Cross-Domain Submesoscale Eddy Detection Neural Network for HF Radar

With the rapid development of deep learning, the neural network becomes an efficient approach for eddy detection. However, previous work employs a traditional neural network with a focus on improving the detecting accuracy only using limited data under a single scenario. Meanwhile, the experience of...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2021-07, Vol.13 (13), p.2441
Main Authors: Liu, Fangyuan, Zhou, Hao, Huang, Weimin, Tian, Yingwei, Wen, Biyang
Format: Article
Language:English
Subjects:
cross-domain learning
Deep learning
Domains
Eddies
eddy detection
Geometry
high-frequency radar
Labeling
Methods
Neural networks
Ocean circulation
Physics
Pipelines
Radar
Remote sensing
Sensors
Trajectory analysis
Transfer learning
Vortices
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Summary:With the rapid development of deep learning, the neural network becomes an efficient approach for eddy detection. However, previous work employs a traditional neural network with a focus on improving the detecting accuracy only using limited data under a single scenario. Meanwhile, the experience of detecting eddies from one experiment is not directly inherited from the detection model for other experiments. Therefore, a cross-domain submesoscale eddy detection neural network (CDEDNet) based on the high-frequency radar (HFR) data of the Nansan and Xuwen region is proposed in this paper. Firstly, a fundamental deep eddy detection architecture CDEDNet-0 is constructed with a fully convolutional network (FCN). Secondly, for solving the problem of insufficient labeled eddy data, an instance-based domain adaption method is adopted in CDEDNet-1 to increase training samples. Thirdly, for tackling the problem of unable to inherit previous detection experience, parameter-based transfer learning is incorporated in CDEDNet-2 for multi-scene eddy detection. The experiment results demonstrate CDEDNet-1 and CDEDNet-2 perform better than CDEDNet-0 in terms of accuracy. Meanwhile, eddy characteristics including eddy type, radius, occurring time, merger, and dynamic trajectory are analyzed for the Nansan and Xuwen regions.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs13132441