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Deep Neural Network for Sea Surface Temperature Prediction: Referencesby@oceanography
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Deep Neural Network for Sea Surface Temperature Prediction: References

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In this paper, researchers enhance SST prediction by transferring physical knowledge from historical observations to numerical models.
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Authors:

(1) Yuxin Meng;

(2) Feng Gao;

(3) Eric Rigall;

(4) Ran Dong;

(5) Junyu Dong;

(6) Qian Du.

REFERENCES

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Yuxin Meng received the B.Eng. degree in computer science and technology from the Anhui University of Science and Technology, Huainan, China, in 2010. She is currently pursuing the Ph.D. degree with the Vision Lab, Ocean University of China, Qingdao, China, supervised by Prof. Junyu Dong. Her research interests include image processing and computer vision.


Feng Gao (Member, IEEE) received the B.Sc degree in software engineering from Chongqing University, Chongqing, China, in 2008, and the Ph.D. degree in computer science and technology from Beihang University, Beijing, China, in 2015. He is currently an Associate Professor with the School of Information Science and Engineering, Ocean University of China. His research interests include remote sensing image analysis, pattern recognition and machine learning.


Eric Rigall received the Engineering degree from the Graduate School of Engineering, University of Nantes, Nantes, France, in 2018. He is currently pursuing the Ph.D. degree with the Vision Laboratory, Ocean University of China, Qingdao, China, supervised by Prof. Junyu Dong. His research interests include radio-frequency identification (RFID)-based positioning, signal and image processing, machine learning, and computer vision.


Ran Dong received the B.Sc degree in Mathematics and Statistics from Donghua University, Shanghai, China, in 2014, and the Ph.D. degree in Mathematics and Statistics from University of Strathclyde, United Kingdom, in 2020. She is currently a Lecturer with the School of Mathematical Science, Ocean University of China. Her research interests include artificial intelligence, mathematics, and statistics.


Junyu Dong (Member, IEEE) received the B.Sc. and M.Sc. degrees from the Department of Applied Mathematics, Ocean University of China, Qingdao, China, in 1993 and 1999, respectively, and the Ph.D. degree in image processing from the Department of Computer Science, Heriot-Watt University, Edinburgh, United Kingdom, in 2003. He is currently a Professor and Dean with the School of Computer Science and Technology, Ocean University of China. His research interests include visual information analysis and understanding, machine learning and underwater image processing.


Qian Du (Fellow, IEEE) received the Ph.D. degree in electrical engineering from the University of Maryland at Baltimore, Baltimore, MD, USA, in 2000. She is currently the Bobby Shackouls Professor with the Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS, USA. Her research interests include hyperspectral remote sensing image analysis and applications, and machine learning. Dr. Du was the recipient of the 2010 Best Reviewer Award from the IEEE Geoscience and Remote Sensing Society (GRSS). She was a Co-Chair for the Data Fusion Technical Committee of the IEEE GRSS from 2009 to 2013, the Chair for the Remote Sensing and Mapping Technical Committee of International Association for Pattern Recognition from 2010 to 2014, and the General Chair for the Fourth IEEE GRSS Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing held at Shanghai, China, in 2012. She was an Associate Editor for the PATTERN RECOGNITION, and IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING. From 2016 to 2020, she was the Editor-in-Chief of the IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATION AND REMOTE SENSING. She is currently a member of the IEEE Periodicals Review and Advisory Committee and SPIE Publications Committee. She is a Fellow of SPIE-International Society for Optics and Photonics (SPIE).


This paper is available on arxiv under CC 4.0 license.