{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,12]],"date-time":"2026-07-12T10:23:02Z","timestamp":1783851782263,"version":"3.55.0"},"reference-count":36,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2020,3,1]],"date-time":"2020-03-01T00:00:00Z","timestamp":1583020800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2020,3,1]],"date-time":"2020-03-01T00:00:00Z","timestamp":1583020800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"}],"funder":[{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Environmental Pollution"],"published-print":{"date-parts":[[2020,3]]},"DOI":"10.1016\/j.envpol.2019.113688","type":"journal-article","created":{"date-parts":[[2019,12,2]],"date-time":"2019-12-02T17:30:32Z","timestamp":1575307832000},"page":"113688","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":17,"special_numbering":"C","title":["Single spectral imagery and faster R-CNN to identify hazardous and noxious substances spills"],"prefix":"10.1016","volume":"258","author":[{"given":"Hui","family":"Huang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuchang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zehao","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dejun","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caicai","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuyue","family":"Zhan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haofei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ren","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.envpol.2019.113688_bib1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.knosys.2015.02.029","article-title":"A\u00a0local fuzzy thresholding methodology for multiregion image segmentation","volume":"83","author":"Aja-Fern\u00e1ndez","year":"2015","journal-title":"Knowl. Based Syst."},{"key":"10.1016\/j.envpol.2019.113688_bib2","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":"10.1016\/j.envpol.2019.113688_bib3","series-title":"Proceedings of the European Conference on Computer Vision (ECCV)","first-page":"801","article-title":"Encoder-decoder with atrous separable convolution for semantic image segmentation","author":"Chen","year":"2018"},{"key":"10.1016\/j.envpol.2019.113688_bib4","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"1251","article-title":"Xception: deep learning with depthwise separable convolutions","author":"Chollet","year":"2017"},{"key":"10.1016\/j.envpol.2019.113688_bib5","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1016\/j.jhazmat.2014.11.005","article-title":"Review on hazardous and noxious substances (HNS) involved in marine spill incidents-An online database","volume":"285","author":"Cunha","year":"2015","journal-title":"J.\u00a0Hazard Mater."},{"key":"10.1016\/j.envpol.2019.113688_bib6","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"3992","article-title":"Convolutional feature masking for joint object and stuff segmentation","author":"Dai","year":"2015"},{"issue":"3","key":"10.1016\/j.envpol.2019.113688_bib7","first-page":"364","article-title":"Convolutional neural network with data augmentation for SAR target recognition","volume":"13","author":"Ding","year":"2016","journal-title":"IEEE. Geosci. Rem. Sens. Lett."},{"key":"10.1016\/j.envpol.2019.113688_bib8","series-title":"Regulation on the Safety Management of Hazardous Chemicals(2011 Revision)","author":"General Office of the State Council, P","year":"2011"},{"issue":"2","key":"10.1016\/j.envpol.2019.113688_bib9","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1002\/(SICI)1097-0274(199808)34:2<144::AID-AJIM7>3.0.CO;2-X","article-title":"Associations between several sites of cancer and occupational exposure to benzene, toluene, xylene, and styrene: results of a case-control study in Montreal","volume":"34","author":"G\u00e9rin","year":"1998","journal-title":"Am. J. Ind. Med."},{"key":"10.1016\/j.envpol.2019.113688_bib10","series-title":"Proceedings of the IEEE International Conference on Computer Vision","first-page":"1440","article-title":"Fast r-cnn","author":"Girshick","year":"2015"},{"key":"10.1016\/j.envpol.2019.113688_bib11","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"580","article-title":"Rich feature hierarchies for accurate object detection and semantic segmentation","author":"Girshick","year":"2014"},{"issue":"6","key":"10.1016\/j.envpol.2019.113688_bib12","doi-asserted-by":"crossref","first-page":"646","DOI":"10.1049\/ip-f-1.1984.0099","article-title":"Maximum entropy method in image processing","volume":"131","author":"Gull","year":"1984","journal-title":"IEE Proc. F (Commun. Radar Signal Process.)"},{"key":"10.1016\/j.envpol.2019.113688_bib13","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.envint.2014.05.012","article-title":"Development of a risk-based prioritisation methodology to inform public health emergency planning and preparedness in case of accidental spill at sea of hazardous and noxious substances (HNS)","volume":"72","author":"Harold","year":"2014","journal-title":"Environ. Int."},{"key":"10.1016\/j.envpol.2019.113688_bib14","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"issue":"14","key":"10.1016\/j.envpol.2019.113688_bib15","doi-asserted-by":"crossref","first-page":"3046","DOI":"10.1021\/es000888r","article-title":"Micromachined electrophoresis chips with electrochemical detectors for analysis of explosive compounds in soil and groundwater","volume":"34","author":"Hilmi","year":"2000","journal-title":"Environ. Sci. Technol."},{"issue":"16","key":"10.1016\/j.envpol.2019.113688_bib16","doi-asserted-by":"crossref","first-page":"4458","DOI":"10.1364\/AO.58.004458","article-title":"On-site visualized classification of transparent hazards and noxious substances on a water surface by multispectral techniques","volume":"58","author":"Huang","year":"2019","journal-title":"Appl. Optic."},{"key":"10.1016\/j.envpol.2019.113688_bib17","series-title":"Protocol on Preparedness, Response and Co-operation to Pollution Incidents by Hazardous and Noxious Substances (OPRC-HNS Protocol)","author":"IMO","year":"2000"},{"issue":"10","key":"10.1016\/j.envpol.2019.113688_bib18","doi-asserted-by":"crossref","first-page":"1552","DOI":"10.1021\/es9805627","article-title":"Determination of benzo [a] pyrene diones in air particulate matter with liquid chromatography mass spectrometry","volume":"33","author":"Koeber","year":"1999","journal-title":"Environ. Sci. Technol."