Published in

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020

DOI: 10.1109/icassp40776.2020.9053909

Links

Tools

Export citation

Search in Google Scholar

Deep-SST-Eddies: A Deep Learning Framework to Detect Oceanic Eddies in Sea Surface Temperature Images

Proceedings article published in 2020 by Evangelos Moschos, Olivier Schwander, Alexandre Stegner, Patrick Gallinari
This paper is available in a repository.
This paper is available in a repository.

Full text: Download

Green circle
Preprint: archiving allowed
Green circle
Postprint: archiving allowed
Red circle
Published version: archiving forbidden
Data provided by SHERPA/RoMEO

Abstract

Until now, mesoscale oceanic eddies have been automatically detected through physical methods on satellite altimetry. Nevertheless, they often have a visible signature on Sea Surface Temperature (SST) satellite images, which have not been yet sufficiently exploited. We introduce a novel method that employs Deep Learning to detect eddy signatures on such input. We provide the first available dataset for this task, retaining SST images through altimetric-based region proposal. We train a CNN-based classifier which succeeds in accurately detecting eddy signatures in well-defined examples. Our experiments show that the difficulty of classifying a large set of automatically retained images can be tackled by training on a smaller subset of manually labeled data. The difference in performance on the two sets is explained by the noisy automatic labeling and intrinsic complexity of the SST signal. This approach can provide to oceanographers a tool for validation of altimetric eddy detection through SST.