气候变化领域集成服务门户(CCPortal): DUNet: Dual U-Net Architecture for Ocean Eddies Detection and Tracking

CCPortal

DOI	10.1109/TETCI.2024.3359099
	DUNet: Dual U-Net Architecture for Ocean Eddies Detection and Tracking
	Saida, Shaik John; Ari, Samit
发表日期	2024
ISSN	2471-285X
起始页码	8
结束页码	2
卷号	8 期号:2
英文摘要	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.
英文关键词	Dual encoder; dual decoder; pixel-level segmentation; eddies; tracking
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:001164192300001
来源期刊	IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/296208
作者单位	National Institute of Technology (NIT System); National Institute of Technology Rourkela
推荐引用方式 GB/T 7714	Saida, Shaik John,Ari, Samit. DUNet: Dual U-Net Architecture for Ocean Eddies Detection and Tracking[J],2024,8(2).
APA	Saida, Shaik John,&Ari, Samit.(2024).DUNet: Dual U-Net Architecture for Ocean Eddies Detection and Tracking.IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE,8(2).
MLA	Saida, Shaik John,et al."DUNet: Dual U-Net Architecture for Ocean Eddies Detection and Tracking".IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 8.2(2024).