Automatic Detection of Ocean Eddy based on Deep Learning Technique with Attention Mechanism

Abstract

Ocean eddies are a common occurrence in ocean water circulation. They have an enormous impact on the marine ecosystem. One of the most active study topics in physical oceanography is ocean eddy detection. Although using deep learning algorithms to detect eddies is a recent trend, it is still in its infancy. In this paper, an attention mechanism-based ocean eddy detection approach using deep learning is proposed. Attention mechanism has spatial and channel attention modules that are cascaded to convolution blocks-based encoder model to simulate spatial and channel semantic interdependencies. In the spatial attention module, the feature at each point is aggregated selectively by the sum of the features at all positions. The channel attention module aggregates related data from all channel maps to selectively highlight interdependent channel maps. The original feature map and the feature map obtained through the attention mechanism are appended to enhance the feature representation further, resulting in more accurate segmentation results. The findings of the experiments show that adopting an attentionbased deep framework improves eddy recognition accuracy significantly