An Automatic Method for Tree Species Point Cloud Segmentation Based on Deep Learning

Tree species segmentation is an essential condition for research forestry and has a large impact on forest resource monitoring, sustainable forest management, and biodiversity research. Recently, the development of hardware and software has been rapidly increasing. Regarding hardware, the active remote sensing system LiDAR can be used to obtain many point clouds and can significantly improve the tree segmentation accuracy compared with traditional optical remote sensing hardware. With respect to software, deep learning theory is effectively utilized to process 3D point clouds, such as extracting the features of data. However, deep learning-based methods are underutilized in tree species point cloud segmentation. Therefore, it is extremely important to combine current technological advantages for this application. In this article, we construct a point cloud processing dataset that comprises substantial tree information and 5 tree species, including willow, fir, bamboo, palm, and rubber. The novel representation of point clouds via a superpoint graph is utilized to pre-process the point clouds in a large outdoor scene. We propose to apply state-of-the-art deep learning frameworks, including PointNet network and graph convolution networks, to process tree species point clouds in complex forest scenes. We also discuss the effectiveness of the method and the situations influenced by different parameters. The experimental results finally verify the effectiveness of the framework in tree species segmentation.