Sea surface reconstruction from marine radar images using deep convolutional neural networks  

作　　者：Mingxu Zhao Yaokun Zheng Zhiliang Lin 

机构地区：[1]State Key Laboratory of Ocean Engineering,Shanghai Jiao Tong University,Shanghai,200240,PR China [2]Marine Numerical Experiment Center,School of Naval Architecture,Ocean and Civil Engineering,Shanghai Jiao Tong University,Shanghai,200240,PR China

出　　处：《Journal of Ocean Engineering and Science》2023年第6期647-661,共15页海洋工程与科学（英文）

基　　金：the National Natu-ral Science Foundation of China(grant no.51979162 and no.52088102);the Fundamental Research Funds for the Central Universities of China;the Oceanic Interdisciplinary Program of Shanghai Jiao Tong University(project number SL2021MS019).

摘　　要：The sea surface reconstructed from radar images provides valuable information for marine operations and maritime transport.The standard reconstruction method relies on the three-dimensional fast Fourier transform(3D-FFT),which introduces empirical parameters and modulation transfer function(MTF)to correct the modulation effects that may cause errors.In light of the convolutional neural networks’(CNN)success in computer vision tasks,this paper proposes a novel sea surface reconstruction method from marine radar images based on an end-to-end CNN model with the U-Net architecture.Synthetic radar images and sea surface elevation maps were used for training and testing.Compared to the standard reconstruction method,the CNN-based model achieved higher accuracy on the same data set,with an improved correlation coefficient between reconstructed and actual wave fields of up to 0.96-0.97,and a decreased non-dimensional root mean square error(NDRMSE)of around 0.06.The influence of training data on the deep learning model was also studied.Additionally,the impact of the significant wave height and peak period on the CNN model’s accuracy was investigated.It has been demonstrated that the accuracy will fluctuate as the wave steepness increases,but the correlation coefficient remains above 0.90,and the NDRMSE remains less than 0.11.

关 键 词：Sea surface reconstruction Radar image CNN model 

分 类 号：P73[天文地球—海洋科学] TP183[自动化与计算机技术—控制理论与控制工程]