{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T18:07:38Z","timestamp":1771006058068,"version":"3.50.1"},"reference-count":27,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,5,2]],"date-time":"2022-05-02T00:00:00Z","timestamp":1651449600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Fundamental Research Funds for the Central Universities","award":["3132022141"],"award-info":[{"award-number":["3132022141"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Oil spills can cause damage to the marine environment. When an oil spill occurs in the sea, it is critical to rapidly detect and respond to it. Because of their convenience and low cost, navigational radar images are commonly employed in oil spill detection. However, they are currently only used to assess whether or not there are oil spills, and the area affected is calculated with less accuracy. The main reason for this is that there have been very few studies on how to retrieve oil spill locations. Given the above problems, this article introduces a model of image segmentation based on the soft attention mechanism. First, the semantic segmentation model was established to fully integrate multi-scale features. It takes the target detection model based on the feature pyramid network as the backbone model, including high-level semantic information and low-level location information. The channel attention method was then used for each of the feature layers of the model to calculate the weight relationship between channels to boost the model\u2019s expressive ability for extracting oil spill features.Simultaneously, a multi-task loss function was used. Finally, the public dataset of oil spills on the sea surface was used for detection. The experimental results show that the proposed method improves the segmentation accuracy of the oil spill region. At the same time, compared with segmentation models, such as PSPNet, DeepLab V3+, and Attention U-net, the segmentation accuracy based on the pixel level improved to 95.77%, and the categorical pixel accuracy increased to 96.45%.<\/jats:p>","DOI":"10.3390\/rs14092180","type":"journal-article","created":{"date-parts":[[2022,5,2]],"date-time":"2022-05-02T07:08:58Z","timestamp":1651475338000},"page":"2180","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Oil Spill Identification in Radar Images Using a Soft Attention Segmentation Model"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8314-8593","authenticated-orcid":false,"given":"Peng","family":"Chen","sequence":"first","affiliation":[{"name":"Navigation College, Dalian Maritime University, Dalian 116026, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7328-1398","authenticated-orcid":false,"given":"Hui","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer and Software, Dalian Neusoft Information University, Dalian 116023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Li","sequence":"additional","affiliation":[{"name":"Environmental Information Institute, Dalian Maritime University, Dalian 116026, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9835-9983","authenticated-orcid":false,"given":"Bingxin","family":"Liu","sequence":"additional","affiliation":[{"name":"Navigation College, Dalian Maritime University, Dalian 116026, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Liu","sequence":"additional","affiliation":[{"name":"Navigation College, Dalian Maritime University, Dalian 116026, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.marpolbul.2012.11.025","article-title":"Investigating the Marine Protected Areas most at risk of current-driven pollution in the Gulf of Finland, the Baltic Sea, using a Lagrangian transport model","volume":"67","author":"Soomere","year":"2013","journal-title":"Mar. Pollut. Bull."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Al-Ruzouq, R., Gibril, M.B.A., Shanableh, A., Kais, A., Hamed, O., Al-Mansoori, S., and Khalil, M.A. (2020). Sensors, features, and machine learning for oil spill detection and monitoring: A review. Remote Sens., 12.","DOI":"10.3390\/rs12203338"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/srep36882","article-title":"Multidisciplinary oil spill modeling to protect coastal communities and the environment of the Eastern Mediterranean Sea","volume":"6","author":"Alves","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Fingas, M., and Brown, C.E. (2016). Oil spill remote sensing: A forensics approach. Standard Handbook Oil Spill Environmental Forensics, Elsevier.","DOI":"10.1016\/B978-0-12-803832-1.00021-0"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Fingas, M., and Brown, C.E. (2018). A review of oil spill remote sensing. Sensors, 18.","DOI":"10.3390\/s18010091"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ober, H.K. (2010). Effects of Oil Spills on Marine and Coastal Wildlife, UF\/IFAS North Florida Research and Education