Waveform decomposition using the Levenberg–Marquardt algorithm is a powerful tool for detecting surface return, bottom return, and volume backscatter return in a superposed green waveform of airborne LiDAR bathymetry (ALB). However, traditional decomposition methods do not handle bound constraints and are easily trapped in local optimum. Thus, the decomposed components may be inconsistent with the measurement principle of ALB. This study proposes an improved waveform decomposition method by setting reasonable lower and upper bounds of waveform parameters to guarantee the fidelity of the decomposed components. First, a comprehensive mathematical model of a green waveform is proposed by considering the early return. Second, the lower and upper bounds of the waveform parameters are given on the basis of the measurement principle of ALB. Finally, improved waveform decomposition is achieved using the comprehensive model and a constrained nonlinear optimization. The proposed method is applied to a practical ALB measurement using Optech coastal zone mapping and imaging LiDAR. Compared with traditional decomposition methods, the improved waveform decomposition not only ensures good fitness but also guarantees the fidelity of the decomposed components.
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.
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