CNN-based Super-resolution Full-waveform LiDAR

Liu, Gangping; Ke, Jun; Lam, Edmund Y.

doi:10.1364/3d.2020.jw2a.29

Cited by 2 publications

(2 citation statements)

References 3 publications

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“…Unsupervised denoising based on an encoder-decoder has achieved good results [14,31]. The deconvolution layer is used for upsampling, and highquality echo signals can be obtained at a low sampling rate [16,32]. By combining the prior knowledge provided by echo classification with the denoising algorithm [33], the parameter selection strategy can be optimized for different scenes, which can optimize the peak signal-to-noise ratio of the target.…”

Section: Learning-based Methodsmentioning

confidence: 99%

“…Fullwaveform data can be regarded as a one-dimensional time series, so deep learning is widely used in LiDAR data processing [14,15]. Super-resolution technology can restore the echo with a higher sampling rate without upgrading the hardware [16,17]. Although multi-channel information has been proven to be effective for echo classification, denoising and signal enhancement have not been used [5,6].…”

Section: Introductionmentioning

confidence: 99%

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Coupling Dilated Encoder–Decoder Network for Multi-Channel Airborne LiDAR Bathymetry Full-Waveform Denoising

Zhao

Zhou

et al. 2023

Remote Sensing

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Multi-channel airborne full-waveform LiDAR is widely used for high-precision underwater depth measurement. However, the signal quality of full-waveform data is unstable due to the influence of background light, dark current noise, and the complex transmission process. Therefore, we propose a nonlocal encoder block (NLEB) based on spatial dilated convolution to optimize the feature extraction of adjacent frames. On this basis, a coupled denoising encoder–decoder network is proposed that takes advantage of the echo correlation in deep-water and shallow-water channels. Firstly, full waveforms from different channels are stacked together to form a two-dimensional tensor and input into the proposed network. Then, NLEB is used to extract local and nonlocal features from the 2D tensor. After fusing the features of the two channels, the reconstructed denoised data can be obtained by upsampling with a fully connected layer and deconvolution layer. Based on the measured data set, we constructed a noise–noisier data set, on which several denoising algorithms were compared. The results show that the proposed method improves the stability of denoising by using the inter-channel and multi-frame data correlation.

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