{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T04:50:49Z","timestamp":1791867049607,"version":"4.3.4"},"reference-count":107,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T00:00:00Z","timestamp":1674086400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Marine Pollution Bulletin"],"published-print":{"date-parts":[[2023,3]]},"DOI":"10.1016\/j.marpolbul.2023.114598","type":"journal-article","created":{"date-parts":[[2023,2,10]],"date-time":"2023-02-10T03:21:24Z","timestamp":1675999284000},"page":"114598","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":29,"special_numbering":"C","title":["Detection of marine oil-like features in Sentinel-1 SAR images by supplementary use of deep learning and empirical methods: Performance assessment for the Great Barrier Reef marine park"],"prefix":"10.1016","volume":"188","author":[{"given":"David","family":"Blondeau-Patissier","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Schroeder","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gopika","family":"Suresh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhibin","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Foivos I.","family":"Diakogiannis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul","family":"Irving","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christian","family":"Witte","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew D.L.","family":"Steven","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.marpolbul.2023.114598_bb0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.marpolbul.2022.113718","article-title":"Monitoring of oil spill in the offshore zone of the Nile Delta using sentinel data","volume":"179","author":"Abou Samra","year":"2022","journal-title":"Mar. Pollut. Bull."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0010","doi-asserted-by":"crossref","DOI":"10.3390\/rs12203338","article-title":"Sensors, features, and machine learning for oil spill detection and monitoring: a review","volume":"12","author":"Al-Ruzouq","year":"2020","journal-title":"Remote Sens."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0015","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1186\/s40537-021-00444-8","article-title":"Review of deep learning: concepts, CNN architectures, challenges, applications, future directions","volume":"8","author":"Alzubaidi","year":"2021","journal-title":"J. Big Data"},{"key":"10.1016\/j.marpolbul.2023.114598_bb0020","series-title":"Tail Dark Spot Classification Using SAR Imagery and Wind Parameters for Oil Slick Disaster Prevention, SPSympo (Signal Processing Symposium), Krakow; Poland, 17-19 September","first-page":"265","author":"Benito-Ortiz","year":"2019"},{"key":"10.1016\/j.marpolbul.2023.114598_bb0025","series-title":"CSIRO, Brisbane, Australia, Technical Report 978-1-4863-1338-9","first-page":"95","article-title":"Satellite detection of oil spills in the Great Barrier Reef using the Sentinel-1, -2 and -3 satellite constellations - a technical assessment of a synergistic approach using SAR, optical and thermal information","author":"Blondeau-Patissier","year":"2020"},{"key":"10.1016\/j.marpolbul.2023.114598_bb0030","series-title":"CSIRO DAP (The Commonwealth Scientific and Industrial Research Organisation Data Access Portal)","article-title":"CSIRO Sentinel-1 SAR image dataset of oil- and non-oil features for machine learning (Deep Learning) (version 1)","author":"Blondeau-Patissier","year":"2022"},{"key":"10.1016\/j.marpolbul.2023.114598_bb0035","series-title":"Maritime Surveillance With Synthetic Aperture Radar","first-page":"227","article-title":"SAR oil spill imaging, interpretation and information retrieval techniques","author":"Brekke","year":"2021"},{"key":"10.1016\/j.marpolbul.2023.114598_bb0040","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2004.11.015","article-title":"Oil spill detection by satellite remote sensing","volume":"95","author":"Brekke","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0045","doi-asserted-by":"crossref","DOI":"10.1029\/2020JC016499","article-title":"Integrated analysis of multisensor datasets and oil drift simulations\u2014a free-floating oil experiment in the Open Ocean","volume":"126","author":"Brekke","year":"2021","journal-title":"J. Geophys. Res. Oceans"},{"key":"10.1016\/j.marpolbul.2023.114598_bb0050","series-title":"International Oil Spill Conference Proceedings","first-page":"2005","article-title":"Full scale implementation of state of the art mechanical oil spill response technology on the norwegian continental shelf","volume":"831\u2013836","author":"Brekne","year":"2005"},{"key":"10.1016\/j.marpolbul.2023.114598_bb0055","doi-asserted-by":"crossref","first-page":"105716","DOI":"10.1016\/j.asoc.2019.105716","article-title":"Oil