Faint Bottom Echo Detection for Airborne LiDAR Bathymetry Based on a Constrained Waveform Stacking Model

Airborne LiDAR bathymetry is particularly suited for acquiring three-dimensional spatial information in shallow water zones. However, its signal undergoes rapid attenuate in the water column, resulting in faint bottom echoes that are challenging to detect and directly limit the maximum bathymetric performance. To achieve accurate extraction of these echoes, a faint bottom echo detection method based on a constrained waveform stacking model is proposed. The model employs distances between water-surface points and their refraction vectors as dual constraints to filter spatially adjacent waveforms. The filtered waveforms are then utilized to construct a pseudo waveform with high signal-to-noise ratio by waveform stacking. On this basis, the accurate faint bottom echo moment is subsequently determined based on a normalized peak significance score. The proposed method is validated using two Mapper-20KU datasets acquired from a coastal zone and an inland reservoir in Qingdao, China. The results demonstrate that the method increases the maximum bathymetric depth by approximately 50% in both regions and significantly improves the point density. Compared with existing topographic data, the root mean square errors (RMSEs) of the derived bathymetric points are 0.22 m and 0.09 m, respectively. Therefore, the proposed method can significantly enhance the maximum bathymetric depth while ensuring the bathymetric accuracy.