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

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 13; p. 3293
• Main Authors: Hu, Bin, Zhao, Yiqiang, Zhou, Guoqing, He, Jiaji, Liu, Changlong, Liu, Qiang, Ye, Mao, Li, Yao
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2023
• Subjects: Accuracy; airborne LiDAR; Algorithms; Aquaporins; Background noise; Bathymetry; Channels; Classification; Coders; couplings; Dark current; Data correlation; Data processing; data quality; Datasets; Decomposition; Deep learning; Deep water; Depth measurement; exhibitions; extracts; Feature extraction; full-waveform denoising; Lasers; Lidar; Mathematical analysis; Methods; Noise reduction; Optical radar; Remote sensing; Shallow water; Signal quality; Signal to noise ratio; Tensors; Topography; Water quality; Waveforms; China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15133293

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.