spill segmentation in SAR images using convolutional neural networks. A comparative analysis with clustering and logistic regression algorithms","volume":"84","author":"Cantorna","year":"2019","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0060","series-title":"Illegal Oil Discharges From Vessels: Investigative Manual","volume":"118","author":"Caston","year":"2007"},{"key":"10.1016\/j.marpolbul.2023.114598_bb0065","doi-asserted-by":"crossref","DOI":"10.3390\/rs13091737","article-title":"Detection of biogenic oil films near aquaculture sites using Sentinel-1 and Sentinel-2 satellite images","volume":"13","author":"Chatziantoniou","year":"2021","journal-title":"Remote Sens."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0070","doi-asserted-by":"crossref","DOI":"10.1016\/j.asr.2020.05.003","article-title":"Marine oil slicks detection using spaceborne and airborne SAR data","author":"Chaudhary","year":"2020","journal-title":"Adv. Space Res."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0075","doi-asserted-by":"crossref","DOI":"10.3390\/rs13112044","article-title":"SAR oil spill detection system through random Forest classifiers","volume":"13","author":"Concei\u00e7\u00e3o","year":"2021","journal-title":"Remote Sens."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0080","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/0034-4257(91)90048-B","article-title":"A review of assessing the accuracy of classifications of remotely sensed data","volume":"37","author":"Congalton","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0085","article-title":"Application of C-band sentinel-1A SAR data as proxies for detecting oil spills of Chennai, East Coast of India","volume":"174","author":"Dasari","year":"2021","journal-title":"Mar. Pollut. Bull."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0090","series-title":"State and Trends of Australia\u2019s Ocean (STAR)","article-title":"Spatial and Seasonal trends in Trichodesmium","volume":"6","author":"Davies","year":"2020"},{"key":"10.1016\/j.marpolbul.2023.114598_bb0095","doi-asserted-by":"crossref","DOI":"10.1117\/1.JMI.6.2.025503","article-title":"Comparison of deep learning and human observer performance for detection and characterization of simulated lesions","volume":"6","author":"De Man","year":"2019","journal-title":"J. Med. Imaging"},{"key":"10.1016\/j.marpolbul.2023.114598_bb0100","series-title":"IGARSS (International Geoscience and Remote Sensing Symposium), Hamburg, Ger., 28 June - 02 July","first-page":"2282","article-title":"Neural networks for oil spill detection using ERS-SAR data","author":"Del Frate","year":"2000"},{"key":"10.1016\/j.marpolbul.2023.114598_bb0105","series-title":"IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA, 20-25 June","first-page":"248","article-title":"Imagenet: A large-scale hierarchical image database","author":"Deng","year":"2009"},{"key":"10.1016\/j.marpolbul.2023.114598_bb0110","series-title":"Maritime Surveillance with Synthetic Aperture Radar","first-page":"293","article-title":"Sea state and wind speed","author":"Di Martino","year":"2020"},{"key":"10.1016\/j.marpolbul.2023.114598_bb0115","series-title":"Indonesians Are Still Fighting to be Heard","article-title":"\u2018Very hard life now\u2019: 12 years after the Montara oil spill","author":"Doherty","year":"2022"},{"key":"10.1016\/j.marpolbul.2023.114598_bb0120","doi-asserted-by":"crossref","first-page":"1300","DOI":"10.1126\/science.abm5940","article-title":"Chronic oiling in global oceans","volume":"376","author":"Dong","year":"2022","journal-title":"Science"},{"key":"10.1016\/j.marpolbul.2023.114598_bb0125","article-title":"A global assessment of atoll island planform changes over the past decades","volume":"10","author":"Duvat","year":"2018","journal-title":"Wiley Interdiscip. Rev. Clim. Chang."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0130","doi-asserted-by":"crossref","first-page":"15129","DOI":"10.1038\/s41598-019-51468-3","article-title":"Rapid human-driven undermining of atoll island capacity to adjust to ocean climate-related pressures","volume":"9","author":"Duvat","year":"2019","journal-title":"Sci. Rep."