{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T11:27:34Z","timestamp":1780918054199,"version":"3.54.1"},"reference-count":23,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T00:00:00Z","timestamp":1704844800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Open Fund of Hubei Key Laboratory of Drilling and Production Engineering for Oil and Gas, Yangtze University","award":["YQZC202205"],"award-info":[{"award-number":["YQZC202205"]}]},{"name":"Open Fund of Hubei Key Laboratory of Drilling and Production Engineering for Oil and Gas, Yangtze University","award":["XSTS-202101"],"award-info":[{"award-number":["XSTS-202101"]}]},{"name":"Open Fund of Hubei Key Laboratory of Drilling and Production Engineering for Oil and Gas, Yangtze University","award":["D20201304"],"award-info":[{"award-number":["D20201304"]}]},{"name":"Open Fund of Xi\u2019an Key Laboratory of Tight oil (Shale oil)","award":["YQZC202205"],"award-info":[{"award-number":["YQZC202205"]}]},{"name":"Open Fund of Xi\u2019an Key Laboratory of Tight oil (Shale oil)","award":["XSTS-202101"],"award-info":[{"award-number":["XSTS-202101"]}]},{"name":"Open Fund of Xi\u2019an Key Laboratory of Tight oil (Shale oil)","award":["D20201304"],"award-info":[{"award-number":["D20201304"]}]},{"name":"Development of the Scientific Research Projects of the Hubei Provincial Department of Education","award":["YQZC202205"],"award-info":[{"award-number":["YQZC202205"]}]},{"name":"Development of the Scientific Research Projects of the Hubei Provincial Department of Education","award":["XSTS-202101"],"award-info":[{"award-number":["XSTS-202101"]}]},{"name":"Development of the Scientific Research Projects of the Hubei Provincial Department of Education","award":["D20201304"],"award-info":[{"award-number":["D20201304"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Offshore oil spills have the potential to inflict substantial ecological damage, underscoring the critical importance of timely offshore oil spill detection and remediation. At present, offshore oil spill detection typically combines hyperspectral imaging with deep learning techniques. While these methodologies have made significant advancements, they prove inadequate in scenarios requiring real-time detection due to limited model detection speeds. To address this challenge, a method for detecting oil spill areas is introduced, combining convolutional neural networks (CNNs) with the DBSCAN clustering algorithm. This method aims to enhance the efficiency of oil spill area detection in real-time scenarios, providing a potential solution to the limitations posed by the intricate structures of existing models. The proposed method includes a pre-feature selection process applied to the spectral data, followed by pixel classification using a convolutional neural network (CNN) model. Subsequently, the DBSCAN algorithm is employed to segment oil spill areas from the classification results. To validate our proposed method, we simulate an offshore oil spill environment in the laboratory, utilizing a hyperspectral sensing device to collect data and create a dataset. We then compare our method with three other models\u2014DRSNet, CNN-Visual Transformer, and GCN\u2014conducting a comprehensive analysis to evaluate the advantages and limitations of each model.<\/jats:p>","DOI":"10.3390\/s24020411","type":"journal-article","created":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T07:50:48Z","timestamp":1704873048000},"page":"411","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Offshore Oil Spill Detection Based on CNN, DBSCAN, and Hyperspectral Imaging"],"prefix":"10.3390","volume":"24","author":[{"given":"Ce","family":"Zhan","sequence":"first","affiliation":[{"name":"Hubei Key Laboratory of Drilling and Production Engineering for Oil and Gas, Yangtze University, Jingzhou 430023, China"},{"name":"Xi\u2019an Key Laboratory of Tight Oil (Shale Oil) Development, Xi\u2019an Shiyou University, Xi\u2019an 710065, China"},{"name":"School of Computer Science, Yangtze University, Jingzhou 430023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Bai","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Drilling and Production Engineering for Oil and Gas, Yangtze University, Jingzhou 430023, China"},{"name":"Xi\u2019an Key Laboratory of Tight Oil (Shale Oil) Development, Xi\u2019an Shiyou University, Xi\u2019an 710065, China"},{"name":"School of Computer Science, Yangtze University, Jingzhou 430023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Binrui","family":"Tu","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Drilling and Production Engineering for Oil and Gas, Yangtze University, Jingzhou 430023, China"},{"name":"Xi\u2019an Key Laboratory of Tight Oil (Shale Oil) Development, Xi\u2019an Shiyou University, Xi\u2019an 710065, China"},{"name":"School of Computer Science, Yangtze University, Jingzhou 430023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wanxing","family":"Zhang","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Drilling and Production Engineering for Oil and Gas, Yangtze University, Jingzhou 430023, China"},{"name":"Xi\u2019an Key Laboratory of Tight Oil (Shale Oil) Development, Xi\u2019an Shiyou University, Xi\u2019an 710065, China"},{"name":"School of Computer Science, Yangtze University, Jingzhou 430023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"042011","DOI":"10.1088\/1742-6596\/1486\/4\/042011","article-title":"Analysis of offshore oil spill pollution treatment technology","volume":"Volume 510","author":"Li","year":"2020","journal-title":"IOP Conference Series: Earth and Environmental Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"137969","DOI":"10.1016\/j.jclepro.2023.137969","article-title":"The effects of socioeconomic factors on particulate matter concentration in China\u2019s: New evidence from spatial econometric model","volume":"417","author":"Bhatti","year":"2023","journal-title":"J. Clean. Prod."