Discriminating Forest Leaf and Wood Components in TLS Point Clouds at Single-Scan Level Using Derived Geometric Quantities
Discriminating Forest Leaf and Wood Components in TLS Point Clouds at Single-Scan Level Using Derived Geometric Quantities
复制标题
使用导出的几何量在单扫描级别区分 TLS 点云中的森林叶子和木材成分
DOI：
10.1109/tgrs.2021.3121256
复制
发表时间：
2022
影响因子：
8.2
通讯作者：
Songbo Wu
中科院分区：
文献类型：
--
作者：
Kai Tan;Ke Tao;Tao Pengjie;Kunbo Liu;Yansong Duan;Weiguo Zhang;Songbo Wu
判别地面激光扫描（TLS）点云中的叶和木材成分是准确估计单个树木和整个森林的3-D结构和生物物理属性的先决条件。然而，大多数现有的分离方法是在本地进行的（即，个人或情节）水平。局部水平分离方法需要对获取的点云进行预分割，森林遮挡效应和点云质量对分离的精度和可靠性有很大影响。提出了一种新的广义方法，仅基于几何特征的差异，包括曲率，密度，和显着的特点，在这项研究中提出了在TLS单扫描水平分离叶和木材成分。使用正常变化率的量（即，表面变化），因为叶点经常表现出急剧的局部曲率变化。然后，基于校准的密度数据（即，给定半径中的点的数量），因为叶子的分散方向和小尺寸。最后，提出了一种新的自调整连通性分割算法，将剩余点分成不同的类。叶簇和木簇根据显著特征和尺寸同时分离。结果表明，从单个点和分割集群的曲率、密度和显着特征中推导出的几何量可以联合用于在单次扫描TLS点云中有效且鲁棒地区分树叶和木材成分，平均总体准确度约为93%。此外，实验结果表明，所提出的方法在对距离、仪器类型、遮挡效应和森林组成的不敏感性方面表现出良好的性能。
Discriminating leaf and wood components in terrestrial laser scanning (TLS) point clouds is a prerequisite for accurately estimating 3-D structural and biophysical attributes of both individual trees and entire forests. However, most existing separation methods are conducted at local (i.e., individual or plot) level. The local level separation methods need a presegmentation of the acquired point clouds, and the separation accuracy and reliability are greatly influenced by forest occlusion effect and point cloud qualities. A new generalized method merely based on differences in geometric features, including curvature, density, and salient features, is proposed in this study for separating leaf and wood components at the TLS single-scan level. A preliminary separation is conducted using the quantity of normal change rate (i.e., surface variation) given that leaf points often demonstrate sharp local curvature changes. Then, separation is continually conducted on the basis of calibrated density data (i.e., number of points in a given radius) because of the scattered orientations and small sizes of leaves. Finally, a new self-adjusting connectivity segmentation algorithm is proposed to group remaining points into different clusters. Leaf and wood clusters are separated in accordance with salient features and sizes simultaneously. Results indicate that derived geometric quantities from curvature, density, and salient features of individual points and segmented clusters can be jointly used to discriminate leaf and wood components effectively and robustly in single-scan TLS point clouds with a mean overall accuracy of approximately 93%. In addition, results show good performance in terms of the insensitivity to distance, instrument type, occlusion effect, and forest composition of the proposed method.
登录
查看更多内容
影响因子：
5
作者：
Kaasalainen, Sanna;Jaakkola, Anttoni;Kukko, Antero
通讯作者：
Kukko, Antero
DOI：
10.1016/j.isprsjprs.2012.10.003
发表时间：
2013-01-01
影响因子：
12.7
作者：
Kankare, Ville;Holopainen, Markus;Alho, Petteri
通讯作者：
Alho, Petteri
影响因子：
6.6
作者：
Di Wang;S. Momo Takoudjou;E. Casella
通讯作者：
Di Wang;S. Momo Takoudjou;E. Casella
DOI：
10.1016/j.isprsjprs.2015.10.001
发表时间：
2015-12-01
影响因子：
12.7
作者：
Zhu, Xi;Wang, Tiejun;Niemann, K. Olaf
通讯作者：
Niemann, K. Olaf
DOI：
10.1109/tgrs.2018.2866056
发表时间：
2019-03-01
影响因子：
8.2
作者：
Jin, Shichao;Su, Yanjun;Guo, Qinghua
通讯作者：
Guo, Qinghua