Wood-leaf classification of tree point cloud based on intensity and geometrical information

• URL: http://arxiv.org/abs/2108.01002v1
• Date: Mon, 2 Aug 2021 16:04:48 GMT
• Title: Wood-leaf classification of tree point cloud based on intensity and geometrical information
• Authors: Jingqian Sun, Pei Wang, Zhiyong Gao, Zichu Liu, Yaxin Li, Xiaozheng Gan
• Abstract summary: Terrestrial laser scanning (TLS) can obtain tree point cloud with high precision and high density. Efficient classification of wood points and leaf points is essential to study tree structural parameters and ecological characteristics. Three-step classification and verification method was proposed to achieve automated wood-leaf classification.
• Score: 22.738466338122418
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Terrestrial laser scanning (TLS) can obtain tree point cloud with high precision and high density. Efficient classification of wood points and leaf points is essential to study tree structural parameters and ecological characteristics. By using both the intensity and spatial information, a three-step classification and verification method was proposed to achieve automated wood-leaf classification. Tree point cloud was classified into wood points and leaf points by using intensity threshold, neighborhood density and voxelization successively. Experiment was carried in Haidian Park, Beijing, and 24 trees were scanned by using the RIEGL VZ-400 scanner. The tree point clouds were processed by using the proposed method, whose classification results were compared with the manual classification results which were used as standard results. To evaluate the classification accuracy, three indicators were used in the experiment, which are Overall Accuracy (OA), Kappa coefficient (Kappa) and Matthews correlation coefficient (MCC). The ranges of OA, Kappa and MCC of the proposed method are from 0.9167 to 0.9872, from 0.7276 to 0.9191, and from 0.7544 to 0.9211 respectively. The average values of OA, Kappa and MCC are 0.9550, 0.8547 and 0.8627 respectively. Time cost of wood-leaf classification was also recorded to evaluate the algorithm efficiency. The average processing time are 1.4 seconds per million points. The results showed that the proposed method performed well automatically and quickly on wood-leaf classification based on the experimental dataset.

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