Decomposition of full-waveform LiDAR data utilizing an adaptive B-spline-based model and particle swarm optimization

Abstract

High-precision waveform decomposition is crucial for LiDAR applications. Existing methods encounter challenges including poor target detection and low accuracy in extracting parameters of irregular components, especially in complex echoes. We introduce an adaptive B-spline-based decomposition (AdaptB-spline) method, which uses B-spline curves to adaptively adjust the shape and position of component through the particle swarm optimization (PSO); and proposes an initial parameter estimation method based on the B-spline and Richardson-Lucy (RL) deconvolution, which improves the noise immunity and component detection. Experiments were conducted on synthetic waveforms and satellite LiDAR waveforms by AdaptB-spline and other four methods (Gaussian (Gauss), B-spline-based (B-spline), skew-normal (SkewN), and multi-Gaussian (MultiGauss) decomposition). We concluded that AdaptB-spline exhibits superior performance in terms of component RMSE, CC, R2, component parameter error and range error metrics compared to the four methods. So AdaptB-spline can enhance component detection and accurately fit Gaussian or non-Gaussian waveforms, demonstrating outstanding target detection and ranging precision.