Building point cloud reconstruction in TomoSAR based on deep learning semantic segmentation

Tomographic synthetic aperture radar (TomoSAR) possesses 3D imaging capability, making it significant for building reconstruction using TomoSAR data. The reconstruction algorithm is closely related to building point cloud detection, while traditional detection methods suffer from low automation and reliance on manual configuration. This study proposes a building point cloud reconstruction method based on deep learning semantic segmentation. Initially, deep learning method is employed for end-to-end building point cloud segmentation, followed by point cloud reconstruction based on the segmentation results. The proposed method is simple and efficient, elevating the level of automation in point cloud processing. Experimental validation on real TomoSAR data confirms that the proposed method achieves automated and refined reconstruction of building point clouds. In this letter, we propose an SAR building point cloud reconstruction method based on deep learning point cloud segmentation. Leveraging the end-to-end processing advantage of deep learning, this method achieves automated and efficient detection of building point clouds. The subsequent reconstruction process is relatively straightforward, avoiding complex manual operations, and results in detailed building facade and roof structures. image