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Han, H., Huang, S., Liu, S., Sha, J., and Lv, X. (2021). An Assessment of Marine Ecosystem Damage from the Penglai 19-3 Oil Spill Accident. J. Mar. Sci. Eng., 9.","DOI":"10.3390\/jmse9070732"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Al-Ruzouq, R., Gibril, M.B., 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_5","doi-asserted-by":"crossref","first-page":"1022","DOI":"10.1080\/15481603.2021.1952542","article-title":"Oil spill detection from Synthetic Aperture Radar Earth observations: A meta-analysis and comprehensive review","volume":"58","author":"Jafarzadeh","year":"2021","journal-title":"GIScience Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3416","DOI":"10.3390\/rs12203416","article-title":"Advances in remote sensing technology, machine learning and deep learning for marine oil spill detection, prediction and vulnerability assessment","volume":"12","author":"Shamsudeen","year":"2020","journal-title":"Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1016\/j.isprsjprs.2020.07.011","article-title":"A novel deep learning instance segmentation model for automated marine oil spill detection","volume":"167","author":"Yekeen","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Yang, J., Hu, Y., Ma, Y., Li, Z., and Zhang, J. (2022, January 17\u201322). Research on oil spill pollution type identification using rpnet deep learning model and airborne hyperspectral image. Proceedings of the IGARSS 2022\u20132022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia.","DOI":"10.1109\/IGARSS46834.2022.9883530"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Vasconcelos, R.N., Lima, A.T., Lentini, C.A., Miranda, J.G., de Mendon\u00e7a, L.F., Lopes, J.M., Santana, M.M., Cambu\u00ed, E.C., Souza, D.T., and Costa, D.P. (2023). Deep Learning-Based Approaches for Oil Spill Detection: A Bibliometric Review of Research Trends and Challenges. J. Mar. Sci. Eng., 11.","DOI":"10.3390\/jmse11071406"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Haut, J.M., Moreno-Alvarez, S., Pastor-Vargas, R., Perez-Garcia, A., and Paoletti, M.E. (2023). Cloud-Based Analysis of Large-Scale Hyperspectral Imagery for Oil Spill Detection. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.","DOI":"10.1109\/JSTARS.2023.3344022"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Jiang, Z., Zhang, J., Ma, Y., and Mao, X. (2021). Hyperspectral remote sensing detection of marine oil spills using an adaptive long-term moment estimation optimizer. Remote Sens., 14.","DOI":"10.3390\/rs14010157"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"120496","DOI":"10.1016\/j.eswa.2023.120496","article-title":"MFFCG\u2013Multi feature fusion for hyperspectral image classification using graph attention network","volume":"229","author":"Bhatti","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wang, D., Wan, J., Liu, S., Chen, Y., Yasir, M., Xu, M., and Ren, P. (2022). BO-DRNet: An improved deep learning model for oil spill detection by polarimetric features from SAR images. Remote Sens., 14.","DOI":"10.3390\/rs14020264"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"114981","DOI":"10.1016\/j.marpolbul.2023.114981","article-title":"An improved semantic segmentation model based on SVM for marine oil spill detection using SAR image","volume":"192","author":"Wang","year":"2023","journal-title":"Mar. Pollut. Bull."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Yang, J., Wang, J., Hu, Y., Ma, Y., Li, Z., and Zhang, J. (2023). Hyperspectral Marine Oil Spill Monitoring Using a Dual-Branch Spatial\u2013Spectral Fusion Model. Remote Sens., 15.","DOI":"10.3390\/rs15174170"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"10941","DOI":"10.1109\/JSTARS.2021.3123163","article-title":"Oil spill detection based on multiscale multidimensional residual CNN for optical remote sensing imagery","volume":"14","author":"Seydi","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"114834","DOI":"10.1016\/j.marpolbul.2023.114834","article-title":"Oil spills detection from SAR Earth observations based on a hybrid CNN transformer networks","volume":"190","author":"Akhoondzadeh","year":"2023","journal-title":"Mar. Pollut. Bull."},{"key":"ref_18","first-page":"1","article-title":"Research Progress Review of Hyperspectral Remote Sensing Image Band Selection","volume":"58","author":"Hongyan","year":"2022","journal-title":"J. Comput. Eng. Appl."},{"key":"ref_19","first-page":"10","article-title":"A new noisy random forest based method for feature selection","volume":"21","author":"Akhiat","year":"2021","journal-title":"Cybern. Inf. Technol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"114367","DOI":"10.1016\/j.enconman.2021.114367","article-title":"Performance prediction of proton-exchange membrane fuel cell based on convolutional neural network and random forest feature selection","volume":"243","author":"Huo","year":"2021","journal-title":"Energy Convers. Manag."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"6999","DOI":"10.1109\/TNNLS.2021.3084827","article-title":"A survey of convolutional neural networks: Analysis, applications, and prospects","volume":"33","author":"Li","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_22","unstructured":"Saha, P.K., and Logofatu, D. (2021, January 25\u201327). Efficient approaches for density-based spatial clustering of applications with noise. Proceedings of the Artificial Intelligence Applications and Innovations: 17th IFIP WG 12.5 International Conference, AIAI 2021, Hersonissos, Crete, Greece."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"107297","DOI":"10.1016\/j.compag.2022.107297","article-title":"HSI-TransUNet: A transformer based semantic segmentation model for crop map** from UAV hyperspectral imagery","volume":"201","author":"Niu","year":"2022","journal-title":"Comput. Electron. Agric."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/2\/411\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:43:24Z","timestamp":1760103804000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/2\/411"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,10]]},"references-count":23,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["s24020411"],"URL":"https:\/\/doi.org\/10.3390\/s24020411","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,10]]}}}