Neural Network Training for the Detection and Classification of Oceanic Mesoscale Eddies
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Aug 14, 2020
- Source ID
- 10.3390/rs12162625
Entities
People
- Daniel Hernández-sosa
- Jeffrey Martz
- Oliverio Jesús Santana Jaria
- Ryan Smith
Organizations
- Esperantic Studies Foundation