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0135","series-title":"Advances in Environmental Research","first-page":"95","article-title":"An update on oil spill remote sensing","author":"Fingas","year":"2021"},{"key":"10.1016\/j.marpolbul.2023.114598_bb0140","article-title":"A review of oil spill remote sensing","volume":"18","author":"Fingas","year":"2018","journal-title":"Sensors"},{"key":"10.1016\/j.marpolbul.2023.114598_bb0145","series-title":"Synthetic Aperture Radar Image Processing using the Supervised Textural-Neural Network Classification Algorithm, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium, Boston, USA, 7-11 July","first-page":"1265","author":"Garcia-Pineda","year":"2008"},{"key":"10.1016\/j.marpolbul.2023.114598_bb0150","doi-asserted-by":"crossref","first-page":"411","DOI":"10.5589\/m09-035","article-title":"Using SAR images to delineate ocean oil slicks with a texture-classifying neural network algorithm (TC-NNA)","volume":"35","author":"Garcia-Pineda","year":"2009","journal-title":"Can. J. Remote. Sens."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0155","doi-asserted-by":"crossref","first-page":"2517","DOI":"10.1109\/JSTARS.2013.2244061","article-title":"Oil spill mapping and measurement in the Gulf of Mexico with textural classifier neural network algorithm (TC-NNA)","volume":"6","author":"Garcia-Pineda","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0160","doi-asserted-by":"crossref","DOI":"10.3390\/rs9060567","article-title":"Detection of oil near shorelines during the Deepwater Horizon oil spill using synthetic aperture radar (SAR)","volume":"9","author":"Garcia-Pineda","year":"2017","journal-title":"Remote Sens."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0165","series-title":"Great Barrier Reef Outlook Report, Great Barrier Reef Marine Park Authority, Townsville, Report ER 978-0-6483570-5-6","volume":"374","author":"GBRMPA","year":"2019"},{"key":"10.1016\/j.marpolbul.2023.114598_bb0170","series-title":"Green Ships, Eco Shipping, Clean Seas: Proceedings of the 12th Annual General Assembly of the International Association of Maritime Universities, AGA-IAMU 2011","first-page":"145","article-title":"EMSA cleanseanet system in its practical implementation","author":"Gegenava","year":"2020"},{"key":"10.1016\/j.marpolbul.2023.114598_bb0175","doi-asserted-by":"crossref","first-page":"1879","DOI":"10.1016\/j.marpolbul.2021.112892","article-title":"Fate of MV wakashio oil spill off Mauritius coast through modelling and remote sensing observations","volume":"172","author":"Gurumoorthi","year":"2021","journal-title":"Mar. Pollut. Bull."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0180","series-title":"Construction and Assessment of Classification Rules","author":"Hand","year":"1997"},{"key":"10.1016\/j.marpolbul.2023.114598_bb0185","doi-asserted-by":"crossref","DOI":"10.1016\/j.ocecoaman.2022.106103","article-title":"Management of marine oil spills: a case study of the wakashio oil spill in Mauritius using a lens-actor-focus conceptual framework","volume":"221","author":"Hebbar","year":"2022","journal-title":"Ocean Coast. Manag."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0190","series-title":"Oil Spill Monitoring Handbook","author":"Hook","year":"2016"},{"key":"10.1016\/j.marpolbul.2023.114598_bb0195","doi-asserted-by":"crossref","first-page":"9141902","DOI":"10.34133\/2021\/9141902","article-title":"Optical remote sensing of oil spills in the ocean: what is really possible?","volume":"2021","author":"Hu","year":"2021","journal-title":"J. Remote Sens."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0200","doi-asserted-by":"crossref","DOI":"10.1016\/j.marpolbul.2022.113666","article-title":"A novel deep learning method for marine oil spill detection from satellite synthetic aperture radar imagery","volume":"179","author":"Huang","year":"2022","journal-title":"Mar. Pollut. Bull."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0205","series-title":"6th International Conference on Smart and Sustainable Technologies, SpliTech 2021, Split, Croatia, 8 - 11 September 1-6","article-title":"An assessment of social media oil spill reports using transfer learning on remotely sensed images","author":"Ivanda","year":"2021"},{"key":"10.1016\/j.marpolbul.2023.114598_bb0210","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":"Gisci. Remote Sens."