Classification of Individual Tree Species Using UAV LiDAR Based on Transformer

Tree species surveys are crucial in forest resource management and can provide references for forest protection policymakers. Traditional tree species surveys in the field are labor-intensive and time-consuming. In contrast, airborne LiDAR technology is highly capable of penetrating forest vegetation; it can be used to quickly obtain three-dimensional information regarding vegetation over large areas with a high level of precision, and it is widely used in the field of forestry. At this stage, most studies related to individual tree species classification focus on traditional machine learning, which often requires the combination of external information such as hyperspectral cameras and has difficulty in selecting features manually. In our research, we directly processed the point cloud from a UAV LiDAR system without the need to voxelize or grid the point cloud. Considering that relationships between disorder points can be effectively extracted using Transformer, we explored the potential of a 3D deep learning algorithm based on Transformer in the field of individual tree species classification. We used the UAV LiDAR data obtained in the experimental forest farm of Northeast Forestry University as the research object, and first, the data were preprocessed by being denoised and ground filtered. We used an improved random walk algorithm for individual tree segmentation and made our own data sets. Six different 3D deep learning neural networks and random forest algorithms were trained and tested to classify the point clouds of three tree species. The results show that the overall classification accuracy of PCT based on Transformer reached up to 88.3%, the kappa coefficient reached up to 0.82, and the optimal point density was 4096, which was slightly higher than that of the other deep learning algorithms we analyzed. In contrast, the overall accuracy of the random forest algorithm was only 63.3%. These results show that compared with the commonly used machine learning algorithms and a few algorithms based on multi-layer perceptron, Transformer-based networks provide higher accuracy, which means they can provide a theoretical basis and technical support for future research in the field of forest resource supervision based on UAV remote sensing.