ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3/W1, 7-13, 2013
© Author(s) 2013. This work is distributed
under the Creative Commons Attribution 3.0 License.
16 May 2013
School of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan, 430079, China
Keywords: Building extraction, LiDAR, Scan line, Segmentation, Local analysis Abstract. One of the major problems in processing LiDAR (Light Detection And Ranging) data is its huge data volume which causes very high computational load when dealing with large areas with high point density. A fast and simple algorithm based on scan line analysis is proposed for automatic detection of building points from LiDAR data. At first, ground/non-ground classification is performed to filter out the ground points. Douglas–Peucker algorithm is then used to segment the scan line into segment objects based on height variation. These objects are preliminarily classified into buildings and vegetation based on local analysis using simple rules. At last, the region growing method is used to improve the quality of the extraction. The test data provided by the ISPRS test project on urban object extraction, containing a lot of buildings with complex roof structures, various sizes, and different heights, is used to test the algorithm. The experimental results show that the proposed algorithm can extract building regions effectively.
Received: 05 Mar 2013 – Revised: 02 May 2013 – Accepted: 04 May 2013 – Published: 16 May 2013