PointCNN-Based Individual Tree Detection Using LiDAR Point Clouds

Due to the rapid development of deep learning technology in recent years, many scholars have applied deep learning technology to the field of remote sensing imagery. But few have directly applied LiDAR point clouds to 3D neural networks for tree detection. And the existingmethods usually have better detection results in a specific single scene, but in some complex scenes, such as containing diverse types of trees, urban forests and high forest density, the detection results are not satisfactory. Therefore, this paper presents a PointCNN-based method of 3D tree detection using LiDAR point clouds, which aims to improve the detection accuracy of trees in complex scenes and versatility. This method first builds a canopy height model (CHM) using raw LiDAR point clouds and obtains rough seed points on CHM. Then it extracts the detection samples consisting of single tree's point cloud data based on the rough seed points. Next, the 3D-CNN classifier based on PointCNN is adopted to classify detection samples, and the classification results are used for filtering seed points. Finally, our method performs the tree stagger analysis on those close seed points. This study selected twelve experimental plots from study areas in Bend, Central Oregon, USA. Based on the results of our experiments, the highest matching score and average score reached 91.0 and 88.3. Experimental results show that our method can effectively extract tree information in complex scenes.