Deep Learning for Eddy Detection in the Arctic Marginal Ice Zone by Spaceborne SAR

Eddies are ubiquitous in the Arctic Ocean, as observed by spaceborne synthetic aperture radar (SAR) over the open seas and marginal ice zones (MIZs). In MIZs, SAR captures eddies mainly owing to the accumulation of drifting ice floes, known as ice eddies. We recently proposed a deep learning network named EOLO (Eddy detection based on You-Only-Look-Once algorithm) to detect ocean eddies by SAR, which presents high precision (about 95%) on open-water eddies in generalized experiments; however, it will over-extract complex background information of sea ice and generate false detections. Therefore, we move further to multi-class and multi-scale eddy detection in this study. First, a Sentinel-1 (S1) SAR data-based ice eddy dataset was established, placing particular emphasis on incorporating diverse background (non-eddy) samples, especially for sea ice; subsequently, the algorithm for ice eddy detection was trained based on the novel YOLOv8 network, named EOLO-ice. Finally, incorporating full and slicing-aided inferences and combining the sea ice extent data, the EOLO and EOLO-ice were integrated to generate a comprehensive eddy inference framework to identify multiple-scale eddies in both open oceans and MIZs. Extensive testing was performed on EOLO-ice using the ice eddy dataset, with a precision (P) of 92.37% and a recall (R) of 90.23%. The effectiveness of EOLO-ice for ice eddy detection was further illustrated by visualizing feature maps at critical stages, highlighting the crucial role of the C2F module and decoupled head in YOLOv8 to suppress background features of sea ice in SAR imagery. Finally, the integrated eddy inference framework was applied to 912 S1 scenes acquired over the Greenland Sea and Fram Strait, and then achieved a high performance, with a P and a R of 94.39% and 89.46%, respectively, suggesting its good applicability in the open seas and MIZs.