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

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 choic...

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Autores principales:	Yue Song, Houpu Li, Guojun Zhai, Yan He, Shaofeng Bian, Wei Zhou
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b6f4c9dea95c40909b646277ca872964
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spelling	oai:doaj.org-article:b6f4c9dea95c40909b646277ca8729642021-12-02T15:10:46ZComparison of multichannel signal deconvolution algorithms in airborne LiDAR bathymetry based on wavelet transform10.1038/s41598-021-96551-w2045-2322https://doaj.org/article/b6f4c9dea95c40909b646277ca8729642021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-96551-whttps://doaj.org/toc/2045-2322Abstract 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).Yue SongHoupu LiGuojun ZhaiYan HeShaofeng BianWei ZhouNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-16 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yue Song Houpu Li Guojun Zhai Yan He Shaofeng Bian Wei Zhou Comparison of multichannel signal deconvolution algorithms in airborne LiDAR bathymetry based on wavelet transform
description	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).
format	article
author	Yue Song Houpu Li Guojun Zhai Yan He Shaofeng Bian Wei Zhou
author_facet	Yue Song Houpu Li Guojun Zhai Yan He Shaofeng Bian Wei Zhou
author_sort	Yue Song
title	Comparison of multichannel signal deconvolution algorithms in airborne LiDAR bathymetry based on wavelet transform
title_short	Comparison of multichannel signal deconvolution algorithms in airborne LiDAR bathymetry based on wavelet transform
title_full	Comparison of multichannel signal deconvolution algorithms in airborne LiDAR bathymetry based on wavelet transform
title_fullStr	Comparison of multichannel signal deconvolution algorithms in airborne LiDAR bathymetry based on wavelet transform
title_full_unstemmed	Comparison of multichannel signal deconvolution algorithms in airborne LiDAR bathymetry based on wavelet transform
title_sort	comparison of multichannel signal deconvolution algorithms in airborne lidar bathymetry based on wavelet transform
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b6f4c9dea95c40909b646277ca872964
work_keys_str_mv	AT yuesong comparisonofmultichannelsignaldeconvolutionalgorithmsinairbornelidarbathymetrybasedonwavelettransform AT houpuli comparisonofmultichannelsignaldeconvolutionalgorithmsinairbornelidarbathymetrybasedonwavelettransform AT guojunzhai comparisonofmultichannelsignaldeconvolutionalgorithmsinairbornelidarbathymetrybasedonwavelettransform AT yanhe comparisonofmultichannelsignaldeconvolutionalgorithmsinairbornelidarbathymetrybasedonwavelettransform AT shaofengbian comparisonofmultichannelsignaldeconvolutionalgorithmsinairbornelidarbathymetrybasedonwavelettransform AT weizhou comparisonofmultichannelsignaldeconvolutionalgorithmsinairbornelidarbathymetrybasedonwavelettransform
_version_	1718387715748134912

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

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