SinGAN-Labeler: An Enhanced SinGAN for Generating Marine Oil Spill SAR Images with Labels
Deep learning-based SAR oil spill detection faces significant challenges due to limited labeled training data. To address this, we propose SinGAN-Labeler, an enhanced framework that generates high-quality synthetic SAR oil spill images and their labels from minimal input. The model integrates an ada...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/3/422 |
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| author | Bin Wang Lei Chen Dongmei Song Weimin Chen Jintao Yu |
| author_facet | Bin Wang Lei Chen Dongmei Song Weimin Chen Jintao Yu |
| author_sort | Bin Wang |
| collection | DOAJ |
| description | Deep learning-based SAR oil spill detection faces significant challenges due to limited labeled training data. To address this, we propose SinGAN-Labeler, an enhanced framework that generates high-quality synthetic SAR oil spill images and their labels from minimal input. The model integrates an adaptive module to automate scale parameter optimization, accelerating training, and a hybrid attention module combining spatial, channel, and global contextual mechanisms to enhance feature extraction. By leveraging multi-scale training with diverse receptive fields, the generated images retain critical structural details while ensuring diversity. Experiments demonstrate that detection models trained on synthetic data achieve performance comparable to those using real images. Notably, expanding data sets by fivefold (from 5, 10, and 15 baseline images) improves the UNet++ model’s IoU by 78.2%, 58.5%, and 22.5%, respectively. These results validate SinGAN-Labeler’s capability to mitigate data scarcity and enhance oil spill detection accuracy, particularly under extreme sample limitations. |
| format | Article |
| id | doaj-art-a7adf99c8b964bf19006fc6095f6aa06 |
| institution | Kabale University |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-a7adf99c8b964bf19006fc6095f6aa062025-08-20T03:43:34ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-02-0113342210.3390/jmse13030422SinGAN-Labeler: An Enhanced SinGAN for Generating Marine Oil Spill SAR Images with LabelsBin Wang0Lei Chen1Dongmei Song2Weimin Chen3Jintao Yu4The College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266404, ChinaThe College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266404, ChinaThe College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266404, ChinaThe College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266404, ChinaThe College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266404, ChinaDeep learning-based SAR oil spill detection faces significant challenges due to limited labeled training data. To address this, we propose SinGAN-Labeler, an enhanced framework that generates high-quality synthetic SAR oil spill images and their labels from minimal input. The model integrates an adaptive module to automate scale parameter optimization, accelerating training, and a hybrid attention module combining spatial, channel, and global contextual mechanisms to enhance feature extraction. By leveraging multi-scale training with diverse receptive fields, the generated images retain critical structural details while ensuring diversity. Experiments demonstrate that detection models trained on synthetic data achieve performance comparable to those using real images. Notably, expanding data sets by fivefold (from 5, 10, and 15 baseline images) improves the UNet++ model’s IoU by 78.2%, 58.5%, and 22.5%, respectively. These results validate SinGAN-Labeler’s capability to mitigate data scarcity and enhance oil spill detection accuracy, particularly under extreme sample limitations.https://www.mdpi.com/2077-1312/13/3/422marine oil spillgenerative adversarial network (GAN)hybrid attention mechanismSAR image expansion |
| spellingShingle | Bin Wang Lei Chen Dongmei Song Weimin Chen Jintao Yu SinGAN-Labeler: An Enhanced SinGAN for Generating Marine Oil Spill SAR Images with Labels Journal of Marine Science and Engineering marine oil spill generative adversarial network (GAN) hybrid attention mechanism SAR image expansion |
| title | SinGAN-Labeler: An Enhanced SinGAN for Generating Marine Oil Spill SAR Images with Labels |
| title_full | SinGAN-Labeler: An Enhanced SinGAN for Generating Marine Oil Spill SAR Images with Labels |
| title_fullStr | SinGAN-Labeler: An Enhanced SinGAN for Generating Marine Oil Spill SAR Images with Labels |
| title_full_unstemmed | SinGAN-Labeler: An Enhanced SinGAN for Generating Marine Oil Spill SAR Images with Labels |
| title_short | SinGAN-Labeler: An Enhanced SinGAN for Generating Marine Oil Spill SAR Images with Labels |
| title_sort | singan labeler an enhanced singan for generating marine oil spill sar images with labels |
| topic | marine oil spill generative adversarial network (GAN) hybrid attention mechanism SAR image expansion |
| url | https://www.mdpi.com/2077-1312/13/3/422 |
| work_keys_str_mv | AT binwang singanlabeleranenhancedsinganforgeneratingmarineoilspillsarimageswithlabels AT leichen singanlabeleranenhancedsinganforgeneratingmarineoilspillsarimageswithlabels AT dongmeisong singanlabeleranenhancedsinganforgeneratingmarineoilspillsarimageswithlabels AT weiminchen singanlabeleranenhancedsinganforgeneratingmarineoilspillsarimageswithlabels AT jintaoyu singanlabeleranenhancedsinganforgeneratingmarineoilspillsarimageswithlabels |