Proceedings of the 2016 4th International Conference on Machinery, Materials and Computing Technology

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

Authors
Xingbo Hu, Ying Xie
Corresponding Author
Xingbo Hu
Available Online March 2016.
DOI
https://doi.org/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.
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This is an open access article distributed under the CC BY-NC license.

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Volume Title
Proceedings of the 2016 4th International Conference on Machinery, Materials and Computing Technology
Series
Advances in Engineering Research
Publication Date
March 2016
ISBN
978-94-6252-165-0
ISSN
2352-5401
DOI
https://doi.org/10.2991/icmmct-16.2016.250How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

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  - https://doi.org/10.2991/icmmct-16.2016.250
ID  - Hu2016/03
ER  -