Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/117892
Título: Oceanic mesoscale eddy detection and convolutional neural network complexity
Autores/as: Santana, Oliverio J. 
Hernández-Sosa, Daniel 
Smith, Ryan N.
Clasificación UNESCO: 120304 Inteligencia artificial
120326 Simulación
Palabras clave: Convolutional Neural Network
Deep Learning
Oceanic Mesoscale Eddy
Remote Sensing
Satellite Altimetry
Fecha de publicación: 2022
Publicación seriada: International Journal of Applied Earth Observation and Geoinformation 
Resumen: Deep learning has drawn the attention of oceanographic researchers over the past few years, making the research community adopt computer vision techniques for oceanic mesoscale eddy detection on satellite altimetry gridded products. In this paper, we describe a convolutional neural network designed to detect eddies in satellite altimetry maps after being trained using segmentation masks provided by the OpenEddy detection algorithm. Against the current trend, in which increasingly complex neural networks are being proposed to address this problem, our design is relatively simple and yet provides competitive performance when compared to any of the previous deep learning methods reported in the literature. Furthermore, we show that our model is less sensitive to timely variations than the traditional models based on physical and geometric features defined by human experts, making it possible for our model to use the general data context to identify eddies that those traditional models would have missed. These results prove that overly complex neural network architectural designs are not required to solve the eddy detection problem on altimetry maps and generate a sufficiently good model for most practical applications in the field of marine sciences.
URI: http://hdl.handle.net/10553/117892
ISSN: 1569-8432
DOI: 10.1016/j.jag.2022.102973
Fuente: International Journal of Applied Earth Observation and Geoinformation [ISSN 1569-8432],v. 113, 102973, (Septiembre 2022)
Colección:Artículos
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