Abstract
Tomographic Synthetic Aperture Radar (TomoSAR) is a breakthrough of the traditional SAR, which has the three-dimentional (3D) observation ability of layover scenes such as buildings and high mountains. As an advanced system, the airborne array TomoSAR can effectively avoid temporal de-correlation caused by long revisit time, which has great application in high-precision mountain surveying and mapping. The 3D reconstruction using TomoSAR has mainly focused on low targets, while there are few literatures on 3D mountain reconstruction. Due to the layover phenomenon, surveying in high mountain areas remains a difficult task. Consequently, it is meaningful to carry out the research on 3D mountain reconstruction using the airborne array TomoSAR. However, the original TomoSAR mountain point cloud faces the problem of elevation ambiguity. Furthermore, for mountains with complex terrain, the points located in different elevation periods may intersect. This phenomenon increases the difficulty of solving the problem. In this paper, a novel elevation ambiguity resolution method is proposed. First, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Gaussian Mixture Model (GMM) are combined for point cloud segmentation. The former ensures coarse segmentation based on density, and the latter allows fine segmentation of the abnormal categories caused by intersection. Subsequently, the segmentation results are reorganized in the elevation direction to reconstruct all possible point clouds. Finally, the real point cloud can be extracted automatically under the constraints of the boundary and elevation continuity. The performance of the proposed method is demonstrated by simulations and experiments. Based on the airborne array TomoSAR experiment in Leshan City, Sichuan Province, China in 2019, the 3D model of the surveyed mountain is presented. Moreover, three kinds of external data are applied to fully verify the validity of this method.
Highlights
Because of the problem of temporal decorrelation caused by long revisit time, the traditional Tomographic Synthetic Aperture Radar (TomoSAR) systems with repeat-pass cannot obtain a well-focused point cloud in the elevation direction, especially for mountains
The system combines the advantages of elevation solution and high-precision coherent measurement, filling the gap of TomoSAR system in the field of 3D mountain reconstruction
The results of Density-Based Spatial Clustering of Applications with Noise (DBSCAN), three-step automatic clustering (TSAC), Gaussian Mixture Model (GMM), and the proposed segmentation method are shown in Figure 10, where the different categories are color-coded
Summary
In 2006, Fornaro et al provided the first validation of spaceborne long-term SAR tomography
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