Comparison of Multichannel Signal Deconvolution Algorithms in Airborne LiDAR Bathymetry Based on Wavelet Transform

Airborne LiDAR bathymetry offers low cost and high mobility, making it an ideal option for shallow-water measurements. However, due to differences in the measurement environment and the laser emission channel, the received waveform is difficult to extract using a single algorithm. The choice of a suitable waveform processing method is thus extremely important to guarantee the accuracy of the bathymetric retrieval. In this work, we use a wavelet-denoising method to denoise the received waveform and then test four algorithms for denoised-waveform processing: Richardson–Lucy deconvolution (RLD), blind deconvolution (BD), Wiener filter deconvolution (WFD), and constrained least-squares filter deconvolution (RFD). The simulation database and the measured multichannel database are used to evaluate the algorithms, with the focus on improving their performance after the data-denoising preprocessing and their capability of extracting water depth. The results show that applying wavelet denoising before deconvolution improves the extraction accuracy. The four algorithms perform better for the shallow water orthogonal polarization channel (PMT2) than the shallow horizontal row polarization channel (PMT1). Of the four algorithms, RLD provides the best signal-detection rate, and RFD is the most robust. BD has low computational efficiency, and WFD performs poorly in deep water (<25 m).