LiDAR Ground Filtering Algorithm for Urban Areas Using Scan Line Based Segmentation
This work addresses a domain-specific problem for urban LiDAR data processing, offering an incremental improvement in ground filtering methods.
The paper tackles the problem of separating ground points from airborne LiDAR point clouds in urban areas by proposing a novel ground filtering method called SLSGF, which uses scan line segmentation and a coarse-to-fine labeling scheme, achieving computational efficiency and noise insensitivity without requiring denoising.
This paper addresses the task of separating ground points from airborne LiDAR point cloud data in urban areas. A novel ground filtering method using scan line segmentation is proposed here, which we call SLSGF. It utilizes the scan line information in LiDAR data to segment the LiDAR data. The similarity measurements are designed to make it possible to segment complex roof structures into a single segment as much as possible so the topological relationships between the roof and the ground are simpler, which will benefit the labeling process. In the labeling process, the initial ground segments are detected and a coarse to fine labeling scheme is applied. Data from ISPRS 2011 are used to test the accuracy of SLSGF; and our analytical and experimental results show that this method is computationally-efficient and noise-insensitive, thereby making a denoising process unnecessary before filtering.