Transformer-Based Deep Learning for Mesoscale Eddy Detection in Sea Surface Temperature Maps
Mesoscale eddies are dynamic oceanic phenomena significantly influencing marine ecosystems’ energy transfer, nutrients, and biogeochemical cycles. These eddies’ precise identification and categorization can improve climate modeling, ocean circulation research, and environmental...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/11015982/ |
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| author | Chen Ji Wenyang Xu Xiangtian Zheng Yasmeen Ahmed Saad Ahmed Jamal Fakhar Imam Mohammed Saleh Ali Muthanna Maha Ibrahim Sajid Ullah Dmitry E. Kucher |
| author_facet | Chen Ji Wenyang Xu Xiangtian Zheng Yasmeen Ahmed Saad Ahmed Jamal Fakhar Imam Mohammed Saleh Ali Muthanna Maha Ibrahim Sajid Ullah Dmitry E. Kucher |
| author_sort | Chen Ji |
| collection | DOAJ |
| description | Mesoscale eddies are dynamic oceanic phenomena significantly influencing marine ecosystems’ energy transfer, nutrients, and biogeochemical cycles. These eddies’ precise identification and categorization can improve climate modeling, ocean circulation research, and environmental surveillance. This study presents an innovative methodology for mesoscale eddy detection utilizing Transformer-based deep learning models, namely, Swin Transformer U-Net and SegFormer, to categorize ocean eddies from sea surface temperature (SST) maps sourced from the copernicus marine environment monitoring service. In contrast to traditional convolutional neural networks (CNNs) that have prevailed in the domain, Transformer-based models provide superior global attention mechanisms, facilitating greater feature extraction and segmentation precision. The models are trained on labeled SST datasets and assessed using intersection over union, Dice coefficient, precision, recall, and F1-score. Experimental findings demonstrate that Transformer-based designs surpass conventional CNN-based techniques, yielding enhanced generalization and superior accuracy in classifying cyclonic and anticyclonic eddies. This study illustrates the efficacy of attention-based segmentation algorithms for resilient oceanographic applications. |
| format | Article |
| id | doaj-art-8a0267a56b3d4010b491d1a218a71b82 |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-8a0267a56b3d4010b491d1a218a71b822025-08-20T03:31:55ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118152491526410.1109/JSTARS.2025.357400411015982Transformer-Based Deep Learning for Mesoscale Eddy Detection in Sea Surface Temperature MapsChen Ji0https://orcid.org/0000-0001-5740-4872Wenyang Xu1Xiangtian Zheng2https://orcid.org/0000-0001-5819-1215Yasmeen Ahmed3Saad Ahmed Jamal4Fakhar Imam5Mohammed Saleh Ali Muthanna6https://orcid.org/0000-0002-1165-7812Maha Ibrahim7Sajid Ullah8https://orcid.org/0000-0002-5079-3761Dmitry E. Kucher9https://orcid.org/0000-0002-7919-3487School of Geography and Ocean Science, Nanjing University, Nanjing, ChinaNanjing Institute of Technology, Nanjing, ChinaNanjing Institute of Technology, Nanjing, ChinaDepartment of Building Construction Science College of Architecture Art and Design, Mississippi State University, Mississippi State, MS, USAInstitute for Research and Advanced Studies, Universidade de Evora, Évora, PortugalUniversity of the Punjab, Lahore, PakistanDepartment of International Business Management, Tashkent State University of Economics, Tashkent, UzbekistanDepartment of International Business Management, Tashkent State University of Economics, Tashkent, UzbekistanDepartment of Water Resources and Environmental Engineering, Nangarhar University, Nangarhar, AfghanistanDepartment of Environmental Management, Institute of Environmental Engineering, Peoples’ Friendship University of Russia, Moscow, RussiaMesoscale eddies are dynamic oceanic phenomena significantly influencing marine ecosystems’ energy transfer, nutrients, and biogeochemical cycles. These eddies’ precise identification and categorization can improve climate modeling, ocean circulation research, and environmental surveillance. This study presents an innovative methodology for mesoscale eddy detection utilizing Transformer-based deep learning models, namely, Swin Transformer U-Net and SegFormer, to categorize ocean eddies from sea surface temperature (SST) maps sourced from the copernicus marine environment monitoring service. In contrast to traditional convolutional neural networks (CNNs) that have prevailed in the domain, Transformer-based models provide superior global attention mechanisms, facilitating greater feature extraction and segmentation precision. The models are trained on labeled SST datasets and assessed using intersection over union, Dice coefficient, precision, recall, and F1-score. Experimental findings demonstrate that Transformer-based designs surpass conventional CNN-based techniques, yielding enhanced generalization and superior accuracy in classifying cyclonic and anticyclonic eddies. This study illustrates the efficacy of attention-based segmentation algorithms for resilient oceanographic applications.https://ieeexplore.ieee.org/document/11015982/Deep learningocean eddiesremote sensingSegFormersemantic segmentationsea surface temperature (SST) data |
| spellingShingle | Chen Ji Wenyang Xu Xiangtian Zheng Yasmeen Ahmed Saad Ahmed Jamal Fakhar Imam Mohammed Saleh Ali Muthanna Maha Ibrahim Sajid Ullah Dmitry E. Kucher Transformer-Based Deep Learning for Mesoscale Eddy Detection in Sea Surface Temperature Maps IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Deep learning ocean eddies remote sensing SegFormer semantic segmentation sea surface temperature (SST) data |
| title | Transformer-Based Deep Learning for Mesoscale Eddy Detection in Sea Surface Temperature Maps |
| title_full | Transformer-Based Deep Learning for Mesoscale Eddy Detection in Sea Surface Temperature Maps |
| title_fullStr | Transformer-Based Deep Learning for Mesoscale Eddy Detection in Sea Surface Temperature Maps |
| title_full_unstemmed | Transformer-Based Deep Learning for Mesoscale Eddy Detection in Sea Surface Temperature Maps |
| title_short | Transformer-Based Deep Learning for Mesoscale Eddy Detection in Sea Surface Temperature Maps |
| title_sort | transformer based deep learning for mesoscale eddy detection in sea surface temperature maps |
| topic | Deep learning ocean eddies remote sensing SegFormer semantic segmentation sea surface temperature (SST) data |
| url | https://ieeexplore.ieee.org/document/11015982/ |
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