[1]曹荣贞,刘浩然,林文树*. 基于多尺度几何特征单木点云的语义分割[J].西北林学院学报,2024,39(2):28-35.[doi:10.3969/j.issn.1001-7461.2024.02.04]
　CAO Rong-zhen,LIU Hao-ran,LIN Wen-shu*. Semantic Segmentation of Individual Tree Point Clouds Based on Multi-scale Geometric Features[J].JOURNAL OF NORTHWEST FORESTRY UNIVERSITY,2024,39(2):28-35.[doi:10.3969/j.issn.1001-7461.2024.02.04]
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 基于多尺度几何特征单木点云的语义分割()
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《西北林学院学报》[ISSN:1001-7461/CN:61-1202/S]

卷:
第39卷
期数:
2024年第2期
页码:
28-35
栏目:
出版日期:
2024-03-31

文章信息/Info

Title:
 Semantic Segmentation of Individual Tree Point Clouds Based on Multi-scale Geometric Features
文章编号:
1001-7461(2024)02-0028-08
作者:
 曹荣贞; 刘浩然; 林文树*
 （东北林业大学 机电工程学院，黑龙江 哈尔滨 150040）
Author(s):
 CAO Rong-zhen; LIU Hao-ran; LIN Wen-shu*
 (College of Mechanical and Electrical Engineering,Northeast Forestry University,Harbin 150040,Heilongjiang,China)
关键词:
 ; 地面激光扫描; 点云数据; 多尺度; 几何特征; 语义分割
Keywords:
 ; terrestrial laser scanning;  point cloud data;  multiple scale;  geometric feature;  semantic segmentation
分类号:
S758.1
DOI:
10.3969/j.issn.1001-7461.2024.02.04
文献标志码:
A
摘要:
 针对目前地面激光扫描树木点云精确分离枝干点和树叶点成分困难的问题，提出一种基于多尺度几何特征的单木点云语义分割的方法。首先将水曲柳和樟子松样地点云数据进行单木分割，再计算多尺度点云协方差矩阵特征值，之后选择特征分类器，并根据特征值重要性选择最优特征值，最后对树木的枝干叶点云进行分割。通过比较支持向量机（SVM）、极限梯度增强（XGBoost）和随机森林（RF）3种分类器的训练时间和精度，选择XGBoost作为最终的分类器，并根据特征值重要性选择出6种最优特征。结果表明，2块样地枝干点和叶点的分割精度都>0.88，并且F1值和IOU也都在0.8以上。提出的方法能够有效分割出水曲柳和樟子松树叶点云和枝干点云，有较高的识别精度，研究结果为后续单木三维模型构建及生物量估算提供了条件。
Abstract:
 It is difficult to accurately separate limb and leaf points from terrestrial laser scanning (TLS) tree point clouds.To solve the problem a method of semantic segmentation of individual tree point clouds based on multi-scale geometry features was proposed.First,the point cloud data of trees in the sample plots of Fraxinus mandshurica and Pinus sylvestris were segmented into individual trees,and the eigenvalues of the multi-scale point cloud covariance matrix were calculated,then the feature classifier was selected,and the optimal eigenvalues were selected according to the importance of the eigenvalues,and finally the limb and leaf points of tree were divided.By comparing the training time and accuracy of support vector machine (SVM),extreme gradient boosting (XGBoost) and random forest (RF) classifiers,the XGBoost was selected as the final classifier,and six optimal features were selected according to the importance of eigenvalues.The results showed that the segmentation accuracy of limb and leaf in the two plots was more than 0.88,and F1-Score and IOU (intersection over union) were also above 0.8.The method proposed in this study can effectively segment limb and leaf points of F.mandshurica and P.sylvestris,and has a high identification accuracy.The research results provide conditions for the subsequent three-dimensional model construction and biomass estimation of individual tree.

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备注/Memo

备注/Memo:
 收稿日期：2023-03-17修回日期：2023-05-08
基金项目：国家自然科学基金（31971574）。
第一作者：曹荣贞。研究方向：林业遥感。E-mail：1046085920@qq.com
*通信作者：林文树，博士，教授，博士生导师。研究方向：森林资源遥感监测。E-mail：linwenshu@nefu.edu.cn
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