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0215","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.rse.2017.01.031","article-title":"Monitoring of natural oil seepage in the lower Congo Basin using SAR observations","volume":"191","author":"Jatiault","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0220","series-title":"International Oil Spill Conference Proceedings","first-page":"2008","article-title":"Recent experience from multiple remote sensing and monitoring to improve oil spill response operations","volume":"407\u2013412","author":"Jensen","year":"2008"},{"key":"10.1016\/j.marpolbul.2023.114598_bb0225","series-title":"Early Identification of Oil Spills in Satellite Images Using Deep CNNs","author":"Krestenitis","year":"2019"},{"key":"10.1016\/j.marpolbul.2023.114598_bb0230","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1109\/TPAMI.1980.4766994","article-title":"Digital image enhancement and noise filtering by use of local statistics","volume":"PAMI-2","author":"Lee","year":"1980","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0235","doi-asserted-by":"crossref","DOI":"10.1016\/j.mex.2022.101756","article-title":"An inverse planned oil release validation method for estimating oil slick thickness from thermal contrast remote sensing by in-scene calibration","volume":"9","author":"Leifer","year":"2022","journal-title":"MethodsX"},{"key":"10.1016\/j.marpolbul.2023.114598_bb0240","article-title":"Deep learning in multimodal remote sensing data fusion: a comprehensive review","volume":"112","author":"Li","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0245","doi-asserted-by":"crossref","first-page":"1584","DOI":"10.1093\/nsr\/nwaa047","article-title":"Deep-learning-based information mining from ocean remote-sensing imagery","volume":"7","author":"Li","year":"2020","journal-title":"Natl. Sci. Rev."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0250","doi-asserted-by":"crossref","DOI":"10.3390\/ijgi8040160","article-title":"A spectral feature-based convolutional neural network for classification of sea surface oil spill","volume":"8","author":"Liu","year":"2019","journal-title":"ISPRS Int. J. Geo Inf."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0255","doi-asserted-by":"crossref","DOI":"10.1016\/j.ocecoaman.2021.105783","article-title":"Tracing illegal oil discharges from vessels using SAR and AIS in Bohai Sea of China","volume":"211","author":"Liu","year":"2021","journal-title":"Ocean Coast. Manag."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0260","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.rse.2016.04.007","article-title":"Detecting and quantifying oil slick thickness by thermal remote sensing: a ground-based experiment","volume":"181","author":"Lu","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0265","series-title":"Oil Spill Segmentation of SAR Image Based on Improved Deep Convolution Neural Network, Photonics & Electromagnetics Research Symposium (PIERS), Hangzhou, China, 21-25 Nov","first-page":"2232","author":"Luo","year":"2021"},{"key":"10.1016\/j.marpolbul.2023.114598_bb0275","doi-asserted-by":"crossref","first-page":"9364","DOI":"10.3390\/s22239364","article-title":"A critical review of deep learning-based multi-sensor fusion techniques","volume":"22","author":"Marsh","year":"2022","journal-title":"Sensors"},{"key":"10.1016\/j.marpolbul.2023.114598_bb0280","doi-asserted-by":"crossref","first-page":"2784","DOI":"10.1080\/01431161.2018.1433343","article-title":"Implementation of machine-learning classification in remote sensing: an applied review","volume":"39","author":"Maxwell","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0285","first-page":"1489","article-title":"Applicability analysis of VGGNET-based transfer learning for oil spill classification on SAR data","volume":"12","author":"Mehta","year":"2021","journal-title":"Turk. J. Comput. Math. Educ."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0290","article-title":"Radar backscattering over sea surface oil emulsions: simulation and observation","volume":"1\u201314","author":"Meng","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0295","series-title":"Handbook of Environmental Chemistry: Oil Pollution in the Mediterranean Sea: Part II","first-page":"159","article-title":"Mapping of oil slicks in the Adriatic Sea: Croatia case study","author":"Morovi\u0107","year":"2018"},{"key":"10.1016\/j.marpolbul.2023.114598_bb0300","doi-asserted-by":"crossref","DOI":"10.1016\/j.jlp.2022.104912","article-title":"Oil spills characteristics, detection, and recovery methods: a systematic risk-based view","volume":"80","author":"Negreiros","year":"2022","journal-title":"J. Loss Prev. Process Ind."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0305","doi-asserted-by":"crossref","first-page":"5217","DOI":"10.1109\/TGRS.2018.2812619","article-title":"Two-stage convolutional neural network for ship and spill detection using SLAR images","volume":"56","author":"Nieto-Hidalgo","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0310","first-page":"82","article-title":"Science, monitoring and management for offshore petroleum activities","volume":"4","author":"Oliver","year":"2012","journal-title":"Austral. J. Mar. Ocean Aff."