ABSTRACT Tomographic synthetic aperture radar (TomoSAR) has been widely used in three-dimensional (3D) reconstruction of urban buildings. However, due to the baseline distribution and the limitations of the algorithm itself, the building point cloud after tomographic imaging is flooded by substantial noise and/or false targets. Thus, TomoSAR point clouds must be extracted from these unwanted factors to reconstruct the building structure. Existing line-based extraction methods can only detect straight lines, which results in the loss of non-linear point clouds. Thus, inspired by density clustering, we propose a point cloud extraction method using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. The DBSCAN can preserve the building structure more completely by enabling the extraction of various shapes of the buildings. Since the detection of point clouds is density-based, noise and false targets that exhibit low-density distribution can be accurately detected and rejected. The experimental results demonstrated the effectiveness of our method for TomoSAR point cloud extraction, as well as the structural protection of buildings, which achieves a higher extraction accuracy compared to linear detection.

中文翻译：

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基于 DBSCAN 的点云提取用于断层合成孔径雷达 (TomoSAR) 三维 (3D) 建筑重建

摘要 断层合成孔径雷达（TomoSAR）已广泛应用于城市建筑的三维（3D）重建。然而，由于基线分布和算法本身的局限性，断层成像后的建筑点云被大量噪声和/或虚假目标淹没。因此，必须从这些不需要的因素中提取 TomoSAR 点云来重建建筑结构。现有的基于线的提取方法只能检测直线，导致非线性点云的丢失。因此，受密度聚类的启发，我们提出了一种使用基于密度的噪声应用空间聚类（DBSCAN）算法的点云提取方法。DBSCAN 可以通过提取建筑物的各种形状来更完整地保留建筑物结构。由于点云的检测是基于密度的，因此可以准确检测和拒绝呈现低密度分布的噪声和假目标。实验结果证明了我们的TomoSAR点云提取方法的有效性，以及建筑物的结构保护，与线性检测相比，该方法具有更高的提取精度。