A Two-Stage Deep Learning Method for Marine Oil Spill Localization and Segmentation From Synthetic Aperture Radar Images
Oil spills have devastating impacts on ocean ecosystems, leading to significant economic losses. Consequently, it is crucial to develop effective techniques for the rapid detection of marine oil spills using satellite observations. Existing methods for detecting oil spills using synthetic aperture r...
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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/10990149/ |
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| author | Xudong Huang Biao Zhang William Perrie |
| author_facet | Xudong Huang Biao Zhang William Perrie |
| author_sort | Xudong Huang |
| collection | DOAJ |
| description | Oil spills have devastating impacts on ocean ecosystems, leading to significant economic losses. Consequently, it is crucial to develop effective techniques for the rapid detection of marine oil spills using satellite observations. Existing methods for detecting oil spills using synthetic aperture radar (SAR) often face challenges related to algorithm complexity, limited data availability, and practical implementation. To address these issues, we propose a two-stage deep learning (DL) method to automatically localize and segment oil spills in Sentinel-1 SAR images. First, we utilize the faster R-CNN model to identify oil spill locations. Subsequently, subimages containing the oil spills are input into the UNet<sup>++</sup> model to achieve pixel-wise segmentation. We have created a comprehensive dataset consisting of 3166 well-annotated oil spill samples derived from 1000 Sentinel-1 VV-polarized SAR images. This unique dataset will be used for training, validation, and testing the two DL models. Experimental results indicate that the precision and recall for oil spill localization are 87.46% and 87.59%, respectively, while for oil spill segmentation, they are 89.55% and 89.37%. The proposed method demonstrates optimal oil detection performance at wind speeds between 2 and 10 m/s. Furthermore, the total runtime for oil spill localization and segmentation is less than 1 min for each full-resolution SAR image. These results suggest that the proposed approach has strong potential for operational oil spill monitoring using spaceborne SAR images. |
| format | Article |
| id | doaj-art-124e9146b83d410ca47dfc2b10add450 |
| 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-124e9146b83d410ca47dfc2b10add4502025-08-20T03:48:27ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118123151232710.1109/JSTARS.2025.356785910990149A Two-Stage Deep Learning Method for Marine Oil Spill Localization and Segmentation From Synthetic Aperture Radar ImagesXudong Huang0Biao Zhang1https://orcid.org/0000-0001-6569-1998William Perrie2State Key Laboratory of Climate System Prediction and Risk Management, Nanjing University of Information Science and Technology, Nanjing, ChinaSchool of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing, ChinaFisheries and Oceans Canada, Bedford Institute of Oceanography, Dartmouth, NS, CanadaOil spills have devastating impacts on ocean ecosystems, leading to significant economic losses. Consequently, it is crucial to develop effective techniques for the rapid detection of marine oil spills using satellite observations. Existing methods for detecting oil spills using synthetic aperture radar (SAR) often face challenges related to algorithm complexity, limited data availability, and practical implementation. To address these issues, we propose a two-stage deep learning (DL) method to automatically localize and segment oil spills in Sentinel-1 SAR images. First, we utilize the faster R-CNN model to identify oil spill locations. Subsequently, subimages containing the oil spills are input into the UNet<sup>++</sup> model to achieve pixel-wise segmentation. We have created a comprehensive dataset consisting of 3166 well-annotated oil spill samples derived from 1000 Sentinel-1 VV-polarized SAR images. This unique dataset will be used for training, validation, and testing the two DL models. Experimental results indicate that the precision and recall for oil spill localization are 87.46% and 87.59%, respectively, while for oil spill segmentation, they are 89.55% and 89.37%. The proposed method demonstrates optimal oil detection performance at wind speeds between 2 and 10 m/s. Furthermore, the total runtime for oil spill localization and segmentation is less than 1 min for each full-resolution SAR image. These results suggest that the proposed approach has strong potential for operational oil spill monitoring using spaceborne SAR images.https://ieeexplore.ieee.org/document/10990149/Deep learning (DL)faster R-CNNoil spillsynthetic aperture radar (SAR)UNet<sup xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">++</sup> |
| spellingShingle | Xudong Huang Biao Zhang William Perrie A Two-Stage Deep Learning Method for Marine Oil Spill Localization and Segmentation From Synthetic Aperture Radar Images IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Deep learning (DL) faster R-CNN oil spill synthetic aperture radar (SAR) UNet<sup xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">++</sup> |
| title | A Two-Stage Deep Learning Method for Marine Oil Spill Localization and Segmentation From Synthetic Aperture Radar Images |
| title_full | A Two-Stage Deep Learning Method for Marine Oil Spill Localization and Segmentation From Synthetic Aperture Radar Images |
| title_fullStr | A Two-Stage Deep Learning Method for Marine Oil Spill Localization and Segmentation From Synthetic Aperture Radar Images |
| title_full_unstemmed | A Two-Stage Deep Learning Method for Marine Oil Spill Localization and Segmentation From Synthetic Aperture Radar Images |
| title_short | A Two-Stage Deep Learning Method for Marine Oil Spill Localization and Segmentation From Synthetic Aperture Radar Images |
| title_sort | two stage deep learning method for marine oil spill localization and segmentation from synthetic aperture radar images |
| topic | Deep learning (DL) faster R-CNN oil spill synthetic aperture radar (SAR) UNet<sup xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">++</sup> |
| url | https://ieeexplore.ieee.org/document/10990149/ |
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