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0315","series-title":"25th IEEE International Conference on Image Processing (ICIP), Athens, Greece, 07-10 Oct","article-title":"A deep neural network for oil spill semantic segmentation in SAR images","author":"Orfanidis","year":"2018"},{"key":"10.1016\/j.marpolbul.2023.114598_bb0320","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A survey on transfer learning","volume":"22","author":"Pan","year":"2010","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0325","doi-asserted-by":"crossref","DOI":"10.3390\/rs13081422","article-title":"Applications of satellite radar imagery for Hazard monitoring: insights from Australia","volume":"13","author":"Parker","year":"2021","journal-title":"Remote Sens."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0330","series-title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library, NeurIPS 2019 - 33rd Conference on Neural Information Processing System, Vancouver, Canada, 03 Dec","first-page":"8024","author":"Paszke","year":"2019"},{"key":"10.1016\/j.marpolbul.2023.114598_bb0335","doi-asserted-by":"crossref","DOI":"10.1016\/j.rse.2022.113188","article-title":"On the capacity of Sentinel-1 synthetic aperture radar in detecting floating macroalgae and other floating matters","volume":"280","author":"Qi","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0340","doi-asserted-by":"crossref","DOI":"10.1016\/j.envpol.2021.116618","article-title":"Detection of wakashio oil spill off Mauritius using Sentinel-1 and 2 data: capability of sensors, image transformation methods and mapping","volume":"274","author":"Rajendran","year":"2021","journal-title":"Environ. Pollut."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0345","doi-asserted-by":"crossref","DOI":"10.1016\/j.marpolbul.2022.113330","article-title":"History of a disaster: a baseline assessment of the wakashio oil spill on the coast of Mauritius, Indian Ocean","volume":"175","author":"Rajendran","year":"2022","journal-title":"Mar. Pollut. Bull."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0350","doi-asserted-by":"crossref","DOI":"10.1016\/j.marpolbul.2022.113994","article-title":"Coastal zone management in Sri Lanka: a lesson after recent naval accidents","volume":"182","author":"Ratnayake","year":"2022","journal-title":"Mar. Pollut. Bull."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0355","series-title":"Wiley-Praxis Series in Remote Sensing","article-title":"Satellite oceanography: an introduction for oceanographers and remote-sensing scientists","author":"Robinson","year":"1995"},{"key":"10.1016\/j.marpolbul.2023.114598_bb0360","first-page":"1","article-title":"Remote sensing of Trichodesmium spp. mats in the Western Tropical South Pacific","author":"Rousset","year":"2018","journal-title":"Biogeosciences"},{"key":"10.1016\/j.marpolbul.2023.114598_bb0365","series-title":"Earth Observation for monitoring of Australian Marine Parks and other off-shore Marine Protected Areas. Geoscience Australia, Canberra, Australia","volume":"71","author":"Sagar","year":"2020"},{"key":"10.1016\/j.marpolbul.2023.114598_bb0370","series-title":"4th International Symposium on Geoinformatics (ISyG), Malang, Indonesia, 10 - 12 November","first-page":"1","article-title":"Oil spill mapping using multi-sensor Sentinel data in Balikpapan Bay, Indonesia","author":"Setiani","year":"2018"},{"key":"10.1016\/j.marpolbul.2023.114598_bb0375","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 Observ. Remote Sens."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0380","doi-asserted-by":"crossref","first-page":"2026","DOI":"10.1016\/j.rse.2010.04.009","article-title":"Dark-spot detection from SAR intensity imagery with spatial density thresholding for oil-spill monitoring","volume":"114","author":"Shu","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0385","series-title":"Very Deep Convolutional Networks for Large-scale Image Recognition, International Conference on Learning Representations (ICLR) San Diego, CA, USA, 10 April","author":"Simonyan","year":"2015"},{"key":"10.1016\/j.marpolbul.2023.114598_bb0390","series-title":"2012 IEEE International Geoscience and Remote Sensing Symposium, Munich. Germany, 22-27 July","first-page":"5630","article-title":"Detection and classification of oil spill and look-alike spots from SAR imagery using an artificial neural network","author":"Singha","year":"2012"},{"key":"10.1016\/j.marpolbul.2023.114598_bb0395","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.rse.2018.05.001","article-title":"Effect of wind direction and incidence angle on polarimetric SAR observations of slicked and unslicked sea surfaces","volume":"213","author":"Skrunes","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0400","series-title":"IGARSS 