Wood–Leaf Classification of Tree Point Cloud Based on Intensity and Geometric Information
| Autor: | Yaxin Li, Zichu Liu, Jingqian Sun, Zhongnan Liu, Xiaozheng Gan, Zhiyong Gao, Pei Wang |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
wood–leaf separation
automation intensity point density three-step classification verification Scanner business.industry Science Point cloud Pattern recognition Matthews correlation coefficient Tree (data structure) Cohen's kappa General Earth and Planetary Sciences Point (geometry) Artificial intelligence business Kappa Intensity (heat transfer) Mathematics |
| Zdroj: | Remote Sensing; Volume 13; Issue 20; Pages: 4050 Remote Sensing, Vol 13, Iss 4050, p 4050 (2021) |
| ISSN: | 2072-4292 |
| Popis: | Terrestrial laser scanning (TLS) can obtain tree point clouds with high precision and high density. The efficient classification of wood points and leaf points is essential for the study of tree structural parameters and ecological characteristics. Using both intensity and geometric information, we present an automated wood–leaf classification with a three-step classification and wood point verification. The tree point cloud was classified into wood points and leaf points using intensity threshold, neighborhood density and voxelization successively, and was then verified. Twenty-four willow trees were scanned using the RIEGL VZ-400 scanner. Our results were compared with the manual classification results. To evaluate the classification accuracy, three indicators were introduced into the experiment: overall accuracy (OA), Kappa coefficient (Kappa), and Matthews correlation coefficient (MCC). The ranges of OA, Kappa, and MCC of our results were from 0.9167 to 0.9872, 0.7276 to 0.9191, and 0.7544 to 0.9211, respectively. The average values of OA, Kappa, and MCC were 0.9550, 0.8547, and 0.8627, respectively. The time costs of our method and another were also recorded to evaluate the efficiency. The average processing time was 1.4 s per million points for our method. The results show that our method represents a potential wood–leaf classification technique with the characteristics of automation, high speed, and good accuracy. |
| Databáze: | OpenAIRE |
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