Water Bottom and Surface Classification Algorithm for Bathymetric LiDAR Point Clouds of Very Shallow Waters - Hyejin Kim; Jaehoon Jung; Jaebin Lee; Gwangjae Wie (Canadian Journal of Remote Sensing, 2023)
The absence of accurate point classification limits the effective use of airborne bathymetric LiDAR (ABL) data for coastal zone mapping. In this study, we propose a classification approach using a custom waveform decomposition technique with the pseudo-waveform generated from ABL point cloud data. Initially, the input point clouds were organized into a 2D grid. Next, the points that fall into a grid cell were organized into a histogram using Z-values to generate the pseudo-waveform. Subsequently, the pseudo-waveform was decomposed into water bottom, column, surface, and noise components using a custom multiple Gaussian curve fitting method. The proposed approach was evaluated with datasets acquired in Florida, USA, using a Riegl VQ-880-G ABL system. With an optimized parameter set, the proposed approach achieved F1 score of 98.944% for the classification of water bottom and an overall accuracy of 91.234% for all the classes. Further, the proposed approach was evaluated with datasets acquired in South Korea using a Seahawk system and compared against MBES data, demonstrating that the water bottom was successfully classified with a vertical error of 0.049 ± 0.167 m.