Oceanic Eddy Identification Using Pyramid Split Attention U-Net With Remote Sensing Imagery

Oceanic eddy is the ubiquitous ocean flow phenomenon, which has been the key factor in the transportation of ocean energy and materials. Consequently, oceanographic understanding can be enhanced by the intelligent identification of eddy. State-of-the-art deep learning technologies are gradually improving identification methods. This letter proposes the pyramid split attention (PSA) eddy detection U-Net architecture (PSA-EDUNet) that targets oceanic eddy identification from ocean remote sensing imagery. As for the PSA-EDUNet, its inspiration comes from U-Net, which contains encoder and decoder parts, making the integration of inferior and senior features efficient and ensuring the feature information will not be lost in large quantities through nonlinear connection mode. Meanwhile, the PAS module is introduced to enhance feature extraction. In terms of the fusion data, the sea surface feature is the main criterion of eddy identification, including sea surface temperature (SST) and sea level anomaly (SLA). The experiments are implemented on the Kuroshio Extension (KE) and the South Atlantic regions, the results demonstrate that the proposed method can outperform other methods, especially for eddy edges and small-scale eddies.