Faint Echo Extraction from ALB Waveforms Using a Point Cloud Semantic Segmentation Model

Abstract

As an active remote sensing technology, airborne LIDAR can work at all times while emitting specific wavelengths of laser light that can penetrate seawater. Airborne LIDAR bathymetry (ALB) records an object’s full return waveform, including the water surface, water column, seafloor, and the objects on it. Due to the seawater’s absorption and scattering and the seafloor’s reflectivity effect, the seafloor’s amplitude of seafloor echoes varies greatly. Seafloor echoes with low signal-to-noise ratios are not easily detected using waveform processing methods, which can lead to insufficient seafloor topography depth and incomplete seafloor topography coverage. To extract faint seafloor echoes, we proposed a depth extraction method based on the PointConv deep learning model, called FWConv. The method assumed that spatially adjacent echoes were correlated. We converted all the spatially adjacent multi-frame waveforms into a point cloud. Each point represented a bin value in the waveform, and the points’ properties contained spatial coordinates and the amplitude in the waveform. In the semantic segmentation of these point clouds using deep learning models, we considered not only each centroid’s amplitude, but also its neighboring points’ distance and amplitude. This enriched the centroids’ features and allowed the model to better discriminate between background noise and seafloor echoes. The results showed that FWConv could extract faint seafloor echoes in the experimental area and was not easily affected by noise, and that the correctness reached 99.82%. The number of point clouds increased by 158%, and the seafloor elevation accuracy reached 0.20 m concerning the multibeam echo sounder data.