Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-5, 77-81, 2014
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-5/77/2014/
doi:10.5194/isprsarchives-XL-5-77-2014
© Author(s) 2014. This work is distributed
under the Creative Commons Attribution 3.0 License.
 
05 Jun 2014
Automatic Classification of coarse density LiDAR data in urban area
H.M. Badawy, A. Moussa, and N. El-Sheimy Department of Geomatics Engineering, University of Calgary, 2500 University Dr. NW, Calgary, AB T2N 1N4, Canada
Keywords: LIDAR, Vehicle, Classification, Airborne, Urban, PCA, NDSM, DTM Abstract. The classification of different objects in the urban area using airborne LIDAR point clouds is a challenging problem especially with low density data. This problem is even more complicated if RGB information is not available with the point clouds. The aim of this paper is to present a framework for the classification of the low density LIDAR data in urban area with the objective to identify buildings, vehicles, trees and roads, without the use of RGB information. The approach is based on several steps, from the extraction of above the ground objects, classification using PCA, computing the NDSM and intensity analysis, for which a correction strategy was developed. The airborne LIDAR data used to test the research framework are of low density (1.41 pts/m2) and were taken over an urban area in San Diego, California, USA. The results showed that the proposed framework is efficient and robust for the classification of objects.
Conference paper (PDF, 1395 KB)


Citation: Badawy, H. M., Moussa, A., and El-Sheimy, N.: Automatic Classification of coarse density LiDAR data in urban area, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-5, 77-81, doi:10.5194/isprsarchives-XL-5-77-2014, 2014.

BibTeX EndNote Reference Manager XML