SymmetricNet: end-to-end mesoscale eddy detection with multi-modal data fusion

Abstract

Mesoscale eddies play a significant role in marine energy and matter transportation. Due to their huge impact on the ocean, mesoscale eddy detection has been studied for many years. However, existing methods mainly use single-modal data, such as the sea surface height (SSH), to detect mesoscale eddies, resulting in inaccurate detection results. In this paper, we propose an end-to-end mesoscale eddy detection method based upon multi-modal data fusion. Particularly, we don’t only use SSH, but also add data of other two modals, i.e., the sea surface temperature (SST) and the velocity of flow, which are closely related to mesoscale eddy detection. Moreover, we design a novel network named SymmetricNet, which is able to achieve multi-modal data fusion in mesoscale eddy detection. The proposed SymmetricNet mainly contains a downsampling pathway and an upsampling pathway, where the low-level feature maps from the downsampling pathway and the high-level feature maps from the upsampling pathway are merged through lateral connections. In addition, we apply dilated convolutions to the network structure to increase the receptive field without sacrificing resolution. Experiments on multi-modal mesoscale eddy dataset demonstrate the advantages of the proposed method over previous approaches for mesoscale eddy detection.