Application of Landweber with Optimization for Small Footprint Waveform Lidar Decomposition

Small-footprint waveform LiDAR requires waveform decomposition for accurate target structure characterization. To improve the ability for identifying close targets, this paper first introduces the Landweber (LW) deconvolution method to decompose the small-footprint LiDAR waveforms. Our study emphasizes the advantages of the deconvolution methods in capturing more targets of waveforms. Generally, the LW approach introduced with optimization excels in detecting more targets after false target removal. Experiments were conducted on datasets collected under various conditions using small-footprint waveform LiDAR system. The findings highlight an average target distance error of 0.083m, showcasing superior performance compared to direct decomposition methods. When compared with the GOLD and RL methods, the decomposition accuracy is nearly indistinguishable, but the success rates are higher. Our research establishes the LW method as a viable waveform decomposition method, contributing to the diversity of choices for waveform data processing.