Airborne LiDAR data tree species classification method based on three-dimensional deep learning

The invention discloses an airborne LiDAR data tree species classification method based on three-dimensional deep learning, and belongs to the technical field of airborne laser LiDAR point cloud dataclassification. The method comprises the following steps: loading airborne LiDAR data; removing noise points of the airborne LiDAR data, and filtering out ground points; performing individual tree segmentation through a watershed segmentation algorithm and a segmentation algorithm based on a point cloud distance to form individual tree point cloud data; making a deep learning sample data set for the single tree point cloud data obtained after segmentation, and dividing the data set into a training set and a test set; performing feature abstraction on the training set by using a deep learning network and completing training; and using the trained network model to classify the test set tree species. The method is simple and efficient, and the point cloud does not need to be subjected to voxel segmentation or multi-v