Aquaculture Areas Extraction Model Using Semantic Segmentation from Remote Sensing Images at the Maowei Sea of Beibu Gulf
The extraction of aquaculture areas from high-resolution remote sensing images is crucial for effective coastal management and resource preservation. This study introduces SwinNet, a semantic segmentation model leveraging multi-scale feature fusion to enhance the extraction of aquaculture areas, par...
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MDPI AG
2025-05-01
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| Online Access: | https://www.mdpi.com/2410-3888/10/5/236 |
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| author | Weirong Qin Mohd Hasmadi Ismail Yangyang Luo Yifeng Yuan Junlin Deng Mohammad Firuz Ramli Ning Wu |
| author_facet | Weirong Qin Mohd Hasmadi Ismail Yangyang Luo Yifeng Yuan Junlin Deng Mohammad Firuz Ramli Ning Wu |
| author_sort | Weirong Qin |
| collection | DOAJ |
| description | The extraction of aquaculture areas from high-resolution remote sensing images is crucial for effective coastal management and resource preservation. This study introduces SwinNet, a semantic segmentation model leveraging multi-scale feature fusion to enhance the extraction of aquaculture areas, particularly in the Maowei Sea of the Beibu Gulf, China. Utilizing the Swin Transformer backbone and a novel Parallel Pooling Attention Module (PPAM), SwinNet minimizes background noise and improves segmentation accuracy. SwinNet achieved a pixel accuracy of 96.53% and an intersection over the union of 93.07% on an aquaculture dataset, demonstrating superior performance in overcoming noise and accurately extracting aquaculture areas. SwinNet offers an effective solution for large-scale, high-precision monitoring of coastal aquaculture, with potential broader applicability in aquatic resource conservation and management. |
| format | Article |
| id | doaj-art-27a1c80ad16b4299a6fa7f1e380895be |
| institution | Kabale University |
| issn | 2410-3888 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Fishes |
| spelling | doaj-art-27a1c80ad16b4299a6fa7f1e380895be2025-08-20T03:47:54ZengMDPI AGFishes2410-38882025-05-0110523610.3390/fishes10050236Aquaculture Areas Extraction Model Using Semantic Segmentation from Remote Sensing Images at the Maowei Sea of Beibu GulfWeirong Qin0Mohd Hasmadi Ismail1Yangyang Luo2Yifeng Yuan3Junlin Deng4Mohammad Firuz Ramli5Ning Wu6Key Laboratory of Beibu Gulf Offshore Engineering Equipment and Technology, Beibu Gulf University, Qinzhou 535011, ChinaFaculty of Forestry and Environment, Universiti Putra Malaysia, Serdang 43400, Selangor, MalaysiaSchool of Computer, Electronics and Information, Guangxi University, Nanning 530004, ChinaKey Laboratory of Beibu Gulf Offshore Engineering Equipment and Technology, Beibu Gulf University, Qinzhou 535011, ChinaKey Laboratory of Beibu Gulf Offshore Engineering Equipment and Technology, Beibu Gulf University, Qinzhou 535011, ChinaFaculty of Forestry and Environment, Universiti Putra Malaysia, Serdang 43400, Selangor, MalaysiaKey Laboratory of Beibu Gulf Offshore Engineering Equipment and Technology, Beibu Gulf University, Qinzhou 535011, ChinaThe extraction of aquaculture areas from high-resolution remote sensing images is crucial for effective coastal management and resource preservation. This study introduces SwinNet, a semantic segmentation model leveraging multi-scale feature fusion to enhance the extraction of aquaculture areas, particularly in the Maowei Sea of the Beibu Gulf, China. Utilizing the Swin Transformer backbone and a novel Parallel Pooling Attention Module (PPAM), SwinNet minimizes background noise and improves segmentation accuracy. SwinNet achieved a pixel accuracy of 96.53% and an intersection over the union of 93.07% on an aquaculture dataset, demonstrating superior performance in overcoming noise and accurately extracting aquaculture areas. SwinNet offers an effective solution for large-scale, high-precision monitoring of coastal aquaculture, with potential broader applicability in aquatic resource conservation and management.https://www.mdpi.com/2410-3888/10/5/236remote sensing imagesaquaculture areassemantic segmentationswin transformermulti-scale feature fusion |
| spellingShingle | Weirong Qin Mohd Hasmadi Ismail Yangyang Luo Yifeng Yuan Junlin Deng Mohammad Firuz Ramli Ning Wu Aquaculture Areas Extraction Model Using Semantic Segmentation from Remote Sensing Images at the Maowei Sea of Beibu Gulf Fishes remote sensing images aquaculture areas semantic segmentation swin transformer multi-scale feature fusion |
| title | Aquaculture Areas Extraction Model Using Semantic Segmentation from Remote Sensing Images at the Maowei Sea of Beibu Gulf |
| title_full | Aquaculture Areas Extraction Model Using Semantic Segmentation from Remote Sensing Images at the Maowei Sea of Beibu Gulf |
| title_fullStr | Aquaculture Areas Extraction Model Using Semantic Segmentation from Remote Sensing Images at the Maowei Sea of Beibu Gulf |
| title_full_unstemmed | Aquaculture Areas Extraction Model Using Semantic Segmentation from Remote Sensing Images at the Maowei Sea of Beibu Gulf |
| title_short | Aquaculture Areas Extraction Model Using Semantic Segmentation from Remote Sensing Images at the Maowei Sea of Beibu Gulf |
| title_sort | aquaculture areas extraction model using semantic segmentation from remote sensing images at the maowei sea of beibu gulf |
| topic | remote sensing images aquaculture areas semantic segmentation swin transformer multi-scale feature fusion |
| url | https://www.mdpi.com/2410-3888/10/5/236 |
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