Scientific Reports (Aug 2021)

Comparison of multichannel signal deconvolution algorithms in airborne LiDAR bathymetry based on wavelet transform

  • Yue Song,
  • Houpu Li,
  • Guojun Zhai,
  • Yan He,
  • Shaofeng Bian,
  • Wei Zhou

DOI
https://doi.org/10.1038/s41598-021-96551-w
Journal volume & issue
Vol. 11, no. 1
pp. 1 – 16

Abstract

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Abstract 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 of extreme importance to guarantee the accuracy of the bathymetric retrieval. In this study, we use a wavelet-denoising method to denoise the received waveform and subsequently test four algorithms for denoised-waveform processing, namely, the Richardson–Lucy deconvolution (RLD), blind deconvolution (BD), Wiener filter deconvolution (WFD), and constrained least-squares filter deconvolution (RFD). The simulation and measured multichannel databases are used to evaluate the algorithms, with focus on improving their performance after data-denoising and their capability of extracting water depth. 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 for 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).