05 Jun 2019
05 Jun 2019
3D-CNN BASED TREE SPECIES CLASSIFICATION USING MOBILE LIDAR DATA
H. Guan1, Y. Yu2, W. Yan3, D. Li4, and J. Li5
H. Guan et al.
H. Guan1, Y. Yu2, W. Yan3, D. Li4, and J. Li5
- 1School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing, 210044, China
- 2Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, 223003, China
- 3School of Geographical Science, Nanjing University of Information Science & Technology, Nanjing, 210044, China
- 4State Key laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, China
- 5Department of Geography and Environmental Management, University of Waterloo, Waterloo, ON N2L 3G1, Canada
- 1School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing, 210044, China
- 2Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, 223003, China
- 3School of Geographical Science, Nanjing University of Information Science & Technology, Nanjing, 210044, China
- 4State Key laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, China
- 5Department of Geography and Environmental Management, University of Waterloo, Waterloo, ON N2L 3G1, Canada
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Keywords: Mobile LiDAR Data, Tree Segmentation, Voxelization, NCut, Supervoxels, 3D-CNN
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.
