Edge-aware transformer for coastal raft aquaculture extraction in optical remote sensing imagery
To scientifically plan and accurately manage the coastal aquaculture industry, it is especially critical to quickly and accurately extract raft aquaculture areas. In the study, the Raft-Former was designed to accurately extract coastal raft aquaculture in Sansha Bay using Sentinel-2 remote sensing i...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-08-01
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| Series: | International Journal of Digital Earth |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2484669 |
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| author | Hua Su Yuxin Liu Zhanchao Huang An Wang Wenjun Hong Junchao Cai |
| author_facet | Hua Su Yuxin Liu Zhanchao Huang An Wang Wenjun Hong Junchao Cai |
| author_sort | Hua Su |
| collection | DOAJ |
| description | To scientifically plan and accurately manage the coastal aquaculture industry, it is especially critical to quickly and accurately extract raft aquaculture areas. In the study, the Raft-Former was designed to accurately extract coastal raft aquaculture in Sansha Bay using Sentinel-2 remote sensing imagery. Specifically, a Feature Enhancement Module (FEM) was designed to selectively learn the interest features for solving the omission and mis-extraction caused by changes in the coastal environment. For the boundary adhesion problems caused by the dense distribution of raft aquaculture areas, a Feature Alignment Module (FAM) was developed to enhance edge-aware ability. A Global-Local Fusion Module (GLFM) was introduced to effectively integrate the local features with multi-scale and global features to overcome significant scale differences in aquaculture areas. Numerous experiments show that our method is better than the state-of-the-art models. Specifically, Raft-Former respectively achieves 90.05% and 86.73% mIoU on the Sansha Bay dataset. |
| format | Article |
| id | doaj-art-947a107fff9347ceb7a38d48a01edf5d |
| institution | Kabale University |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-947a107fff9347ceb7a38d48a01edf5d2025-08-25T11:28:27ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552025-08-0118110.1080/17538947.2025.2484669Edge-aware transformer for coastal raft aquaculture extraction in optical remote sensing imageryHua Su0Yuxin Liu1Zhanchao Huang2An Wang3Wenjun Hong4Junchao Cai5Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, The Academy of Digital China, Fuzhou University, Fuzhou, ChinaKey Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, The Academy of Digital China, Fuzhou University, Fuzhou, ChinaKey Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, The Academy of Digital China, Fuzhou University, Fuzhou, ChinaKey Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, The Academy of Digital China, Fuzhou University, Fuzhou, ChinaKey Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, The Academy of Digital China, Fuzhou University, Fuzhou, ChinaKey Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, The Academy of Digital China, Fuzhou University, Fuzhou, ChinaTo scientifically plan and accurately manage the coastal aquaculture industry, it is especially critical to quickly and accurately extract raft aquaculture areas. In the study, the Raft-Former was designed to accurately extract coastal raft aquaculture in Sansha Bay using Sentinel-2 remote sensing imagery. Specifically, a Feature Enhancement Module (FEM) was designed to selectively learn the interest features for solving the omission and mis-extraction caused by changes in the coastal environment. For the boundary adhesion problems caused by the dense distribution of raft aquaculture areas, a Feature Alignment Module (FAM) was developed to enhance edge-aware ability. A Global-Local Fusion Module (GLFM) was introduced to effectively integrate the local features with multi-scale and global features to overcome significant scale differences in aquaculture areas. Numerous experiments show that our method is better than the state-of-the-art models. Specifically, Raft-Former respectively achieves 90.05% and 86.73% mIoU on the Sansha Bay dataset.https://www.tandfonline.com/doi/10.1080/17538947.2025.2484669Raft extractionedge-awaremulti-scaleSentinel-2 remote sensing imagery |
| spellingShingle | Hua Su Yuxin Liu Zhanchao Huang An Wang Wenjun Hong Junchao Cai Edge-aware transformer for coastal raft aquaculture extraction in optical remote sensing imagery International Journal of Digital Earth Raft extraction edge-aware multi-scale Sentinel-2 remote sensing imagery |
| title | Edge-aware transformer for coastal raft aquaculture extraction in optical remote sensing imagery |
| title_full | Edge-aware transformer for coastal raft aquaculture extraction in optical remote sensing imagery |
| title_fullStr | Edge-aware transformer for coastal raft aquaculture extraction in optical remote sensing imagery |
| title_full_unstemmed | Edge-aware transformer for coastal raft aquaculture extraction in optical remote sensing imagery |
| title_short | Edge-aware transformer for coastal raft aquaculture extraction in optical remote sensing imagery |
| title_sort | edge aware transformer for coastal raft aquaculture extraction in optical remote sensing imagery |
| topic | Raft extraction edge-aware multi-scale Sentinel-2 remote sensing imagery |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2025.2484669 |
| work_keys_str_mv | AT huasu edgeawaretransformerforcoastalraftaquacultureextractioninopticalremotesensingimagery AT yuxinliu edgeawaretransformerforcoastalraftaquacultureextractioninopticalremotesensingimagery AT zhanchaohuang edgeawaretransformerforcoastalraftaquacultureextractioninopticalremotesensingimagery AT anwang edgeawaretransformerforcoastalraftaquacultureextractioninopticalremotesensingimagery AT wenjunhong edgeawaretransformerforcoastalraftaquacultureextractioninopticalremotesensingimagery AT junchaocai edgeawaretransformerforcoastalraftaquacultureextractioninopticalremotesensingimagery |