Segmentation of building roofs from airborne LiDAR point clouds using robust voxel-based region growing

The research herein presents a new approach for extracting building roofs using a robust voxel-based region growing segmentation method. The proposed approach exploits the fact that the roof of the building consists of planar surfaces and has distinctive geometric features than other kinds of objects. Based on this assumption, we present a method using voxel structure and region growing strategy with robust principal component analysis (RPCA). The voxels is clustered by a region growing process, utilizing the smoothness, continuity, and convexity as geometric cues. RPCA is introduced to estimate the attribute of voxels. Roofs are recognized from the segments by using the object-based spectral clustering. Our approach has been validated by different airborne laser scanning (ALS) point clouds. Qualitative and quantitative results reveal that our method outperforms some representative algorithms in segmentation using our testing datasets under a complex situation, with overall quality measure better than 0.7 and 0.6.