Extraction of the building region from airborne LiDAR point clouds under complex urban conditions

Abstract

Airborne LiDAR technology provides large quantities of three-dimensional point clouds coordinates for detecting buildings.To effectively identify the building region from the vegetation,the authors adopt a progressive densification TIN method to filter no-ground points.After removing some point clouds whose height is less than 3m from ground surface and isolated point clouds,a binary grid cell is produced based on no-ground points.Next,defined segmentation operators are used to disconnect the possible link between buildings and vegetation.Two kinds of regions are clustered based on height difference criteria using the region growing method.Then building regions are extracted according to large slope density values.Finally,a morphological closing operator is applied to fill small holes and smooth edges of extracted building regions.Three typical areas with complex urban conditions were selected to test this algorithm.The results show that this algorithm gives a comparative performance with a precision of over 91 percent in quality and completeness.The results also demonstrate that this algorithm can automatically recognize buildings.