A Satellite Full-Waveform Laser Decomposition Method for Forested Areas Based on Hidden Peak Detection and Adaptive Genetic Optimization

Abstract

Laser waveform data that contain rich three-dimensional structural object information hold significant value in forest resource monitoring. However, traditional waveform decomposition algorithms are often constrained by complex waveform structures and depend on the initial parameter selections, which affect the accuracy and robustness of the results. To address the issues of the strong dependence on initial parameters, susceptibility to local optima, and difficulty in detecting hidden peaks during waveform overlap in the traditional satellite laser waveform decomposition algorithms, this study proposes a waveform decomposition method that combines hidden peak detection and an adaptive genetic algorithm (HAGA). This method uses hidden peak detection algorithms to improve the accurate extraction of the Gaussian components from the original waveform and provides the initial parameters. The high-precision extraction of waveform parameters is achieved through the adaptive genetic algorithm (AGA) combined with Levenberg–Marquardt (LM) optimization. In the experimental validation, the proposed method outperformed the traditional methods in both waveform decomposition fitting accuracy and tree height extraction. The average waveform decomposition accuracy Rmean2 for more than 2000 laser spots reaches 0.955, whereas the RMSE of the tree height extractions is better than 2 m, demonstrating strong robustness and applicability.

Keywords

• full-waveform LiDAR
• waveform decomposition
• Levenberg-Marquardt optimization
• tree height inversion