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.

中文翻译：

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云覆盖下涡流海面温度特征的分类

中尺度海洋涡旋在海面温度 (SST) 卫星图像上具有可见的特征，描绘了相干涡旋、温度梯度和旋转细丝的不同模式。然而，学习此类签名的规律性定义了具有挑战性的模式识别任务，因为它们的结构复杂，而且云覆盖可能会破坏大部分图像。我们引入了一种新颖的深度学习方法来对海温涡流特征进行分类，即使它们被强大的云覆盖破坏了。SST 图像块的大型数据集会自动保留并用于训练基于 CNN 的分类器。分类在相干涡流特征上以非常高的精度执行，并且对高水平的云覆盖具有鲁棒性，在此任务上超过了人类专家的效率。