Wood-leaf classification of tree point cloud based on intensity and geometrical information
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-ste...
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Zusammenfassung: | 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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DOI: | 10.48550/arxiv.2108.01002 |