基于几何约束移动最小二乘的TomoSAR山区点云高精度三维重建方法

层析合成孔径雷达(TomoSAR)是一种先进的山区三维重建技术手段。然而，TomoSAR点云存在着较强烈的高程向定位误差，给高精度的山区三维重建带来了挑战。针对这个问题，该文提出了一种基于几何约束移动最小二乘(MLS)的高精度TomoSAR山区点云三维重建方法。该方法不仅具有传统MLS基于局部子空间思想进行复杂曲面结构拟合的优势，还可以充分地利用TomoSAR点云高程随地距单调递增的特点进行重建误差修正。首先，将点云投影到新的方位-地距-高程坐标系。然后，使用所提的基于迭代求解的几何约束MLS进行高程向定位误差修正。最后，通过投影变换得到山区三维重建结果。仿真和实测的机载阵列TomoSAR山区数据以及AW3D30 DSM数据和1:10000 DEM数据，验证了该文方法的有效性，同时表明了机载阵列TomoSAR用于山区高精度三维重建等应用的可行性和优越性。 

A Geometry Constrained Moving Least Squares-based High-precision 3D Reconstruction Method of Mountains from TomoSAR Point Clouds

Tomographic Synthetic Aperture Radar (TomoSAR) is an advanced technology for three-dimensional (3D) mountain reconstruction. However, the TomoSAR mountain point clouds have a significant location error in the elevation direction, making high-precision 3D reconstruction of mountains difficult. A geometry constrained Moving Least Squares (MLS)-based high-precision 3D reconstruction method is addressed in this issue. This method not only has the benefits of the traditional MLS in that it uses the local subspace principle for fitting complex surface structures but also fully uses the TomoSAR point cloud characteristic of monotonically increasing elevation with ground distance for reconstruction error correction. The point clouds are first projected onto a new azimuth-ground-elevation domain. Subsequently, the suggested iterative solution-based geometry constrained MLS performs location error correction in the elevation direction. Finally, the projection transformation is used to generate 3D reconstruction results of mountains. The simulation and measurement of airborne array TomoSAR mountain data, AW3D30 DSM data, and 1:10,000 DEM data validate the effectiveness of the proposed method and demonstrate the feasibility and superiority of airborne array TomoSAR for applications such as high-precision 3D mountain reconstruction.