture Radar (SAR). The application of UAV Interferometric SAR (InSAR) is gradually increasing in interferometric measurement fields. UAVs are small and light, which are easily affected by airflow disturbances. Their trajectories are nonlinear and unparallel when adopting the multipass mode for interferometry. The nonlinear and unparallel trajectories result in geometric distortion between the interferometric image pairs. Under complex topography conditions, the interferometric image pairs of UAVs have large offsets that are obviously space-dependent, thereby resulting in substantial technical challenges during image registration. Conventional image registration methods based on polynomial fitting are no longer applicable. In this study, we proposed an image registration method based on image partition with topography assistance. First, an elevation threshold is generated based on the UAV trajectories, and the measurement area is partitioned using the assisted topography. Then, a polynomial fitting model is constructed for offsets within each partition with constraints applied at the partition boundaries for joint optimization. Finally, continuous global offset fitting surfaces are obtained, and precise image registration is achieved by resampling the slave image. The effectiveness of the proposed method is preliminarily validated using real measurement data obtained from UAV InSAR in the P-band. -
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 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 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 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 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 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 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 
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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
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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
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