A Novel Lidar Data Classification Algorithm Combined Densenet with STN

Light detection and ranging (LiDAR) data is a very important type of data used for terrain classification. The traditional convolutional neural network (CNN) has insufficient effective transmission of features and gradients in feature classification and can only set a fixed input size by experience. In this paper, spatial transformation network (STN) and densely connected convolutional network (DenseNet) are combined to form STN-DenseNet, which makes the input data adaptively deform according to the network needs, making full use of all information from the front layers of the network. Thus the transmission of features and gradients are more effective. The proposed framework performs experiments on two LiDAR-DSM datasets (i.e. Bayview Park and Recology datasets). The results show that, comparing with the traditional deep convolution model, STN-DenseNet can improve the classification accuracy of LiDAR-DSM data.