{"title":"DUNet: Dual U-Net Architecture for Ocean Eddies Detection and Tracking","authors":"Shaik John Saida;Samit Ari","doi":"10.1109/TETCI.2024.3359099","DOIUrl":null,"url":null,"abstract":"The accurate and consistent detection of ocean eddies significantly improves the monitoring of ocean surface dynamics and the identification of regional hydrographic and biological characteristics. The study of marine ecosystems and climate change requires an understanding of ocean eddies. Data from multi-satellite altimeters, which track sea surface height, are used in eddy detection. Altimeter measurements provide an accurate representation of the sea surface height. The existing deep learning-based eddy detection approaches suffer from high model and computational complexity. The fact that there are eddies of different diameters makes eddy identification more challenging. In this paper, the detection of ocean eddies using a dual encoder and decoder architecture is proposed to address these inadequacies. An attention mechanism is developed to comprehend the pixel-level context of the semantic segmentation. A series connection of separable convolutions is proposed to adequately describe the context of multi-scale fusion. Further, the tracking of eddies is also proposed using a novel tracking method. The experimental outcomes demonstrate that the proposed approach achieved mean intersection of union score, F-beta score, and mean pixel accuracy of 89.98 %, 94.47%, 95.13% and 89.66%, 94.54%, 95.51% on the Southern Atlantic Ocean and the South China Sea datasets.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":5.3000,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Emerging Topics in Computational Intelligence","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10431818/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
The accurate and consistent detection of ocean eddies significantly improves the monitoring of ocean surface dynamics and the identification of regional hydrographic and biological characteristics. The study of marine ecosystems and climate change requires an understanding of ocean eddies. Data from multi-satellite altimeters, which track sea surface height, are used in eddy detection. Altimeter measurements provide an accurate representation of the sea surface height. The existing deep learning-based eddy detection approaches suffer from high model and computational complexity. The fact that there are eddies of different diameters makes eddy identification more challenging. In this paper, the detection of ocean eddies using a dual encoder and decoder architecture is proposed to address these inadequacies. An attention mechanism is developed to comprehend the pixel-level context of the semantic segmentation. A series connection of separable convolutions is proposed to adequately describe the context of multi-scale fusion. Further, the tracking of eddies is also proposed using a novel tracking method. The experimental outcomes demonstrate that the proposed approach achieved mean intersection of union score, F-beta score, and mean pixel accuracy of 89.98 %, 94.47%, 95.13% and 89.66%, 94.54%, 95.51% on the Southern Atlantic Ocean and the South China Sea datasets.
期刊介绍:
The IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI) publishes original articles on emerging aspects of computational intelligence, including theory, applications, and surveys.
TETCI is an electronics only publication. TETCI publishes six issues per year.
Authors are encouraged to submit manuscripts in any emerging topic in computational intelligence, especially nature-inspired computing topics not covered by other IEEE Computational Intelligence Society journals. A few such illustrative examples are glial cell networks, computational neuroscience, Brain Computer Interface, ambient intelligence, non-fuzzy computing with words, artificial life, cultural learning, artificial endocrine networks, social reasoning, artificial hormone networks, computational intelligence for the IoT and Smart-X technologies.