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Article

Neural Network Training for the Detection and Classification of Oceanic Mesoscale Eddies

1
Department of Computer Science and Systems, University of Las Palmas de Gran Canaria, 35001 Las Palmas de Gran Canaria, Spain
2
Institute of Intelligent Systems and Numeric Applications in Engineering, University of Las Palmas de Gran Canaria, 35001 Las Palmas de Gran Canaria, Spain
3
Department of Physics & Engineering, Fort Lewis College, Durango, CO 81301, USA
4
Institute of Environment, Florida International University, Miami, FL 33199, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(16), 2625; https://doi.org/10.3390/rs12162625
Received: 9 July 2020 / Revised: 7 August 2020 / Accepted: 11 August 2020 / Published: 14 August 2020
(This article belongs to the Special Issue Computer Vision and Deep Learning for Remote Sensing Applications)
Recent advances in deep learning have made it possible to use neural networks for the detection and classification of oceanic mesoscale eddies from satellite altimetry data. Various neural network models have been proposed in recent years to address this challenge, but they have been trained using different types of input data and evaluated using different performance metrics, making a comparison between them impossible. In this article, we examine the most common dataset and metric choices, by analyzing the reasons for the divergences between them and pointing out the most appropriate choice to obtain a fair evaluation in this scenario. Based on this comparative study, we have developed several neural network models to detect and classify oceanic eddies from satellite images, showing that our most advanced models perform better than the models previously proposed in the literature. View Full-Text
Keywords: oceanic mesoscale eddy; satellite altimetry; convolutional neural network; supervised learning; deep learning; detection; classification; data analysis oceanic mesoscale eddy; satellite altimetry; convolutional neural network; supervised learning; deep learning; detection; classification; data analysis
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MDPI and ACS Style

Santana, O.J.; Hernández-Sosa, D.; Martz, J.; Smith, R.N. Neural Network Training for the Detection and Classification of Oceanic Mesoscale Eddies. Remote Sens. 2020, 12, 2625. https://doi.org/10.3390/rs12162625

AMA Style

Santana OJ, Hernández-Sosa D, Martz J, Smith RN. Neural Network Training for the Detection and Classification of Oceanic Mesoscale Eddies. Remote Sensing. 2020; 12(16):2625. https://doi.org/10.3390/rs12162625

Chicago/Turabian Style

Santana, Oliverio J., Daniel Hernández-Sosa, Jeffrey Martz, and Ryan N. Smith 2020. "Neural Network Training for the Detection and Classification of Oceanic Mesoscale Eddies" Remote Sensing 12, no. 16: 2625. https://doi.org/10.3390/rs12162625

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