Point cloud segmentation for an individual tree combining improved point transformer and hierarchical clustering

Individual tree segmentation of forestry point cloud data is of great significance to forest management and resource detection, because it can quickly and efficiently extract tree parameters and calculate biomass. Although research on individual tree segmentation of forestry point cloud data has made great progress, there are still many problems. For example, it is difficult to separate two trees when they are close to each other or occluded. In this work, a point cloud segmentation method is proposed to obtain an individual tree from forest plantation datasets, which combines improved point transformer and hierarchical clustering method. First, we use the improved point transformer to remove the ground and non-tree data to obtain pure tree point cloud data. Second, the tree point cloud data are converted to digital surface model and the watershed segmentation algorithm is used for the preliminary segmentation. Subsequently, a merging algorithm is proposed to merge the missing segmented point cloud data at the edge of the point cloud with the successfully segmented point cloud data, according to the nearest point cloud category. However, the results after the merging algorithm still have trees that cannot be segmented. Finally, a hierarchical clustering method is proposed for fine segmentation. For the improved point transformer, we utilized three regions for verification and three regions for testing. The mean intersection over union (MIOU) of the improved point transformer on the test set is 0.976, which is 1.1% higher than that of the original point transformer. For individual tree segmentation, we tested on five regions and obtained a MIOU of 0.742. The results demonstrate that the method proposed in this work can achieve better individual tree segmentation than other methods.