Chinese Journal of Lasers, Volume. 50, Issue 6, 0610002(2023)
Single Tree Segmentation Method for Terrestrial LiDAR Point Cloud Based on Connectivity Marker Optimization
Fig. 3. Search results of local extreme points. (a) Clustering result of connectivity growth; (b) three-dimensional clustering diagram of branches and trunks; (c) clustering status of branches and trunks; (d) result of extracting tree vertices
Fig. 4. Schematics of density contour. (a) Schematic of under-segmented two-dimensional density contours; (b) schematic of under-segmented three-dimensional density contours
Fig. 5. Schematics of under-segmentation. (a) Result of low vegetation segmentation; (b) result of double tree segmentation; (c) result of three wood tree segmentation
Fig. 7. Single tree segmentation results in this study. (a) Sample 1; (b) sample 2; (c) sample 3
Fig. 9. Single tree extraction result of mixed woodland. (a) 3D point cloud data of mixed woodland; (b) segmentation result of proposed method
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Zhenyang Hui, Na Li, Penggen Cheng, Zhuoxuan Li, Zhaochen Cai. Single Tree Segmentation Method for Terrestrial LiDAR Point Cloud Based on Connectivity Marker Optimization[J]. Chinese Journal of Lasers, 2023, 50(6): 0610002
Category: remote sensing and sensor
Received: Jan. 26, 2022
Accepted: Jun. 7, 2022
Published Online: Mar. 6, 2023
The Author Email: Cheng Penggen (198560017@ecut.edu.cn)