Road centerline extraction from airborne LiDAR point cloud based on hierarchical fusion and optimization

Road information acquisition is an important part of city informatization construction. Airborne LiDAR provides a new means of acquiring road information. However, the existing road extraction methods using LiDAR point clouds always decide the road intensity threshold based on experience, which cannot obtain the optimal threshold to extract a road point cloud. Moreover, these existing methods are deficient in removing the interference of narrow roads and several attached areas (e.g., parking lot and bare ground) to main roads extraction, thereby imparting low completeness and correctness to the city road network extraction result. Aiming at resolving the key technical issues of road extraction from airborne LiDAR point clouds, this paper proposes a novel method to extract road centerlines from airborne LiDAR point clouds. The proposed approach is mainly composed of three key algorithms, namely, Skewness balancing, Rotating neighborhood, and Hierarchical fusion and optimization (SRH). The skewness balancing algorithm used for the filtering was adopted as a new method for obtaining an optimal intensity threshold such that the "pure" road point cloud can be obtained. The rotating neighborhood algorithm on the other hand was developed to remove narrow roads (corridors leading to parking lots or sidewalks), which are not the main roads to be extracted. The proposed hierarchical fusion and optimization algorithm caused the road centerlines to be unaffected by certain attached areas and ensured the road integrity as much as possible. The proposed method was tested using the Vaihingen dataset. The results demonstrated that the proposed method can effectively extract road centerlines in a complex urban environment with 91.4% correctness and 80.4% completeness. (C) 2016 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.