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

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

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.

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Zitierform:

Guan, H. / Yu, Y. / Yan, W. / et al: 3D-CNN BASED TREE SPECIES CLASSIFICATION USING MOBILE LIDAR DATA. 2019. Copernicus Publications.

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