scholarly journals Accuracy and Error Analysis of Vector Measurement of Ocean Surface Current by Multi-Aperture Along-Track Interferometric SAR

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 207551-207562
Author(s):  
Yan Li ◽  
Jinsong Chong ◽  
Kai Sun ◽  
Xue Yang ◽  
Yawei Zhao
Keyword(s):  
Error Analysis ◽  
Surface Current ◽  
Ocean Surface ◽  
Vector Measurement ◽  
Ocean Surface Current ◽  
Interferometric Sar ◽  
Along Track

scholarly journals Velocity Estimation of Ocean Surface Currents in along-Track InSAR System Based on Conditional Generative Adversarial Networks

2021 ◽  
Vol 13 (20) ◽  
pp. 4088
Author(s):  
He Yan ◽  
Qianru Hou ◽  
Guodong Jin ◽  
Xing Xu ◽  
Gong Zhang ◽  
...  
Keyword(s):  
Current Velocity ◽  
Surface Current ◽  
Ocean Surface ◽  
Velocity Estimation ◽  
Training Dataset ◽  
Generative Adversarial Networks ◽  
Surface Currents ◽  
Ocean Surface Currents ◽  
Ocean Surface Current ◽  
Along Track

Velocity estimation of ocean surface currents is of great significance in the fields of the fishery, shipping, sewage discharge, and military affairs. Over the last decade, along-track interferometric synthetic aperture radar (along-track InSAR) has been demonstrated to be one of the important instruments for large-area and high-resolution ocean surface current velocity estimation. The calculation method of the traditional ocean surface current velocity, as influenced by the large-scale wave orbital velocity and the Bragg wave phase velocity, cannot easily separate the current velocity, characterized by large error and low efficiency. In this paper, a novel velocity estimation method of ocean surface currents is proposed based on Conditional Generative Adversarial Networks (CGANs). The main processing steps are as follows: firstly, the known ocean surface current field diagrams and their corresponding interferometric phase diagrams are constructed as the training dataset; secondly, the estimation model of the ocean surface current field is constructed based on the pix2pix algorithm and trained by the training dataset; finally, the interferometric phase diagrams in the test dataset are input into the trained model. In the simulation experiment, processing results of the proposed method are compared with those of traditional ocean surface current velocity estimation methods, which demonstrate the efficiency and effectiveness of the novel method.

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