Tomographic Synthetic Aperture Radar (TomoSAR) has three-dimensional imaging capability. Nevertheless, its building structure is full of considerable noise and wrong targets owing to the baseline distribution and the algorithm restrictions. Accordingly, efficiently extracting and segmenting buildings from SAR point clouds with huge data is a critical issue. According to the characteristics of building facade point clouds, the KD-Tree-based Euclidean clustering is introduced as a fast-clustering method for better extracting and segmenting individual building facades. At first, a simple morphological filter is utilized to identify the ground and non-ground point clouds. The building facades are then extracted from the non-ground point clouds based on density information. Subsequently, the KD-Tree-based Euclidean clustering method denoises the building facades and segments them into individual facades. Finally, a second Euclidean clustering is performed for each of these facades to denoise them again. Experimental results indicate that the presented approach can prepare accurate and efficient extraction and segmentation results for complex urban scenes.

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.