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| Citation: | LI Xiaowan, LIANG Xingdong, ZHANG Fubo, et al. A geometry constrained moving least squares-based high-precision 3D reconstruction method of mountains from TomoSAR point clouds[J]. Journal of Radars, 2022, 11(3): 363–375. doi: 10.12000/JR22049
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A Geometry Constrained Moving Least Squares-based High-precision 3D Reconstruction Method of Mountains from TomoSAR Point Clouds
DOI: 10.12000/JR22049
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Abstract
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. -
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References
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- Figure 1. Diagram of TomoSAR mountain observation geometry (y and z indicate the ground direction and the height direction, respectively, r and s indicate the range direction and the elevation direction, respectively)
- Figure 2. Flowchart of the geometry constrained MLS method
- Figure 3. The S shaped layover mountainous terrain on the range-elevation plane
- Figure 4. Diagram of two typical terrain for stagnation point positioning
- Figure 5. Diagram of projection transform
- Figure 6. Simulation results of LS, MLS and the proposed method
- Figure 7. The precision comparison curves of different methods based on data 1
- Figure 8. The main intermediate results of the proposed method for slice 5
- Figure 9. SAR image of the observation scene (the color map indicates the normalized scattering intensity)
- Figure 10. Results of the real experiment
- Figure 11. Optical image of the observation scene
- Figure 12. The three-dimensional reconstruction image (the color map indicates the normalized scattering intensity)