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Oceanic Eddy Identification Using Pyramid Split Attention U-Net With Remote Sensing Imagery

Zhao, Nan; Huang, Baoxiang; Yang, Jie; Radenkovic, Milena; Chen, Ge

Oceanic Eddy Identification Using Pyramid Split Attention U-Net With Remote Sensing Imagery Thumbnail


Authors

Nan Zhao

Baoxiang Huang

Jie Yang

Ge Chen



Abstract

Oceanic eddy is the ubiquitous ocean flow phenomenon, which has been the key factor in the transportation of ocean energy and materials. Consequently, oceanographic understanding can be enhanced by the intelligent identification of eddy. State-of-the-art deep learning technologies are gradually improving identification methods. This letter proposes the pyramid split attention (PSA) eddy detection U-Net architecture (PSA-EDUNet) that targets oceanic eddy identification from ocean remote sensing imagery. As for the PSA-EDUNet, its inspiration comes from U-Net, which contains encoder and decoder parts, making the integration of inferior and senior features efficient and ensuring the feature information will not be lost in large quantities through nonlinear connection mode. Meanwhile, the PAS module is introduced to enhance feature extraction. In terms of the fusion data, the sea surface feature is the main criterion of eddy identification, including sea surface temperature (SST) and sea level anomaly (SLA). The experiments are implemented on the Kuroshio Extension (KE) and the South Atlantic regions, the results demonstrate that the proposed method can outperform other methods, especially for eddy edges and small-scale eddies.

Journal Article Type Article
Acceptance Date Feb 7, 2023
Online Publication Date Feb 10, 2023
Publication Date 2023
Deposit Date Feb 17, 2023
Publicly Available Date Mar 28, 2023
Journal IEEE Geoscience and Remote Sensing Letters
Print ISSN 1545-598X
Electronic ISSN 1558-0571
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Peer Reviewed Peer Reviewed
Volume 20
Article Number 1500605
DOI https://doi.org/10.1109/lgrs.2023.3243902
Keywords Electrical and Electronic Engineering, Geotechnical Engineering and Engineering Geology
Public URL https://nottingham-repository.worktribe.com/output/17386429
Publisher URL https://ieeexplore.ieee.org/document/10041953

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