No abstract

Mesoscale oceanic eddies have a visible signature on Sea Surface Temperature (SST) satellite images, portraying diverse patterns of coherent vortices, temperature gradients and swirling filaments. However, learning the regularities of such signatures defines a challenging pattern recognition task, due to their complex structure but also to the cloud coverage which can corrupt a large fraction of the image. We introduce a novel Deep Learning approach to classify sea temperature eddy signatures, even if they are corrupted by strong cloud coverage. A large dataset of SST image patches is automatically retained and used to train a CNN-based classifier. Classification is performed with very high accuracy on coherent eddy signatures and is robust to a high level of cloud coverage, surpassing human expert efficiency on this task. This methodology can serve to validate and correct detections on satellite altimetry, the standard method used until now to track mesoscale eddies.

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.