Detecting Ocean Eddies with a Lightweight and Efficient Convolutional Network
As a ubiquitous mesoscale phenomenon, ocean eddies significantly impact ocean energy and mass exchange. Detecting these eddies accurately and efficiently has become a research focus in ocean remote sensing. Many traditional detection methods, rooted in physical principles, often encounter challenges...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/24/4808 |
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| author | Haochen Sun Hongping Li Ming Xu Tianyu Xia Hao Yu |
| author_facet | Haochen Sun Hongping Li Ming Xu Tianyu Xia Hao Yu |
| author_sort | Haochen Sun |
| collection | DOAJ |
| description | As a ubiquitous mesoscale phenomenon, ocean eddies significantly impact ocean energy and mass exchange. Detecting these eddies accurately and efficiently has become a research focus in ocean remote sensing. Many traditional detection methods, rooted in physical principles, often encounter challenges in practical applications due to their complex parameter settings, while effective, deep learning models can be limited by the high computational demands of their extensive parameters. Therefore, this paper proposes a new approach to eddy detection based on the altimeter data, the Ghost Attention Deeplab Network (GAD-Net), which is a lightweight and efficient semantic segmentation model designed to address these issues. The encoder of GAD-Net consists of a lightweight ECA+GhostNet and an Atrous Spatial Pyramid Pooling (ASPP) module. And the decoder integrates an Efficient Attention Network (EAN) module and an Efficient Ghost Feature Integration (EGFI) module. Experimental results show that GAD-Net outperforms other models in evaluation indices, with a lighter model size and lower computational complexity. It also outperforms other segmentation models in actual detection results in different sea areas. Furthermore, GAD-Net achieves detection results comparable to the Py-Eddy-Tracker (PET) method with a smaller eddy radius and a faster detection speed. The model and the constructed eddy dataset are publicly available. |
| format | Article |
| id | doaj-art-b746edf3cfe747428efe8c24d17f3365 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-b746edf3cfe747428efe8c24d17f33652024-12-27T14:51:23ZengMDPI AGRemote Sensing2072-42922024-12-011624480810.3390/rs16244808Detecting Ocean Eddies with a Lightweight and Efficient Convolutional NetworkHaochen Sun0Hongping Li1Ming Xu2Tianyu Xia3Hao Yu4College of Marine Technology, Faculty of Information Science and Engineering, Ocean University of China, Qingdao 266100, ChinaCollege of Marine Technology, Faculty of Information Science and Engineering, Ocean University of China, Qingdao 266100, ChinaCollege of Marine Technology, Faculty of Information Science and Engineering, Ocean University of China, Qingdao 266100, ChinaCollege of Marine Technology, Faculty of Information Science and Engineering, Ocean University of China, Qingdao 266100, ChinaCollege of Marine Technology, Faculty of Information Science and Engineering, Ocean University of China, Qingdao 266100, ChinaAs a ubiquitous mesoscale phenomenon, ocean eddies significantly impact ocean energy and mass exchange. Detecting these eddies accurately and efficiently has become a research focus in ocean remote sensing. Many traditional detection methods, rooted in physical principles, often encounter challenges in practical applications due to their complex parameter settings, while effective, deep learning models can be limited by the high computational demands of their extensive parameters. Therefore, this paper proposes a new approach to eddy detection based on the altimeter data, the Ghost Attention Deeplab Network (GAD-Net), which is a lightweight and efficient semantic segmentation model designed to address these issues. The encoder of GAD-Net consists of a lightweight ECA+GhostNet and an Atrous Spatial Pyramid Pooling (ASPP) module. And the decoder integrates an Efficient Attention Network (EAN) module and an Efficient Ghost Feature Integration (EGFI) module. Experimental results show that GAD-Net outperforms other models in evaluation indices, with a lighter model size and lower computational complexity. It also outperforms other segmentation models in actual detection results in different sea areas. Furthermore, GAD-Net achieves detection results comparable to the Py-Eddy-Tracker (PET) method with a smaller eddy radius and a faster detection speed. The model and the constructed eddy dataset are publicly available.https://www.mdpi.com/2072-4292/16/24/4808eddy detectiondeep learningsemantic segmentationlightweightghost attention deeplab network (GAD-Net) |
| spellingShingle | Haochen Sun Hongping Li Ming Xu Tianyu Xia Hao Yu Detecting Ocean Eddies with a Lightweight and Efficient Convolutional Network Remote Sensing eddy detection deep learning semantic segmentation lightweight ghost attention deeplab network (GAD-Net) |
| title | Detecting Ocean Eddies with a Lightweight and Efficient Convolutional Network |
| title_full | Detecting Ocean Eddies with a Lightweight and Efficient Convolutional Network |
| title_fullStr | Detecting Ocean Eddies with a Lightweight and Efficient Convolutional Network |
| title_full_unstemmed | Detecting Ocean Eddies with a Lightweight and Efficient Convolutional Network |
| title_short | Detecting Ocean Eddies with a Lightweight and Efficient Convolutional Network |
| title_sort | detecting ocean eddies with a lightweight and efficient convolutional network |
| topic | eddy detection deep learning semantic segmentation lightweight ghost attention deeplab network (GAD-Net) |
| url | https://www.mdpi.com/2072-4292/16/24/4808 |
| work_keys_str_mv | AT haochensun detectingoceaneddieswithalightweightandefficientconvolutionalnetwork AT hongpingli detectingoceaneddieswithalightweightandefficientconvolutionalnetwork AT mingxu detectingoceaneddieswithalightweightandefficientconvolutionalnetwork AT tianyuxia detectingoceaneddieswithalightweightandefficientconvolutionalnetwork AT haoyu detectingoceaneddieswithalightweightandefficientconvolutionalnetwork |