A DEEP LEARNING MODEL FOR EDDY TRACKING BASED ON MULTI-SOURCE REMOTE SENSING IMAGERY

被引：1

作者：

Liu, Qian ^{[1
,2
]}

Liu, Yingjie ^{[1
,2
]}

Li, Xiaofeng ^{[1
,2
]}

机构：

[1] Chinese Acad Sci, Inst Oceanol, CAS Key Lab Ocean Circulat & Waves, 7 Nanhai Rd, Qingdao 266071, Peoples R China

[2] Chinese Acad Sci, Ctr Ocean Mega Sci, 7 Nanhai Rd, Qingdao 266071, Peoples R China

来源：

2021 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM IGARSS | 2021年

基金：

中国博士后科学基金;

关键词：

Eddy tracking; deep learning; multi-source remote sensing; data fusion; MESOSCALE EDDIES; IDENTIFICATION;

D O I：

10.1109/IGARSS47720.2021.9553732

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

Mesoscale eddies are swirling water exiting ubiquitously in the global ocean. They are significant for material and energy transit and global ocean circulation. Using satellite sea surface height anomaly (SSHA) and sea surface temperature (SST) images, this study proposes a new automated eddy tracking model based on deep learning (DL) technology. Compared with existing eddy tracking algorithms, the DL-based model fuses SSHA and SST data for eddy tracking. We aim to solve problems as eddy splits, mergers, and transient 'disappearance' that occurred using SSHA data. The DL-based eddy tracking model is applied in the Kuroshio Extension, and the result verified the model's tracking accuracy and efficiency.

引用

页码：7576 / 7579

页数：4

共 28 条

[1] EDDY MOMENTUM AND HEAT FLUXES AND THEIR EFFECTS ON THE CIRCULATION OF THE EQUATORIAL PACIFIC-OCEAN [J].