Segmentation and Clustering of 3D Forest Point Cloud Using Mean Shift Algorithms
Xingbo Hu, Ying Xie
Available Online March 2016.
- 10.2991/icmmct-16.2016.250How to use a DOI?
- Point cloud, mean shift, forest segmentation, LiDAR.
Segmenting individual trees from the forest point cloud has significant implications in forestry inventory. This paper presents a novel computational scheme to segment and cluster the 3D point cloud data acquired by an airborne LiDAR. The scheme employs a mean shift-based iterative procedure on the data sets in a defined complex multimodal feature space to cluster points with similar modes together. Experimental results reveal that the proposed scheme can work effectively and the average accuracy of tree detection (88.6%) can meet the requirements of forest inventory.
- © 2016, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Xingbo Hu AU - Ying Xie PY - 2016/03 DA - 2016/03 TI - Segmentation and Clustering of 3D Forest Point Cloud Using Mean Shift Algorithms BT - Proceedings of the 2016 4th International Conference on Machinery, Materials and Computing Technology PB - Atlantis Press SP - 1274 EP - 1278 SN - 2352-5401 UR - https://doi.org/10.2991/icmmct-16.2016.250 DO - 10.2991/icmmct-16.2016.250 ID - Hu2016/03 ER -