Full-Waveform Classification and Segmentation-Based Signal Detection of Single-Wavelength Bathymetric LiDAR

Single-wavelength bathymetric light detection and ranging (LiDAR) (532 nm) can provide seamless meter- and submeter-scale digital elevation model (DEMs) of both the terrestrial surface and seafloor. However, mixed terrestrial and bathymetric surfaces obtained by this sensor are challenging for full-waveform (FW) signal detection. This study addresses the issues in two FW mixed surfaces: accurate classification of terrestrial and nonterrestrial waveforms from the original waveforms without auxiliary information and flexible detection of peaks based on a new FW theoretical model. A novel FW signal detection model (FWSD) for single-wavelength bathymetric LiDAR is proposed without complex feature extraction and iterative procedure through waveform classification and segmentation. The raw FWs are divided into five categories for subsequent signal detection using a convolutional neural network that merges local descriptors with contextual information. The signal detection task is then split into FW segment recognition and peak extraction using a new FW model, which integrates a leapfrog sliding window FW segmentation, an improved extreme learning machine (ELM) algorithm for FW segment recognition, and a flexible signal detection framework. To search for the optimal initial parameters for ELM, a self-annealing particle swarm optimization (SAPSO) algorithm is introduced, and the output weight is adjusted by online sequence to improve its generalization. When combined with the Richardson–Lucy deconvolution (RLD) algorithm, FWSD can be adapted to deal with shallow water waveforms. Finally, a test demonstration with an airborne dataset shows that FWSD has higher detection efficiency and higher accuracy than Levenberg–Marquardt algorithm optimized generalized Gaussian model (LM-GGM) and RLD algorithm.