Neural Network Training for the Detection and Classification of Oceanic Mesoscale Eddies
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 d...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-08-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/12/16/2625 |
| _version_ | 1797558015258263552 |
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| author | Oliverio J. Santana Daniel Hernández-Sosa Jeffrey Martz Ryan N. Smith |
| author_facet | Oliverio J. Santana Daniel Hernández-Sosa Jeffrey Martz Ryan N. Smith |
| author_sort | Oliverio J. Santana |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T17:25:25Z |
| format | Article |
| id | doaj.art-7e376ee05a1641928700fade16565aec |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T17:25:25Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-7e376ee05a1641928700fade16565aec2023-11-20T10:12:19ZengMDPI AGRemote Sensing2072-42922020-08-011216262510.3390/rs12162625Neural Network Training for the Detection and Classification of Oceanic Mesoscale EddiesOliverio J. Santana0Daniel Hernández-Sosa1Jeffrey Martz2Ryan N. Smith3Department of Computer Science and Systems, University of Las Palmas de Gran Canaria, 35001 Las Palmas de Gran Canaria, SpainInstitute of Intelligent Systems and Numeric Applications in Engineering, University of Las Palmas de Gran Canaria, 35001 Las Palmas de Gran Canaria, SpainDepartment of Physics & Engineering, Fort Lewis College, Durango, CO 81301, USADepartment of Physics & Engineering, Fort Lewis College, Durango, CO 81301, USARecent 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.https://www.mdpi.com/2072-4292/12/16/2625oceanic mesoscale eddysatellite altimetryconvolutional neural networksupervised learningdeep learningdetection |
| spellingShingle | Oliverio J. Santana Daniel Hernández-Sosa Jeffrey Martz Ryan N. Smith Neural Network Training for the Detection and Classification of Oceanic Mesoscale Eddies Remote Sensing oceanic mesoscale eddy satellite altimetry convolutional neural network supervised learning deep learning detection |
| title | Neural Network Training for the Detection and Classification of Oceanic Mesoscale Eddies |
| title_full | Neural Network Training for the Detection and Classification of Oceanic Mesoscale Eddies |
| title_fullStr | Neural Network Training for the Detection and Classification of Oceanic Mesoscale Eddies |
| title_full_unstemmed | Neural Network Training for the Detection and Classification of Oceanic Mesoscale Eddies |
| title_short | Neural Network Training for the Detection and Classification of Oceanic Mesoscale Eddies |
| title_sort | neural network training for the detection and classification of oceanic mesoscale eddies |
| topic | oceanic mesoscale eddy satellite altimetry convolutional neural network supervised learning deep learning detection |
| url | https://www.mdpi.com/2072-4292/12/16/2625 |
| work_keys_str_mv | AT oliveriojsantana neuralnetworktrainingforthedetectionandclassificationofoceanicmesoscaleeddies AT danielhernandezsosa neuralnetworktrainingforthedetectionandclassificationofoceanicmesoscaleeddies AT jeffreymartz neuralnetworktrainingforthedetectionandclassificationofoceanicmesoscaleeddies AT ryannsmith neuralnetworktrainingforthedetectionandclassificationofoceanicmesoscaleeddies |