Water–land classification using three-dimensional point cloud data of airborne LiDAR bathymetry based on elevation threshold intervals

Water–land classification is a basis for water depth calculation or suspended sediment concentration inversion through airborne LiDAR bathymetry (ALB). Traditional classification methods using ALB waveform data offer high accuracy but exhibit low efficiency and convenience in engineering applications. The three-dimensional (3-D) point cloud data of ALB are easier to analyze and utilize than waveform data. Therefore, we propose a water–land classification method that uses the 3-D point cloud data of ALB based on the threshold intervals of water surface points. First, a random sample consensus algorithm is applied to rough water–land classification using the 3-D point cloud data derived by an infrared laser of ALB. Second, the water surface points derived from rough classification are used to determine the means, standard deviations, and threshold intervals. Finally, accurate water–land classification is achieved on the basis of the threshold intervals of the water surface points. The proposed method is applied to a practical ALB measurement using Optech coastal zone mapping and imaging LiDAR and achieves 98.26% accuracy in water–land classification.