DUNet: Dual U-Net Architecture for Ocean Eddies Detection and Tracking

The accurate and consistent detection of ocean eddies significantly improves the monitoring of ocean surface dynamics and the identification of regional hydrographic and biological characteristics. The study of marine ecosystems and climate change requires an understanding of ocean eddies. Data from multi-satellite altimeters, which track sea surface height, are used in eddy detection. Altimeter measurements provide an accurate representation of the sea surface height. The existing deep learning-based eddy detection approaches suffer from high model and computational complexity. The fact that there are eddies of different diameters makes eddy identification more challenging. In this paper, the detection of ocean eddies using a dual encoder and decoder architecture is proposed to address these inadequacies. An attention mechanism is developed to comprehend the pixel-level context of the semantic segmentation. A series connection of separable convolutions is proposed to adequately describe the context of multi-scale fusion. Further, the tracking of eddies is also proposed using a novel tracking method. The experimental outcomes demonstrate that the proposed approach achieved mean intersection of union score, F-beta score, and mean pixel accuracy of 89.98 %, 94.47%, 95.13% and 89.66%, 94.54%, 95.51% on the Southern Atlantic Ocean and the South China Sea datasets.