Attention Aware U-Net Architecture for Semantic Segmentation and Detection of Ocean Eddy’s

Abstract

An eddy is a circular movement of ocean water that carries several ocean elements across the ocean. The study of marine biological environments and climate change benefits from the identification of ocean eddies. Eddy detection is one of the most active fields in physical oceanography research. Despite recent advancements, deep convolution neural networks for eddy detection are still in their infancy. The wide range of eddies’ sizes and shapes makes automatic eddy segmentation challenging. Through the creation of a dense forecast, UNet solves this problem. The network architecture, however, is immensely intricate. In this study, a dilated convolution U-Net is proposed for the semantic segmentation of ocean eddies. This method reduces architectural complexity without compromising performance. Further, a new attention approach is proposed to enhance the decoder features before cascading with the encoded feature map. The experimental results demonstrate that the proposed architecture outperforms the state-of-the art techniques for eddy detection