EDNet: A Mesoscale Eddy Detection Network with Multi-Modal Data

Mesoscale eddies play an important role in the transportation and distribution of energy, material and heat in the global ocean. Therefore, mesoscale eddy detection has been researched for a long time. At present, several deep learning models have been proposed for mesoscale eddy detection. However, most of these methods only use single-modal data, while ignoring data of other modals closely related to mesoscale eddy detection. In this paper, we introduce a multi-modal mesoscale eddy dataset, consisting of the satellite data in three modals, i.e., sea surface height (SSH), sea surface temperature (SST) and velocity of flow. Furthermore, we propose an EDNet (Eddy Detection Network), which contains four modules, i.e., multi-modal data fusion module, deep fusion module, region proposal module and head module. We use multi-modal data fusion module to fuse multi-modal data, use deep fusion module to learn the feature representations of the fused multi-modal data and use the region proposal module to generate region proposals containing the mesoscale eddies. There are two branches in the head module, one for classifying and locating the mesoscale eddies, while the other for providing pixel-level instance segmentation of the mesoscale eddies. The experimental results show that EDNet based on multi-modal data fusion significantly improves the accuracy of mesoscale eddy detection over previous approaches.