DeepEddynet: leveraging CBAM, U-Net, and morphological operations for mesoscale eddy detection and classification

This study addresses the critical task of automatically identifying oceanic eddies, essential features for marine energy and chemical distribution, using sea surface temperature data from the Atlantic Ocean. It introduces the Deep Eddy Network, a sophisticated deep-learning framework based on an encoder-decoder architecture. The network performs pixel-wise classification, generating an output map where each pixel is labeled as ‘0’ (non-eddy), ‘1’ (anticyclonic eddy), or ‘2’ (cyclonic eddy). Key innovations include a dedicated morphological module that injects shapebased information into the input data. The architecture is designed for high efficiency, employing advanced techniques in its core components. The encoder block utilizes dilated convolutions combined with activation functions, batch normalization, and an attention mechanism. Similarly, the decoder block integrates activation functions with 2D transpose convolution, batch normalization, and attention. Developed using Python and Keras, the final model demonstrates a superior balance between computational performance and segmentation accuracy. This makes the proposed Deep Eddy Network a practical and powerful tool, particularly suitable for deployment in real-time oceanographic monitoring and analysis applications, advancing our ability to understand these dynamic oceanic phenomena.