文献类型：期刊文献

英文题名：Tree Species Classification Using Ground-Based LiDAR Data by Various Point Cloud Deep Learning Methods

作者：Liu, Bingjie[1] Huang, Huaguo[1] Su, Yong[2] Chen, Shuxin[3] Li, Zengyuan[3] Chen, Erxue[3] Tian, Xin[3]

第一作者：Liu, Bingjie

通信作者：Tian, X[1]

机构：[1]Beijing Forestry Univ, Res Ctr Forest Management Engn, State Forestry & Grassland Adm, Beijing 100083, Peoples R China;[2]Southwest Forestry Univ, Coll Forestry, Kunming 650224, Yunnan, Peoples R China;[3]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China

年份：2022

卷号：14

期号：22

外文期刊名：REMOTE SENSING

收录：;EI(收录号：20224813189764);Scopus(收录号：2-s2.0-85142769284);WOS:【SCI-EXPANDED(收录号:WOS:000887606100001)】；

基金：This research was funded by the Cooperation Project Between Zhejiang Province and Chinese Academy of Forestry in Forestry Science and Technology (Grant number: 2020SY02), the National Science and Technology Major Project of China's High Resolution Earth Observation System (Grant number: 21-Y20B01-9001-19/22), the National Natural Science Foundation of China (Grant number: 42130111 and 41930111), the National Key Research and Development Program of China (Grant number: 2021YFE0117700), the National Natural Science Foundation of China (Grant number: 41871279), and the Fundamental Research Funds of CAF (Grant number: CAFYBB2021SY006).

语种：英文

外文关键词：tree species classification; point cloud deep learning; multi-layer perceptron; convolution; graph; attention; transformer

摘要：Tree species information is an important factor in forest resource surveys, and light detection and ranging (LiDAR), as a new technical tool for forest resource surveys, can quickly obtain the 3D structural information of trees. In particular, the rapid and accurate classification and identification of tree species information from individual tree point clouds using deep learning methods is a new development direction for LiDAR technology in forest applications. In this study, mobile laser scanning (MLS) data collected in the field are first pre-processed to extract individual tree point clouds. Two downsampling methods, non-uniform grid and farthest point sampling, are combined to process the point cloud data, and the obtained sample data are more conducive to the deep learning model for extracting classification features. Finally, four different types of point cloud deep learning models, including pointwise multi-layer perceptron (MLP) (PointNet, PointNet++, PointMLP), convolution-based (PointConv), graph-based (DGCNN), and attention-based (PCT) models, are used to classify and identify the individual tree point clouds of eight tree species. The results show that the classification accuracy of all models (except for PointNet) exceeded 0.90, where the PointConv model achieved the highest classification accuracy for tree species classification. The streamlined PointMLP model can still achieve high classification accuracy, while the PCT model did not achieve good accuracy in the tree species classification experiment, likely due to the small sample size. We compare the training process and final classification accuracy of the different types of point cloud deep learning models in tree species classification experiments, further demonstrating the advantages of deep learning techniques in tree species recognition and providing experimental reference for related research and technological development.