Center. EDIS.","DOI":"10.32473\/edis-uw330-2010"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.rse.2017.09.002","article-title":"Oil spill detection by imaging radars: Challenges and pitfalls","volume":"201","author":"Alpers","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_8","first-page":"40","article-title":"Automatic oil-spill detection by marine X-band radars","volume":"45","author":"Gangeskar","year":"2004","journal-title":"Sea Technol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Liu, P., Li, Y., Xu, J., and Zhu, X. (2017). Adaptive enhancement of X-band marine radar imagery to detect oil spill segments. Sensors, 17.","DOI":"10.3390\/s17102349"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"e290","DOI":"10.7717\/peerj-cs.290","article-title":"Hydrographic data inspection and disaster monitoring using shipborne radar small range images with electronic navigation chart","volume":"6","author":"Xu","year":"2020","journal-title":"PeerJ Comput. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Xu, J., Wang, H., Cui, C., Zhao, B., and Li, B. (2020). Oil spill monitoring of shipborne radar image features using SVM and local adaptive threshold. Algorithms, 13.","DOI":"10.3390\/a13030069"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3621","DOI":"10.1007\/s11356-018-3824-y","article-title":"Mapping terrestrial oil spill impact using machine learning random forest and Landsat 8 OLI imagery: A case site within the Niger Delta region of Nigeria","volume":"26","author":"Ozigis","year":"2019","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4933","DOI":"10.1109\/TGRS.2015.2413905","article-title":"Hierarchical conditional random fields model for semisupervised SAR image segmentation","volume":"53","author":"Zhang","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Sun, X., Lin, X., Shen, S., and Hu, Z. (2017). High-resolution remote sensing data classification over urban areas using random forest ensemble and fully connected conditional random field. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6080245"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"095985","DOI":"10.1117\/1.JRS.9.095985","article-title":"Oil spill detection method using X-band marine radar imagery","volume":"9","author":"Zhu","year":"2015","journal-title":"J. Appl. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Xu, J., Pan, X., Jia, B., Wu, X., Liu, P., and Li, B. (2021). Oil spill detection using LBP feature and K-means clustering in shipborne radar image. J. Mar. Sci. Eng., 9.","DOI":"10.3390\/jmse9010065"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.marpolbul.2014.03.059","article-title":"Review of oil spill remote sensing","volume":"83","author":"Fingas","year":"2014","journal-title":"Mar. Pollut. Bull."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid scene parsing network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1016\/j.jvcir.2018.10.001","article-title":"Attention guided U-Net for accurate iris segmentation","volume":"56","author":"Lian","year":"2018","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., and Lu, H. (2019, January 15\u201320). Dual attention network for scene segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00326"},{"key":"ref_22","unstructured":"Chen, Y. (February, January 28). Research on maritime oil spill monitoring of multi-source remote sensing image based on deep semantic segmentation. Proceedings of the 43rd COSPAR Scientific Assembly, Sydney, Australia."},{"key":"ref_23","first-page":"101901","article-title":"A machine learning approach to detect crude oil contamination in a real scenario using hyperspectral remote sensing","volume":"82","author":"Pelta","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"7405","DOI":"10.1109\/TGRS.2016.2601622","article-title":"Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images","volume":"54","author":"Cheng","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"6642","DOI":"10.3390\/s8106642","article-title":"Oil spill detection by SAR images: Dark formation detection, feature extraction and classification algorithms","volume":"8","author":"Topouzelis","year":"2008","journal-title":"Sensors"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Liu, P., Li, Y., Liu, B., Chen, P., and Xu, J. (2019). Semi-automatic oil spill detection on X-band marine radar images using texture analysis, machine learning, and adaptive thresholding. Remote Sens., 11.","DOI":"10.3390\/rs11070756"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/9\/2180\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:05:20Z","timestamp":1760137520000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/9\/2180"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,2]]},"references-count":27,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["rs14092180"],"URL":"https:\/\/doi.org\/10.3390\/rs14092180","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,2]]}}}