Unsupervised Learning of ALS Point Clouds for 3-D Terrain Scene Clustering

Terrain scene clustering is a class of unsupervised methods for choosing suitable algorithms or parameters for airborne laser scanning (ALS) point cloud processing. Most existing point cloud clustering methods use hand-crafted features, such as viewpoint feature histogram (VFH), as the input of clustering algorithms. However, few studies on point cloud processing focused on terrain scene clustering via an unsupervised deep neural network. In the present study, we create a data set for terrain scene clustering in ALS point clouds. We also propose DPCC-Net, a deep point cloud clustering network via unsupervised deep learning that jointly learns the parameters of the network and the cluster task of extracted features. DPCC-Net iteratively groups the features extracted by the deep convolution neural network with the ${k}$ -means algorithm and uses the clustering result as the pseudo label to update the parameters of the network. We apply the proposed DPCC-Net to unsupervised training on a large terrain scene data set. The clustering result of DPCC-Net outperforms those of other typical methods.