Airborne LiDAR Point Cloud Classification Based on Transfer Learning

The classification of airborne LiDAR point cloud is one of the key procedure for its further processing and application. Aiming at the difficulty of obtaining high classification accuracy and reducing processing time simultaneously, a transfer learning-based method for classifying airborne LiDAR point cloud is proposed. Firstly, three types of low-level features, i.e. normalized height, intensity and point cloud normal vector are calculated for each LiDAR point, by setting different size of neighborhood, multi-scale point cloud feature images are generated by utilizing the proposed feature image generation method. Then, a pre-trained deep residual network is employed to extract multi-scale deep features from the generated multi-scale feature images. At last, a neural network model containing only two fully connected layers is constructed to achieve being trained efficiently, and point cloud is classified by the trained optimal neural network model. Two International Society for Photogrammetry and Remote Sensing benchmark airborne LiDAR point cloud sets are used in our experiment, the results demonstrate that our method requires less training time, and can obtain 85.9% overall classification accuracy, which can provide reliable information for further processing and application of point cloud.