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遥感技术与应用  2022, Vol. 37 Issue (1): 161-172    DOI: 10.11873/j.issn.1004-0323.2022.1.0161
青促会十周年专栏     
基于网络图的地基激光雷达复杂树木点云枝叶分离方法
林筱涵1,2(),李爱农1(),边金虎1,张正建1,2,南希1
1.中国科学院水利部成都山地灾害与环境研究所数字山地与遥感应用中心,四川 成都 610041
2.中国科学院大学资源与环境学院,北京 100049
A Method for Separating Leaf and Wood Components of Complex Tree Point Cloud Data based on Network Graph with Terrestrial Laser Scanning
Xiaohan Lin1,2(),Ainong Li1(),Jinhu Bian1,Zhengjian Zhang1,2,Xi Nan1
1.Center for Digital Mountain and Remote Sensing Application,Institute of Mountain Hazards and Environment,Chinese Academy of Sciences,Chengdu 610041,China
2.University of Chinese Academy of Sciences,Beijing 100049,China
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摘要:

地基激光雷达树木点云数据的枝叶分离是精确计算地上生物量和叶面积指数的重要前提,也是树木三维建模的重要步骤。然而,山地复杂树木冠幅大且结构复杂,从而造成树叶与枝干之间的相互遮挡,因此很难获取高质量的点云数据,目前对其实现枝叶分离依然存在较大的困难。利用地基激光雷达FARO Focus3D X330获取三维激光点云数据,提出了一种基于网络图的树木点云枝叶分离方法。首先,采用LeWos模型对点云进行初步的枝叶分离,分离出枝干和叶片点云。在此基础上,针对枝干和叶片混合点云通过路径追踪检测算法来精细分离枝干和叶片。随着路径长度从10增加到100,枝干点不断增加,叶片点不断减少,枝叶分离精确度、枝干F分数、叶片F分数、Kappa系数均先增加后减少。综合这4项精度评价指标,选取各个树木最优路径长度执行路径追踪检测算法。通过与LeWos模型、Tlseparation模型和高斯混合模型等主流枝叶分离方法比较发现,该方法精度更优,精确度为91.97%。而且,该方法的枝干F分数和叶片F分数均大于85%,这表明该方法具有很好的平衡性。该方法仅使用路径长度,不考虑几何特征,因此极大地提高了针叶树木的枝叶分离精度。同时,该方法对不同树种、不同密度点云枝叶分离效果均较好,鲁棒性强。精确的树木点云枝叶分离对森林资源管理和生物多样性研究等具有重要意义。

关键词: 地基激光雷达枝叶分离网络图LeWos模型路径追踪检测算法    
Abstract:

The leaf and wood separation of the terrestrial laser scanning tree point cloud data is an important prerequisite for the accurate estimation of above-ground biomass and leaf area index, and it is also an important step for three-dimensional modeling of a tree. However, complex trees in mountain areas have large crowns and complex structures, resulting in mutual occlusion between leaves and branches. Therefore, it is difficult to obtain high-quality point cloud data. At present, it is still difficult for complex trees to separate leaf and wood components. High-resolution point clouds were acquired with Faro Focus3D X330. This paper proposes a method for leaf and wood separation of tree point cloud based on network graph. First, the LeWos model is used to perform preliminary leaf and wood separation on the point cloud, and separate the wood and leaf point cloud. On this basis, the path retrace detection algorithm is used to finely separate the leaf and wood for the mixed point clouds. As the retrace steps increases from 10 to 100, the wood points continue to increase, the leaf points continue to decrease, the accuracy, wood F-score decreases, leaf F-score and the Kappa coefficient first increases and then decreases. By comparing with LeWos model, Tlseparation model and Gaussian mixture model, it is found that the research method in this paper has the better precision, with an accuracy of 91.97%. Moreover, the wood F-score and leaf F-score of the proposal method are both greater than 85%, which means that the proposal method has a good balance when classifying wood and leaf. The proposal method only uses the retrace steps and does not consider the geometric characteristics, so the accuracy of branch and leaf separation of coniferous trees is greatly improved. At the same time, the method in this paper has a good effect on the separation of wood and leaf components of point clouds with different densities and different species. Therefore, the method in this paper is more robust. Accurate wood and leaf separation of tree point clouds is of great significance to forest resource management and biodiversity research.

Key words: Terrestrial laser scanning    Leaf and wood separation    Network graph    LeWos model    Path trace detection algorithm
收稿日期: 2021-10-08 出版日期: 2022-04-08
ZTFLH:  TN958.98  
基金资助: 国家重点研发计划项目“山地生态系统全球变化关键参数立体观测与高分辨率产品研制”(2020YFA0608700);国家自然科学基金项目“山地典型生态参量遥感反演建模及其时空表征能力研究”(41631180)
通讯作者: 李爱农     E-mail: linxiaohan@imde.ac.cn;ainongli@imde.ac.cn
作者简介: 林筱涵(1996—),山东威海人,男,硕士研究生,主要从事激光雷达遥感研究。E?mail:linxiaohan@imde.ac.cn
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引用本文:

林筱涵,李爱农,边金虎,张正建,南希. 基于网络图的地基激光雷达复杂树木点云枝叶分离方法[J]. 遥感技术与应用, 2022, 37(1): 161-172.

