Coupling Dilated Encoder–Decoder Network for Multi-Channel Airborne LiDAR Bathymetry Full-Waveform Denoising
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Published:2023-06-27
Issue:13
Volume:15
Page:3293
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ISSN:2072-4292
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Container-title:Remote Sensing
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language:en
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Short-container-title:Remote Sensing
Author:
Hu Bin12ORCID, Zhao Yiqiang1, Zhou Guoqing3ORCID, He Jiaji1ORCID, Liu Changlong4, Liu Qiang1, Ye Mao1, Li Yao1ORCID
Affiliation:
1. School of Microelectronics, Tianjin University, Tianjin 300072, China 2. Technical College for the Deaf, Tianjin University of Technology, Tianjin 300382, China 3. College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China 4. Fifty-Fourth Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, China
Abstract
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.
Funder
Guangxi Innovative Development Grand
Subject
General Earth and Planetary Sciences
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