FWNetAE: Spatial Representation Learning for Full Waveform Data Using Deep Learning

Deep learning methods that directly handle three-dimensional point clouds, e.g., PointNet, have recently been proposed. Moreover, deep learning-based-techniques have demonstrated excellent performance for supervised learning tasks on point clouds such as classification and segmentation for certain open datasets. In this study, the possibility of using a deep learning method, which is an unsupervised representation learning method to extract features from airborne raw full-waveform data, is investigated. Thus, a novel end-to-end autoencoder network called FWNetAE is proposed to address representation learning challenges on raw full-waveform data. On the encoder side, a PointNet-based network extracts the latent vector of raw full-waveform data. Subsequently, a fully connected network-based decoder deforms the latent vector into the input full-waveform light detection and ranging (LiDAR) data, thus achieving fewer reconstruction errors.