Segmentation and Clustering of 3D Forest Point Cloud Using Mean Shift Algorithms

DOI

10.2991/icmmct-16.2016.250How to use a DOI?

Keywords

Point cloud, mean shift, forest segmentation, LiDAR.

Abstract

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.

Copyright

© 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/).

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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  -