Author:
Xia Linghui,Chen Ge,Chen Xiaoyan,Ge Linyao,Huang Baoxiang
Abstract
Oceanic eddies have a non-negligible impact on ocean energy transfer, nutrient distribution, and biological migration in global oceans. The fine detection of oceanic eddies is significant for the development of marine science. Remarkable achievements of eddy recognition were achieved by mining the satellite altimeter data and its derived data. However, due to the limited spatial resolution of the altimeters, it is difficult to detect the submesoscale oceanic eddies with radial dimensions less than 10 km. Different from the previous works, the context and edge association network (CEA-Net) is proposed to identify submesoscale oceanic eddies with high spatial resolution Sentinel-1 data. The edge information fusion module (EIFM) is designed to associate the context and edge feature more accurately and efficiently. Furthermore, a multi-scale eddy detection strategy is proposed and applied to Sentinel-1 interferometric wide swath data to solve the scale problem of oceanic eddy detection. Specifically, a manually interpreted dataset, SAR-Eddy 2019, was constructed to address the dilemma of insufficient datasets for submesoscale oceanic eddy detection. The experimental results demonstrate that CEA-Net can outperform other mainstream models with the highest mAP reaching 85.47% with SAR-Eddy 2019 dataset. The CEA-Net proposed in this research provides important significance for the study of submesoscale oceanic eddies.
Funder
Polit National Laboratory for Marine Science and Technology
National Natural Science Foundation of China
Natural Science Foundation of Shandong Province
Subject
Ocean Engineering,Water Science and Technology,Aquatic Science,Global and Planetary Change,Oceanography
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