},{"key":"10.1016\/j.envpol.2019.113688_bib19","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","author":"Krizhevsky","year":"2012","journal-title":"Neural Inf. Process. Syst."},{"issue":"12","key":"10.1016\/j.envpol.2019.113688_bib20","doi-asserted-by":"crossref","first-page":"2570","DOI":"10.1021\/es990899o","article-title":"Use of UV spectroscopy to characterize the reaction between NOM and free chlorine","volume":"34","author":"Li","year":"2000","journal-title":"Environ. Sci. Technol."},{"key":"10.1016\/j.envpol.2019.113688_bib21","series-title":"2017 IEEE International Conference on Image Processing","first-page":"900","article-title":"Rotated region based CNN for ship detection","author":"Liu","year":"2017"},{"key":"10.1016\/j.envpol.2019.113688_bib22","series-title":"Handbook of Physical-Chemical Properties and Environmental Fate for Organic Chemicals","author":"Mackay","year":"2006"},{"key":"10.1016\/j.envpol.2019.113688_bib23","series-title":"2018 International Interdisciplinary PhD Workshop (IIPhDW), Swinouj\u015bcie","first-page":"117","article-title":"Data augmentation for improving deep learning in image classification problem","author":"Miko\u0142ajczyk","year":"2018"},{"issue":"9","key":"10.1016\/j.envpol.2019.113688_bib24","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."},{"issue":"1","key":"10.1016\/j.envpol.2019.113688_bib25","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A\u00a0threshold selection method from gray-level histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst, Man, Cybern."},{"key":"10.1016\/j.envpol.2019.113688_bib26","series-title":"The Effectiveness of Data Augmentation in Image Classification Using Deep Learning","author":"Perez","year":"2017"},{"key":"10.1016\/j.envpol.2019.113688_bib27","series-title":"Proceedings of the A White Paper for the Interspill Conference & the 4th IMO R&D Forum","first-page":"12","article-title":"Are HNS spills more dangerous than oil spills","author":"Purnell","year":"2009"},{"issue":"9","key":"10.1016\/j.envpol.2019.113688_bib28","doi-asserted-by":"crossref","first-page":"2352","DOI":"10.1162\/neco_a_00990","article-title":"Deep convolutional neural networks for image classification: a comprehensive review","volume":"29","author":"Rawat","year":"2017","journal-title":"Neural Comput."},{"key":"10.1016\/j.envpol.2019.113688_bib29","first-page":"91","article-title":"Faster r-cnn: towards real-time object detection with region proposal networks","author":"Ren","year":"2015","journal-title":"Neural Inf. Process. Syst."},{"key":"10.1016\/j.envpol.2019.113688_bib30","series-title":"IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","first-page":"4035","article-title":"Multi-frequency and multi-polarization analysis of oil slicks using TerraSAR-X and RADARSAT-2 data, 2016","author":"Singha","year":"2016"},{"issue":"1","key":"10.1016\/j.envpol.2019.113688_bib31","first-page":"1","article-title":"Development of band ratioing algorithms and neural networks to detection of oil spills using Landsat ETM+ data","volume":"2012","author":"Taravat","year":"2012","journal-title":"EURASIP. J. Adv. Sig. Process."},{"issue":"4","key":"10.1016\/j.envpol.2019.113688_bib32","article-title":"Ship detection in optical remote sensing images based on deep convolutional neural networks","volume":"11","author":"Yao","year":"2017","journal-title":"J.\u00a0Appl. Remote Sens."},{"issue":"5","key":"10.1016\/j.envpol.2019.113688_bib33","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1080\/15481603.2017.1323377","article-title":"Deep learning in remote sensing scene classification: a data augmentation enhanced convolutional neural network framework","volume":"54","author":"Yu","year":"2017","journal-title":"GISci. Remote Sens."},{"issue":"9","key":"10.1016\/j.envpol.2019.113688_bib34","doi-asserted-by":"crossref","first-page":"4973","DOI":"10.1109\/TGRS.2018.2803038","article-title":"Oil spill segmentation via adversarial f-divergence learning","volume":"56","author":"Yu","year":"2018","journal-title":"IEEE. Trans. Geosci. Remote. Sens."},{"issue":"9","key":"10.1016\/j.envpol.2019.113688_bib35","doi-asserted-by":"crossref","first-page":"1142","DOI":"10.3390\/rs11091142","article-title":"Floating xylene spill segmentation from ultraviolet images via target enhancement","volume":"11","author":"Zhan","year":"2019","journal-title":"Remote Sens."},{"issue":"11","key":"10.1016\/j.envpol.2019.113688_bib36","doi-asserted-by":"crossref","first-page":"13755","DOI":"10.1364\/OE.22.013755","article-title":"Exploring the potential of optical remote sensing for oil spill detection in shallow coastal waters-a case study in the Arabian Gulf","volume":"22","author":"Zhao","year":"2014","journal-title":"Opt. Express"}],"container-title":["Environmental Pollution"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0269749119340990?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0269749119340990?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T21:22:42Z","timestamp":1759440162000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0269749119340990"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3]]},"references-count":36,"alternative-id":["S0269749119340990"],"URL":"https:\/\/doi.org\/10.1016\/j.envpol.2019.113688","relation":{},"ISSN":["0269-7491"],"issn-type":[{"value":"0269-7491","type":"print"}],"subject":[],"published":{"date-parts":[[2020,3]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Single spectral imagery and faster R-CNN to identify hazardous and noxious substances spills","name":"articletitle","label":"Article Title"},{"value":"Environmental Pollution","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.envpol.2019.113688","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2019 Elsevier Ltd. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"113688"}}