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.
The inadequate spatial resolution of altimeter results in low identification efficiency of oceanic eddies, especially for small-scale eddies. It is well known that eddies can not only induce sea surface signal but more importantly have typical vertical structure characteristics. However, although the vertical structure characteristics are usually used for statistical analysis, they are seldom considered in the process of eddy recognition. This study is devoted to identifying eddies from the perspective of their vertical signal derived from the 18-year Argo data. Due to the irregular and noisy profile pattern, the direct identification of eddy core from Argo profile is deemed to be a challenge. With the popularity of artificial intelligence, a new hybrid method that combines the advantages of convolutional neural network (CNN) with extreme gradient boosting (XGBoost) is proposed to extract the representative vertical feature and identify eddy from a profile. First, CNN is employed as a feature extractor to automatically obtain vertical features from the input profile at the bottom of the network. Second, the obtained high-dimensional feature vectors are inputted into the XGBoost model, combined with other profile features for classifying profiles that are outside altimeter-identified eddies (Alt eddy). Finally, extensive experiments are implemented to demonstrate the efficiency of the proposed method. The results show that the classification accuracy of CNN-XGBoost model can reach 98%, and about 36% eddies are recaptured. These eddies, dubbed CNN-XGB eddies, are benchmarked against Alt eddies for the vertical structure and geographical distribution, demonstrating a similar or even stronger vertical signal and a prominent eddy belt in the tropical ocean. Within the proposed theory framework, there are various potentials to obtain a better outlook for eddy identification and in situ float observations.
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