Xiaohan Lin,Ainong Li,Jinhu Bian,Zhengjian Zhang,Xi Nan. A Method for Separating Leaf and Wood Components of Complex Tree Point Cloud Data based on Network Graph with Terrestrial Laser Scanning. Remote Sensing Technology and Application, 2022, 37(1): 161-172.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2022.1.0161        http://www.rsta.ac.cn/CN/Y2022/V37/I1/161

图1  研究区审图号:GS(2019)3266
参数类型参数值
激光波长/nm1 550
扫描速度/(pts/s)976 000
测量范围/m0.6~130(反射率为90%的物体)
扫描范围/°水平:360°;垂直:300°
角度分辨率/°水平:0.009°;垂直0.009°
激光等级class 1
彩色全景三维相机7 000万像素
表1  FARO Focus3D X330技术参数
图2  样方点云
ID树高/m枝干点叶片点冠幅/m树种
Tree120.491 609 3012 395 49016.68红桦
Tree219.59206 88768 34114.59糙皮桦
Tree315.7657 21941 22510.32冷杉
Tree418.5636 48227 11713.69糙皮桦
Tree515.3539 06912 3947.82冷杉
Tree612.5031 6163 1443.83冷杉
Tree715.724 039 9522 168 60414.64黑桦
Tree822.3624 15812 44913.76冷杉
Tree910.9828 66419 0014.67红桦
Tree1011.20150 4361 033 1235.26冷杉
Tree1116.9475 22466 51612.59红桦
Tree1214.6175 10687 78611.73红桦
表2  树木数据描述
特征公式参考文献
显著特征1λ2[25]
显著特征2λ0-λ1[25]
显著特征3λ1-λ2[25]
平面度(λ1-λ2)/λ1[37]
线性度(λ0-λ1)/λ0[37]
表2  几何特征

分类类别

实际类别

枝干(Cw叶片(Cl
枝干(W)TwFl
叶片(L)FwTl
表3  逐点评估枝叶分离结果的混淆矩阵
图3  精确度、枝干F分数、叶片F分数及Kappa系数随路径长度变化图
图4  样方枝叶分离结果
TreeID精确度枝干F分数叶片F分数Kappa系数
Tree188.0486.1175.6989.50
Tree292.7094.987.1182.12
Tree390.0391.0188.8279.89
Tree496.9892.8796.5493.87
Tree596.0897.3992.3189.69
Tree696.4398.0281.6380.54
Tree778.9382.2373.9156.82
Tree890.5693.2484.3677.76
Tree995.7996.4294.8891.30
Tree1084.2988.0177.2365.82
Tree1195.6096.1695.8391.99
Tree1298.1698.0398.2896.31
平均91.9792.8787.2282.97
表4  本实验方法精度指标计算结果 (%)
树木类型模型精确度枝干F分数叶片F分数

Kappa

系数

阔叶树木LeWos模型85.1172.9483.8269.81
Tlseparotion模型72.0975.6964.2940.41
高斯合模型75.1977.4969.6847.53
本文方法92.3793.0390.8784.01
针叶树木LeWos模型74.8654.9566.8948.09
Tlseparotion模型73.9178.8859.3738.39
高斯合模型72.5276.8163.8445.73
本文方法91.4592.7885.4678.96
表5  精确度、枝干F分数、叶片F分数和kappa系数平均值比较 (%)
TreeID一类错误二类错误
LeWos模型Tlseparation模型高斯混合模型本文方法LeWos模型Tlseparation模型高斯混合模型本实验方法
Tree112.0724.142.245.8020.7645.4235.2319.24
Tree236.3643.1332.8022.490.2213.8213.190.21
Tree341.5837.2333.5516.230.1723.576.574.35
Tree432.3512.0733.215.600.2020.7617.260.91
Tree550.3231.5636.713.420.267.726.273.39
Tree676.6056.7982.5023.710.182.990.011.23
Tree742.2558.7141.3834.9510.3531.0110.249.23
Tree830.3649.4041.113.360.0521.053.3211.61
Tree919.7536.0330.738.021.5825.8414.271.40
Tree1039.9852.5630.435.951.7535.383.4419.57
Tree1113.3914.1733.436.192.0629.5431.011.92
Tree1212.2527.1131.360.792.6937.3436.573.00
平均33.9436.9135.7911.383.3624.5414.786.34
表6  一类错误和二类错误比较 (%)
图5  6棵树木样本4种模型枝叶分离结果精确度
图6  6颗树木样本4种模型枝叶分离结果枝干F分数
图7  6颗树木样本4种模型枝叶分离结果叶片F分数
图8  6颗树木样本4种模型枝叶分离结果叶片kappa系数
图9  三个样本枝叶分离错误分布(第一列为Lewos模型,第二列为Tlseparation模型,第三列为高斯混合模型,第四列为本文方法。棕色点为分类正确枝干的点,绿色点为分类正确叶片的点,蓝色点为分类错误的枝干点,红色点为分类错误的叶片点)
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