期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2008
卷号：XXXVII Part B4
页码：457-462
出版社：Copernicus Publications
摘要：Recently, automatic object extraction from Light Detection And Ranging (LiDAR) data has attracted great attention. The level of detail and the quality of the collected point cloud motivated the research community to investigate the possibility of automatic object extraction from such data. Prior accurate knowledge of terrain information is usually essential for the data to be usable in further processing, such as feature extraction, and to obtain better object detection results. In this paper, a new strategy for automatic terrain extraction from LiDAR data is presented. The proposed strategy is based on the fact that sudden elevation changes, which usually correspond to non-ground objects, will cause relief displacements in perspective views. The introduced relief displacements will occlude neighboring ground points. A Digital Surface Model (DSM) is first generated by resampling the irregular LiDAR point clouds to a regular grid. By using synthesized projection centers located above the DSM and analyzing the visibility maps in perspective images, we can classify the DSM into non-ground and ground hypotheses. Surface roughness and inherent noise in the point cloud will lead to some false hypotheses. By using a novel algorithm which combines plane fitting and statistical filtering to remove these false hypotheses, non- ground and ground points can be separated. The algorithm has been tested using both simulated and real datasets. The results have demonstrated that our approach can perform well with highly complex data from an urban area. In a comparison with the results obtained with TerraScan software, our algorithm showed the capability of producing better results while being less sensitive to used parameters
关键词：LiDAR; DEM/DTM Extraction; Photogrammetry; Classification; Laser Scanning; Point Cloud
