期刊
MEASUREMENT
卷 177, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.measurement.2021.109301
关键词
LiDAR; Point cloud; 3D deep learning; Tree species classification
资金
- National Key Research and Development Program of China [2018YFB0504504]
- National Natural Science Foundation of China [41730107, 41671414]
- Chinese Academy of Surveying and Mapping [AR1920]
Accurate tree species identification is crucial for ecological evaluation and other forest applications. The proposed LayerNet, a point-based deep neural network, shows significant advantages in tree species classification tasks, with the highest classification accuracy reaching 92.5% through 3D data processing and classification.
Accurate tree species identification is essential for ecological evaluation and other forest applications. In this paper, we proposed a point-based deep neural network called LayerNet. For light detection and ranging (LiDAR) data in forest regions, the network can divide multiple overlapping layers in Euclidean space to obtain the local three-dimensional (3D) structural features of the tree. The features of all layers are aggregated, and the global feature is obtained by convolution to classify the tree species. To validate the proposed framework, multiple experiments, including airborne and ground-based LiDAR datasets, are conducted and compared with several existing tree species classification algorithms. The test results show that LayerNet can directly use 3D data to accurately classify tree species, with the highest classification accuracy of 92.5%. Also, the results of comparative experiments demonstrate that the proposed framework has obvious advantages in classification accuracy and provides an effective solution for tree species classification tasks.
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