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.
In another study, Hu and Ye REF proposed a Douglas-Peucker algorithm for segmenting the scanline into segment objects based on height variation and set a simple rule-based classification to distinguish building and non-building in outdoor environment.
A FAST AND SIMPLE METHOD OF BUILDING DETECTION FROM LIDAR DATA BASED ON SCAN LINE ANALYSIS
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Computer Science