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Open AccessArticle

Oceanic Eddy Identification Using an AI Scheme

1
Oceanic Modeling and Observation Laboratory, Nanjing University of Information Science and Technology, Nanjing 210044, China
2
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China
3
UNIVER-NUIST Joint AI Oceanography Academy, Nanjing University of Information Science and Technology, Nanjing 210044, China
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Zhoutong Technology LLC (UNIVER), Beijing 100020, China
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School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, China
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State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, MNR, Hangzhou 310012, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(11), 1349; https://doi.org/10.3390/rs11111349
Received: 4 May 2019 / Revised: 30 May 2019 / Accepted: 1 June 2019 / Published: 5 June 2019
(This article belongs to the Special Issue AI-based Remote Sensing Oceanography)
Oceanic eddies play an important role in global energy and material transport, and contribute greatly to nutrient and phytoplankton distribution. Deep learning is employed to identify oceanic eddies from sea surface height anomalies data. In order to adapt to segmentation problems for multi-scale oceanic eddies, the pyramid scene parsing network (PSPNet), which is able to satisfy the fusion of semantics and details, is applied as the core algorithm in the eddy detection methods. The results of eddies identified from this artificial intelligence (AI) method are well compared with those from a traditional vector geometry-based (VG) method. More oceanic eddies are detected by the AI algorithm than the VG method, especially for small-scale eddies. Therefore, the present study demonstrates that the AI algorithm is applicable of oceanic eddy detection. It is one of the first few of efforts to bridge AI techniques and oceanography research. View Full-Text
Keywords: deep learning; oceanic eddies; pyramid scene parsing network; pyramid pooling module; dilated convolution deep learning; oceanic eddies; pyramid scene parsing network; pyramid pooling module; dilated convolution
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MDPI and ACS Style

Xu, G.; Cheng, C.; Yang, W.; Xie, W.; Kong, L.; Hang, R.; Ma, F.; Dong, C.; Yang, J. Oceanic Eddy Identification Using an AI Scheme. Remote Sens. 2019, 11, 1349. https://doi.org/10.3390/rs11111349

AMA Style

Xu G, Cheng C, Yang W, Xie W, Kong L, Hang R, Ma F, Dong C, Yang J. Oceanic Eddy Identification Using an AI Scheme. Remote Sensing. 2019; 11(11):1349. https://doi.org/10.3390/rs11111349

Chicago/Turabian Style

Xu, Guangjun; Cheng, Cheng; Yang, Wenxian; Xie, Wenhong; Kong, Lingmei; Hang, Renlong; Ma, Furong; Dong, Changming; Yang, Jingsong. 2019. "Oceanic Eddy Identification Using an AI Scheme" Remote Sens. 11, no. 11: 1349. https://doi.org/10.3390/rs11111349

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