DBSCAN-based point cloud extraction for Tomographic synthetic aperture radar (TomoSAR) three-dimensional (3D) building reconstruction

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.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was funded by the National Natural Science Foundation of China (under the grants: 61501019 and 41801229) and the Scientific Research Project of the Beijing Educational Committee (under the grant: SQKM201710016008). It was partially supported by the Fundamental Research Funds for the Beijing University of Civil Engineering and Architecture (under the grant: 18209).