2004 - Geoscience and Remote Sensing Symposium, 20-24 Sept","first-page":"4909","article-title":"Algorithms for oil spill detection in Radarsat and ENVISAT SAR images","author":"Solberg","year":"2004"},{"key":"10.1016\/j.marpolbul.2023.114598_bb0405","doi-asserted-by":"crossref","first-page":"746","DOI":"10.1109\/TGRS.2006.887019","article-title":"Oil spill detection in radarsat and envisat SAR images","volume":"45","author":"Solberg","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0410","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1007\/s00244-016-0356-7","article-title":"The montara oil spill: a 2009 well blowout in the Timor Sea","volume":"73","author":"Spies","year":"2017","journal-title":"Arch. Environ. Contam. Toxicol."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0415","first-page":"1","article-title":"eReefs: An operational information system for managing the Great Barrier Reef","author":"Steven","year":"2019","journal-title":"Journal of Operational Oceanography"},{"key":"10.1016\/j.marpolbul.2023.114598_bb0420","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1016\/j.marpolbul.2015.12.003","article-title":"Oil slick morphology derived from AVIRIS measurements of the Deepwater horizon oil spill: implications for spatial resolution requirements of remote sensors","volume":"103","author":"Sun","year":"2016","journal-title":"Mar. Pollut. Bull."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0425","article-title":"Tracking an oil tanker collision and spilled oils in the East China Sea using multisensor day and night satellite imagery","author":"Sun","year":"2018","journal-title":"Geophys. Res. Lett."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0430","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1016\/j.marpolbul.2019.07.015","article-title":"Oil pollution in the eastern Arabian Sea from invisible sources: a multi-technique approach","volume":"146","author":"Suneel","year":"2019","journal-title":"Mar. Pollut. Bull."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0435","series-title":"Offshore Oil Seepage Visible From Space: A Synthetic Aperture Radar (SAR) Based Automatic Detection, Mapping and Quantification System","author":"Suresh","year":"2015"},{"key":"10.1016\/j.marpolbul.2023.114598_bb0440","series-title":"IEEE International Geoscience and Remote Sensing Symposium - IGARSS, 21-26 July 2013","first-page":"3566","article-title":"An automatic detection system for natural oil seep origin estimation in SAR images","author":"Suresh","year":"2013"},{"issue":"July","key":"10.1016\/j.marpolbul.2023.114598_bb0445","first-page":"1678","article-title":"Natural oil seep location estimation in SAR images using direct and contextual information","volume":"13\u201318","author":"Suresh","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Symp."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0450","doi-asserted-by":"crossref","first-page":"4218","DOI":"10.1109\/TGRS.2015.2393375","article-title":"Automatic estimation of oil seep locations in synthetic aperture radar images","volume":"53","author":"Suresh","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0455","first-page":"16","article-title":"Application of the automatic seep location estimator (ASLE) with the use of contextual information for estimating offshore oil seeps","volume":"5","author":"Suresh","year":"2017","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0460","doi-asserted-by":"crossref","first-page":"522","DOI":"10.1016\/j.net.2020.04.008","article-title":"ConvXGB: a new deep learning model for classification problems based on CNN and XGBoost","volume":"53","author":"Thongsuwan","year":"2021","journal-title":"Nucl. Eng. Technol."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0465","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1016\/j.isprsjprs.2007.05.003","article-title":"Detection and discrimination between oil spills and look-alike phenomena through neural networks","volume":"62","author":"Topouzelis","year":"2007","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0470","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3390\/rs12213647","article-title":"Oil spill detection and mapping: a 50-year bibliometric analysis","volume":"12","author":"Vasconcelos","year":"2020","journal-title":"Remote Sens."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0475","series-title":"Dragon 4 Symposium, Kopenhagen, D\u00e4nemark, 23-26 June","article-title":"Detection and tracking of offshore platform\u2019s oily slicks in TerraSAR-X imagery","author":"Velotto","year":"2017"},{"key":"10.1016\/j.marpolbul.2023.114598_bb0480","article-title":"BO-DRNet: an improved deep learning model for oil spill detection by polarimetric Features from SAR images","volume":"14","author":"Wang","year":"2022","journal-title":"Remote Sens."