Reduction of rain effect on wave height estimation from marine X-band radar images using unsupervised generative adversarial networks

作     者：Li Wang Hui Mei Weilun Luo Yunfei Cheng 

作者机构：The Third Research Institute of the Ministry of Public SecurityShanghaiPeople’s Republic of China The 802 Institute of Shanghai Academy of Space Flight TechnologyShanghaiPeople’s Republic of China 

出 版 物：《International Journal of Digital Earth》 (国际数字地球学报（英文）)

年 卷 期：2023年第16卷第1期

页      面：2356-2373页

核心收录：

学科分类：080904[工学-电磁场与微波技术] 0810[工学-信息与通信工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 081105[工学-导航、制导与控制] 080203[工学-机械设计及理论] 081001[工学-通信与信息系统] 0802[工学-机械工程] 081002[工学-信号与信息处理] 0825[工学-航空宇航科学与技术] 0811[工学-控制科学与工程] 

基　　金：supported by the National Key Research and Development Program of China[grant no 2021YFF0602104-1] 

主　　题：Generative adversarial networks self-attention mechanism unsupervised model marine X-band radar wave height 

摘      要：An intelligent single radar image de-raining method based on unsupervised self-attention generative adversarial networks is proposed to improve the accuracy of wave height parameter inversion *** method builds a trainable end-to-end de-raining model with an unsupervised cycle-consistent adversarial network as an AI framework,which does not require pairs of rain-contaminated and corresponding ground-truth rain-free images for *** model is trained by feeding rain-contaminated and clean radar images in an unpaired manner,and the atmospheric scattering model parameters are not required as a prior ***,a self-attention mechanism is introduced into the model,allowing it to focus on rain clutter when processing radar *** combines global and local rain clutter context information to output more accurate and clear de-raining radar *** proposed method is validated by applying it to actualfield test data,which shows that compared with the wave height derived from the original rain-contaminated data,the root-mean-square error is reduced by 0.11 m and the correlation coefficient of the wave height is increased by 14%using the de-raining *** results demonstrate that the method effectively reduces the impact of rain on the accuracy of wave height parameter estimation from marine X-band radar images.