AN INDIVIDUAL TREE SEGMENTATION ALGORITHM WITHOUT PRIOR-KNOWLEDGE BASED ON AIRBORNE LIDAR DATA

被引：0

作者：

Li Shihua ^{[1
,2
]}

Liu Yuhan ^{[2
]}

Zhao Shunda ^{[2
]}

Wan Lihong ^{[2
]}

机构：

[1] Univ Elect Sci & Technol China, Yangtze Delta Reg Inst Huzhou, Huzhou 313001, Peoples R China

[2] Univ Elect Sci & Technol China, Sch Resources & Environm, Chengdu 611731, Peoples R China

来源：

IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2023年

基金：

中国国家自然科学基金;

关键词：

LiDAR; individual tree segmentation; forest; CANOPY;

D O I：

10.1109/IGARSS52108.2023.10283083

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

This paper aims at improving the individual tree segmentation method proposed by Liu et al. (2019), which is based on the 3-D distribution of point cloud without any prior knowledge or manual threshold setting. The improved algorithm has been applied to 22 plots of single-layered coniferous forest, single-layered broad-leaved forest, single-layered coniferous broad-leaved mixed forest, multi-layered coniferous forest and multi-layered coniferous broad-leaved mixed forest. The results showed that the algorithm can segment trees effectively, with an overall root mean square extraction rate of 0.95, and a root mean square F_Score of 0.73. It was found that the algorithm performed better in coniferous forest and single forest type of single layer with a stand density less than 300 plants/ha.

引用

页码：3225 / 3228

页数：4

共 8 条

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