LAUSR

Full waveform satellite lidar can be used to retrieve the terrestrial surface information by decomposing its received waveforms. However, it is challenging to accurately extract the parameters of each component from a non-Gaussian overlapped waveform, which happens in steep mountain or urban areas. Therefore, a synthetic algorithm with the boosted Richardson-Lucy deconvolution, layered extraction, and gradient descent is proposed to implement the skew-normal decomposition for the received waveforms. To validate the performance of the proposed algorithm, we develop waveform decomposition experiments for three types of data, including known-parameter waveforms, simulated waveforms and global ecosystem dynamics investigation (GEDI) satellite lidar waveforms. Meanwhile, we figure out the evaluation metrics involving correlation coefficients (CC), root mean square errors (RMSE), extracted parameter errors, and successful, missing, and unwanted rate for the decomposed waveforms. Through comparing the decomposed results of the proposed algorithm and the classical direct Gaussian decomposition algorithms, we discover that (1) the average CC has a growth of 4% and the average RMSE has a reduction of 60%; (2) the average errors of extracted amplitude, peak position, and pulse width have mitigated with 3.9%, 2.2%, and 5.1%, respectively; (3) the successful detection rate increases 40% and the unwanted and the missing rate decrease 5% and 35% for the 2000 groups of known-parameter waveforms. In addition, the average CCs have slight growth of 3% and 1.2%, and the average RMSEs have significant reductions of 43% and 49% for the simulated and GEDI lidar waveforms, respectively. This research provides a preferable waveform decomposing approach conductive to characterizing the terrestrial information from the overlapping skew-normal full-waveforms.