The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (Jun 2019)

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

  • H. Guan,
  • Y. Yu,
  • W. Yan,
  • D. Li,
  • J. Li

DOI
https://doi.org/10.5194/isprs-archives-XLII-2-W13-989-2019
Journal volume & issue
Vol. XLII-2-W13
pp. 989 – 993

Abstract

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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.