대한원격탐사학회:학술대회논문집 (Proceedings of the KSRS Conference)
- 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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- Pages.153-155
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- 2003
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- 1226-9743(pISSN)
Segmentation and Classification of Lidar data
- Tseng, Yi-Hsing (Department of Geomatics, National Cheng Kung University) ;
- Wang, Miao (Department of Geomatics, National Cheng Kung University)
- 발행 : 2003.11.03
초록
Laser scanning has become a viable technique for the collection of a large amount of accurate 3D point data densely distributed on the scanned object surface. The inherent 3D nature of the sub-randomly distributed point cloud provides abundant spatial information. To explore valuable spatial information from laser scanned data becomes an active research topic, for instance extracting digital elevation model, building models, and vegetation volumes. The sub-randomly distributed point cloud should be segmented and classified before the extraction of spatial information. This paper investigates some exist segmentation methods, and then proposes an octree-based split-and-merge segmentation method to divide lidar data into clusters belonging to 3D planes. Therefore, the classification of lidar data can be performed based on the derived attributes of extracted 3D planes. The test results of both ground and airborne lidar data show the potential of applying this method to extract spatial features from lidar data.