文献类型：会议论文

英文题名：Tree Species Classification of Point Clouds from Different Laser Sensors Using the PointNet++ Deep Learning Method

作者：Liu, Bingjie[1] Huang, Huaguo[1] Chen, Shuxin[2] Tian, Xin[2] Ren, Min[3]

第一作者：Liu, Bingjie

机构：[1] Beijing Forestry University, State Forest. and Grass. Admin. Key Laboratory of Forest Resources and Environmental Management, China; [2] Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, China; [3] China Mobile Group Shanxi Design Institute Co., Ltd., China

会议论文集：IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings

会议日期：July 16, 2023 - July 21, 2023

会议地点：Pasadena, CA, United states

语种：英文

外文关键词：deep learning; light detection and ranging (LiDAR); point cloud; tree species classification

年份：2023

摘要：Tree species information is a crucial factor in forest resource inventory. Light detection and ranging (LiDAR), as an emerging active remote sensing technology, has unique advantages in extracting three-dimensional (3-D) vegetation structure information, and its application in forest resource assessment and research is gaining increasing attention. Airborne laser scanning (ALS), unmanned aerial vehicle laser scanning (UAVLS) and terrestrial laser scanning (TLS) are important means to acquire 3-D forest data. The challenge of traditional machine learning based tree classification lies in extracting and selecting numerous key diagnostic features from large amounts of LiDAR data, requiring extensive feature extraction expertise, which limits its scalability. The use of deep learning methods for fast and accurate classification and identification of tree species in individual tree point clouds represents a new development direction of LiDAR technology in forest resource inventory applications. In this study, PointNet++ was used to classify tree species by point cloud data obtained from TLS, ALS and UAVLS, respectively. The research results show that high accuracy in tree species classification can be achieved by using point cloud deep learning methods. ? 2023 IEEE.