An automatic marine mesoscale eddy detection model based on improved U-Net network

Dong Ziyi; Du Zhenhong; Wu Sensen; Li Yadong; Zhang Feng; Liu Renyi

doi:10.12284/hyxb2022038

Volume 44 Issue 2

Feb. 2022

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Dong Ziyi，Du Zhenhong，Wu Sensen, et al. An automatic marine mesoscale eddy detection model based on improved U-Net network[J]. Haiyang Xuebao，2022, 44(2)：123–131 doi: 10.12284/hyxb2022038

Citation:

Dong Ziyi，Du Zhenhong，Wu Sensen, et al. An automatic marine mesoscale eddy detection model based on improved U-Net network[J]. Haiyang Xuebao，2022, 44(2)：123–131 doi: 10.12284/hyxb2022038

Citation:

Dong Ziyi，Du Zhenhong，Wu Sensen, et al. An automatic marine mesoscale eddy detection model based on improved U-Net network[J]. Haiyang Xuebao，2022, 44(2)：123–131 doi: 10.12284/hyxb2022038

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An automatic marine mesoscale eddy detection model based on improved U-Net network

doi: 10.12284/hyxb2022038

Dong Ziyi^{1, 2, 3
,},
Du Zhenhong^{1, 2, 3
,
,},
Wu Sensen^{1, 2, 3},
Li Yadong^{1, 2, 3},
Zhang Feng^{1, 2, 3},
Liu Renyi^{1, 2, 3}

1.
School of Earth Sciences, Zhejiang University, Hangzhou 310027, China
2.
Zhejiang Provincial Key Laboratory of Geograpic Information System, Hangzhou 310028, China
3.
Department of Geographic and Spatial Information Science, Zhejiang University, Hangzhou 310027, China

Received Date: 2021-01-09
Rev Recd Date: 2021-03-12

Available Online: 2021-11-25

Publish Date: 2022-02-01

Abstract

Abstract

Marine mesoscale eddies play an important role in plankton distribution, energy and salt transport. The automatic detection of marine mesoscale eddies is a basis for monitoring and analyzing their spatiotemporal variations. Traditional physical characteristics-based methods depend on artificially designing parameters, and result in low accuracy of mesoscale eddy extraction. Therefore, a marine mesoscale eddy automatic detection model based on improved U-Net network according to the marine satellite sea surface height images is proposed in this paper. The proposed model embeds the convolution attention modules in the feature extraction stage, which enables the model focuses on the most relevent area. Meanwhile, the model introduces the residual learning module to solve the problem that the network is too deep to train the model. In this paper, the satellite sea surface height dataset in the South Atlantic Ocean is taken as an example to carry out experiments. The results show that the proposed model achieves a high accuracy of 93.28% when detecting the marine mesoscale eddies, which is significantly better than EddyNet and other models. The model can provide a reliable technology for oceanographers to detect marine mesoscale eddies through the satellite sea surface height.
- marine mesoscale eddy,
- deep learning,
- semantic segmentation,
- convolutional block attention module,
- residual learning

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References(22)

References

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