A Large-Scale Building Unsupervised Extraction Method Leveraging Airborne LiDAR Point Clouds and Remote Sensing Images Based on a Dual P-Snake Model

Abstract

Automatic large-scale building extraction from the LiDAR point clouds and remote sensing images is a growing focus in the fields of the sensor applications and remote sensing. However, this building extraction task remains highly challenging due to the complexity of building sizes, shapes, and surrounding environments. In addition, the discreteness, sparsity, and irregular distribution of point clouds, lighting, and shadows, as well as occlusions of the images, also seriously affect the accuracy of building extraction. To address the above issues, we propose a new unsupervised building extraction algorithm PBEA (Point and Pixel Building Extraction Algorithm) based on a new dual P-snake model (Dual Point and Pixel Snake Model). The proposed dual P-snake model is an enhanced active boundary model, which uses both point clouds and images simultaneously to obtain the inner and outer boundaries. The proposed dual P-snake model enables interaction and convergence between the inner and outer boundaries to improve the performance of building boundary detection, especially in complex scenes. Using the dual P-snake model and polygonization, this proposed PBEA can accurately extract large-scale buildings. We evaluated our PBEA and dual P-snake model on the ISPRS Vaihingen dataset and the Toronto dataset. The experimental results show that our PBEA achieves an area-based quality evaluation metric of 90.0% on the Vaihingen dataset and achieves the area-based quality evaluation metric of 92.4% on the Toronto dataset. Compared with other methods, our method demonstrates satisfactory performance.