3D-CNN BASED TREE SPECIES CLASSIFICATION USING MOBILE LIDAR DATA

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Li, J.^5¹  Yu, Y.^2²  Yan, W.^3³  Guan, H.^1⁴  Li, D.^4⁵ 

[1] Department of Geography and Environmental Management, University of Waterloo, Waterloo, ON N2L 3G1, Canada^5;Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, 223003, China^2;School of Geographical Science, Nanjing University of Information Science & Technology, Nanjing, 210044, China^3;School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing, 210044, China^1;State Key laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, China^4

关键词: Mobile LiDAR Data;    Tree Segmentation;    Voxelization;    NCut;    Supervoxels;    3D-CNN;   

DOI  :  10.5194/isprs-archives-XLII-2-W13-989-2019

学科分类：地球科学(综合)

来源: Copernicus Publications

【 摘 要 】

Our work addresses the problem of classifying tree species from mobile LiDAR data. The work is a two step-wise strategy, including tree segmentation and tree species classification. In the tree segmentation step, a voxel-based upward growing filtering is proposed to remove terrain points from the mobile laser scanning data. Then, individual trees are segmented via a Euclidean distance clustering approach and Voxel-based Normalized Cut (VNCut) segmentation approach. In the tree species classification, a voxel-based 3D convolutional neural network (3D-CNN) model is developed based on intensity information. A road section data acquired by a RIEGL VMX-450 system are selected for evaluating the proposed tree classification method. Qualitative analysis shows that our algorithm achieves a good performance.

【 授权许可】

CC BY