ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume II-3/W3
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3/W3, 1–6, 2013
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3/W3, 1–6, 2013

  08 Oct 2013

08 Oct 2013

Rule-based segmentation of LIDAR point cloud for automatic extraction of building roof planes

M. Awrangjeb1 and C. S. Fraser2 M. Awrangjeb and C. S. Fraser
  • 1Gippsland School of Information Technology, Monash University, Churchill Vic 3842 Australia
  • 2CRC for Spatial Information, Dept. of Infrastructure Engineering, University of Melbourne, Vic 3010, Australia

Keywords: LIDAR, point cloud, segmentation, building, roof, extraction

Abstract. This paper presents a new segmentation technique for LIDAR point cloud data for automatic extraction of building roof planes. Using the ground height from a DEM (Digital Elevation Model), the raw LIDAR points are separated into two groups: ground and nonground points. The ground points are used to generate a "building mask" in which the black areas represent the ground where there are no laser returns below a certain height. The non-ground points are segmented to extract the planar roof segments. First, the building mask is divided into small grid cells. The cells containing the black pixels are clustered such that each cluster represents an individual building or tree. Second, the non-ground points within a cluster are segmented based on their coplanarity and neighbourhood relations. Third, the planar segments are refined using a rule-based procedure that assigns the common points among the planar segments to the appropriate segments. Finally, another rule-based procedure is applied to remove tree planes which are small in size and randomly oriented. Experimental results on the Vaihingen data set show that the proposed method offers high building detection and roof plane extraction rates.