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0485","article-title":"Detection of oil spill using SAR imagery based on AlexNet model","volume":"2021","author":"Wang","year":"2021","journal-title":"Comput. Intell. Neurosci."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0490","first-page":"12","article-title":"Coral reef mapping using remote sensing techniques and a supervised classification algorithm","volume":"2","author":"White","year":"2021","journal-title":"Adv. Environ. Res."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0495","doi-asserted-by":"crossref","first-page":"3197","DOI":"10.1038\/s41598-021-81407-0","article-title":"Surface slicks are pelagic nurseries for diverse ocean fauna","volume":"11","author":"Whitney","year":"2021","journal-title":"Sci. Rep."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0500","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1006\/ecss.1999.0528","article-title":"Sticky waters in the great barrier reef","volume":"50","author":"Wolanski","year":"2000","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0505","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1007\/s13131-017-1086-z","article-title":"A novel oil spill detection method from synthetic aperture radar imageries via a bidimensional empirical mode decomposition","volume":"36","author":"Yang","year":"2017","journal-title":"Acta Oceanol. Sin."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0510","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3390\/jmse9030279","article-title":"Revisit of a case study of spilled oil slicks caused by the Sanchi accident (2018) in the East China Sea","volume":"9","author":"Yang","year":"2021","journal-title":"J. Mar. Sci. Eng."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0515","article-title":"Advances in remote sensing technology, machine learning and deep learning for marine oil spill detection, prediction and vulnerability assessment","volume":"12","author":"Yekeen","year":"2020","journal-title":"Remote Sens."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0520","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":"10.1016\/j.marpolbul.2023.114598_bb0525","doi-asserted-by":"crossref","DOI":"10.1016\/j.rse.2020.111716","article-title":"Deep learning in environmental remote sensing: achievements and challenges","volume":"241","author":"Yuan","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0530","doi-asserted-by":"crossref","DOI":"10.3390\/rs12061015","article-title":"A deep convolutional neural network for oil spill detection from spaceborne SAR images","volume":"12","author":"Zeng","year":"2020","journal-title":"Remote Sens."},{"key":"10.1016\/j.marpolbul.2023.114598_bb0535","series-title":"World Seas: An Environmental Evaluation","first-page":"391","article-title":"Marine oil spills\u2014oil pollution, sources and effects","author":"Zhang","year":"2019"},{"key":"10.1016\/j.marpolbul.2023.114598_bb0540","series-title":"Oil Spills Identification in SAR Image Using mRMR and SVM Model, ICISCE 2018 - 5th International Conference on Information Science and Control Engineering, Zhengzhou, Henan; China, 20-22 July","first-page":"355","author":"Zhou","year":"2019"}],"container-title":["Marine Pollution Bulletin"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0025326X23000292?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0025326X23000292?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,9,21]],"date-time":"2025-09-21T00:34:00Z","timestamp":1758414840000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0025326X23000292"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3]]},"references-count":107,"alternative-id":["S0025326X23000292"],"URL":"https:\/\/doi.org\/10.1016\/j.marpolbul.2023.114598","relation":{},"ISSN":["0025-326X"],"issn-type":[{"value":"0025-326X","type":"print"}],"subject":[],"published":{"date-parts":[[2023,3]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Detection of marine oil-like features in Sentinel-1 SAR images by supplementary use of deep learning and empirical methods: Performance assessment for the Great Barrier Reef marine park","name":"articletitle","label":"Article Title"},{"value":"Marine Pollution Bulletin","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.marpolbul.2023.114598","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"Crown Copyright \u00a9 2023 Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"114598"}}