ARU$^{2}$-Net: A Deep Learning Approach for Global-Scale Oceanic Eddy Detection

Ocean eddies have a significant impact on marine ecosystems and the climate because they transport essential substances in the ocean. Detection of ocean eddies has become one of the most active topics in physical ocean research. In recent years, research based on deep learning has mainly focused on regional oceans, with small and specific data and relatively general detection results. This study processes the global eddy by pixel-by-pixel classification and generates a global eddy classification map with a resolution of 720 × 1440, which expands the data volume and improves the generality of the data. Moreover, a high-precision attention residual U
$^{2}$
-Net model, referred to as ARU
$^{2}$
-Net, is proposed, which is suitable for mining eddy surface features from sea level anomaly (SLA) and sea surface temperature (SST) data in the global ocean. ARU
$^{2}$
-Net integrates the convolutional block attention module (CBAM). The channel attention of the CBAM module is used to learn the correlation features between the SST and SLA dual channels; the spatial attention mechanism of the CBAM module is used to learn the importance of the spatial location of the eddy, focusing on the locally important regions, which further improves the detection ability of ARU
$^{2}$
-Net for eddies, and helps ARU
$^{2}$
-Net to better identify the eddy categories. Finally, we demonstrate the effectiveness of our approach on the global eddy dataset, achieving a test performance of 94.926%, significantly exceeding previous detection in some areas.