测绘通报 ›› 2026, Vol. 0 ›› Issue (3): 38-43.doi: 10.13474/j.cnki.11-2246.2026.0307

• 学术研究 • 上一篇    下一篇

双向生长的LiDAR点云单木分割方法

林磊1, 惠振阳1, 涂梨平2, 范军林2, 毛亚琴2, 惠婷3   

  1. 1. 东华理工大学测绘与空间信息工程学院, 江西 南昌 330013;
    2. 江西核工业测绘院集团有限公司, 江西 南昌 330199;
    3. 广东农工商职业技术学院, 广东 广州 510507
  • 收稿日期:2025-06-17 发布日期:2026-04-08
  • 通讯作者: 惠振阳。E-mail:huizhenyang2008@ecut.edu.cn
  • 作者简介:林磊(1999—),男,硕士生,主要研究方向为激光雷达林业遥感。E-mail:2022120383@ecut.edu.cn
  • 基金资助:
    江西省自然科学基金面上项目(20242BAB25176);国家自然科学基金(42161060;41801325);江西省杰出青年基金(原创探索类)项目(20232ACB213017);江西省“双千计划”高层次人才项目(DHSQT42023002);2024年度铀资源探采与核遥感全国重点实验室项目(2024QZ-TD-26);江西省地质局青年科学技术带头人培养计划(2025JXDZKJRC06)

Single tree segmentation from LiDAR point cloud by bidirectional growth

LIN Lei1, HUI Zhenyang1, TU Liping2, FAN Junlin2, MAO Yaqin2, HUI Ting3   

  1. 1. School of Surveying and Geoinformation Engineering, East China University of Technology, Nanchang 330013, China;
    2. Jiangxi Nuclear Industry Surveying and Mapping Institute Group Co., Ltd., Nanchang 330199, China;
    3. Guangdong AIB Polytechnic, Guangzhou 510507, China
  • Received:2025-06-17 Published:2026-04-08

摘要: 针对传统单木分割方法易受树顶点提取精度影响的问题,本文提出了一种基于LiDAR点云双向生长的单木分割方法。首先,通过计算树干点云多层切片的圆心进行直线拟合,找到树干对应的轴线及轴线与地面交点以确定树木位置。然后,依据树木位置逐步自下而上地进行聚类找到单木树木位置所对应的树顶点。最后,基于树顶点采用自上而下的方式进行单木点云渐进生长逐步实现单木分割。为验证本文方法的有效性,本文选取了3个不同森林环境的区域进行单木分割试验。试验结果表明,3个样地的单木分割结果的F1得分分别为0.971、0.886和0.865。相较于传统方法,本文方法能够获取最优的单木提取率、匹配率,以及最低的漏分误差。试验结果表明,本文所提出的双向生长单木分割法能够获得更为准确的单木分割结果,具有较强的稳健性。

关键词: 激光雷达, 单木分割, 树顶点提取, 双向生长

Abstract: To address the issue that traditional individual tree segmentation methods are susceptible to the accuracy of tree apex extraction,this paper proposes a bidirectional growth-based individual tree segmentation method using LiDAR point cloud data.The approach initially calculates the centers of multi-layer slices of trunk point cloud to perform linear fitting,thereby identifying the trunk axis and its intersection point with the ground to determine the tree position.Subsequently,based on the tree position,clustering is progressively conducted from bottom to top to locate the tree apex corresponding to each individual tree.Finally,a top-down progressive growth strategy is employed to segment individual tree point clouds.To validate the effectiveness of the proposed method,experiments were conducted in three distinct forest environments.The experimental results demonstrate F1 scores of 0.971,0.886,and 0.865 for the individual tree segmentation results in the three sample plots,respectively.Compared to conventional methods,our approach achieves the optimal individual tree extraction rate,matching rate,and the lowest omission error.These findings indicate that the proposed bidirectional growth individual tree segmentation method yields more accurate results and exhibits strong robustness.

Key words: LiDAR, single tree segmentation, tree vertex extraction, bidirectional growth

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