{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T16:33:37Z","timestamp":1784565217700,"version":"3.55.0"},"reference-count":46,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2019,7,26]],"date-time":"2019-07-26T00:00:00Z","timestamp":1564099200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010661","name":"Horizon 2020","doi-asserted-by":"publisher","award":["740593"],"award-info":[{"award-number":["740593"]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010661","name":"Horizon 2020","doi-asserted-by":"publisher","award":["776019"],"award-info":[{"award-number":["776019"]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Oil spill is considered one of the main threats to marine and coastal environments. Efficient monitoring and early identification of oil slicks are vital for the corresponding authorities to react expediently, confine the environmental pollution and avoid further damage. Synthetic aperture radar (SAR) sensors are commonly used for this objective due to their capability for operating efficiently regardless of the weather and illumination conditions. Black spots probably related to oil spills can be clearly captured by SAR sensors, yet their discrimination from look-alikes poses a challenging objective. A variety of different methods have been proposed to automatically detect and classify these dark spots. Most of them employ custom-made datasets posing results as non-comparable. Moreover, in most cases, a single label is assigned to the entire SAR image resulting in a difficulties when manipulating complex scenarios or extracting further information from the depicted content. To overcome these limitations, semantic segmentation with deep convolutional neural networks (DCNNs) is proposed as an efficient approach. Moreover, a publicly available SAR image dataset is introduced, aiming to consist a benchmark for future oil spill detection methods. The presented dataset is employed to review the performance of well-known DCNN segmentation models in the specific task. DeepLabv3+ presented the best performance, in terms of test set accuracy and related inference time. Furthermore, the complex nature of the specific problem, especially due to the challenging task of discriminating oil spills and look-alikes is discussed and illustrated, utilizing the introduced dataset. Results imply that DCNN segmentation models, trained and evaluated on the provided dataset, can be utilized to implement efficient oil spill detectors. Current work is expected to contribute significantly to the future research activity regarding oil spill identification and SAR image processing.<\/jats:p>","DOI":"10.3390\/rs11151762","type":"journal-article","created":{"date-parts":[[2019,7,26]],"date-time":"2019-07-26T08:45:39Z","timestamp":1564130739000},"page":"1762","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":252,"title":["Oil Spill Identification from Satellite Images Using Deep Neural Networks"],"prefix":"10.3390","volume":"11","author":[{"given":"Marios","family":"Krestenitis","sequence":"first","affiliation":[{"name":"Centre for Research and Technology Hellas, Information Technologies Institute, 6th km Harilaou-Thermi, 57001 Thessaloniki, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Georgios","family":"Orfanidis","sequence":"additional","affiliation":[{"name":"Centre for Research and Technology Hellas, Information Technologies Institute, 6th km Harilaou-Thermi, 57001 Thessaloniki, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Konstantinos","family":"Ioannidis","sequence":"additional","affiliation":[{"name":"Centre for Research and Technology Hellas, Information Technologies Institute, 6th km Harilaou-Thermi, 57001 Thessaloniki, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Konstantinos","family":"Avgerinakis","sequence":"additional","affiliation":[{"name":"Centre for Research and Technology Hellas, Information Technologies Institute, 6th km Harilaou-Thermi, 57001 Thessaloniki, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefanos","family":"Vrochidis","sequence":"additional","affiliation":[{"name":"Centre for Research and Technology Hellas, Information Technologies Institute, 6th km Harilaou-Thermi, 57001 Thessaloniki, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6447-9020","authenticated-orcid":false,"given":"Ioannis","family":"Kompatsiaris","sequence":"additional","affiliation":[{"name":"Centre for Research and Technology Hellas, Information Technologies Institute, 6th km Harilaou-Thermi, 57001 Thessaloniki, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,26]]},"reference":[{"key":"ref_1","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":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"2931","DOI":"10.1109\/JPROC.2012.2196250","article-title":"Remote sensing of ocean oil-spill pollution","volume":"100","author":"Solberg","year":"2012","journal-title":"Proc. IEEE"},{"key":"ref_4","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":"ref_5","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_6","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/S1353-2561(98)00023-1","article-title":"Review of oil spill remote sensing","volume":"4","author":"Fingas","year":"1997","journal-title":"Spill Sci. Technol. Bull."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2141","DOI":"10.1080\/01431160050029468","article-title":"Cover: Detection of oil spills near offshore installations using synthetic aperture radar (SAR)","volume":"21","author":"Espedal","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kapustin, I.A., Shomina, O.V., Ermoshkin, A.V., Bogatov, N.A., Kupaev, A.V., Molkov, A.A., and Ermakov, S.A. (2019). On Capabilities of Tracking Marine Surface Currents Using Artificial Film Slicks. Remote Sens., 11.","DOI":"10.3390\/rs11070840"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1916","DOI":"10.1109\/36.774704","article-title":"Automatic detection of oil spills in ERS SAR images","volume":"37","author":"Solberg","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3561","DOI":"10.1080\/014311600750037589","article-title":"Oil spill detection using marine SAR images","volume":"21","author":"Fiscella","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1080\/014311699213596","article-title":"Satellite SAR oil spill detection using wind history information","volume":"20","author":"Espedal","year":"1999","journal-title":"Int. J. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"6281","DOI":"10.1080\/01431160802175488","article-title":"Automatic detection and tracking of oil spills in SAR imagery with level set segmentation","volume":"29","author":"Karantzalos","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1016\/j.envsoft.2004.11.010","article-title":"Automatic identification of oil spills on satellite images","volume":"21","author":"Keramitsoglou","year":"2006","journal-title":"Environ. Model. Softw."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5235","DOI":"10.1080\/01431160600693575","article-title":"An object-oriented methodology to detect oil spills","volume":"27","author":"Karathanassi","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.isprsjprs.2016.04.006","article-title":"Object-oriented approach to oil spill detection using ENVISAT ASAR images","volume":"118","author":"Konik","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.isprsjprs.2012.01.005","article-title":"Oil spill feature selection and classification using decision tree forest on SAR image data","volume":"68","author":"Topouzelis","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2839","DOI":"10.1109\/TGRS.2006.881078","article-title":"Partially supervised oil-slick detection by SAR imagery using kernel expansion","volume":"44","author":"Mercier","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2282","DOI":"10.1109\/36.868885","article-title":"Neural networks for oil spill detection using ERS-SAR data","volume":"38","author":"Petrocchi","year":"2000","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"de Souza, D.L., Neto, A.D., and da Mata, W. (2006, January 3\u20136). Intelligent system for feature extraction of oil slick in sar images: Speckle filter analysis. Proceedings of the International Conference on Neural Information Processing, Hong Kong, China.","DOI":"10.1007\/11893257_81"},{"key":"ref_20","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":"ref_21","doi-asserted-by":"crossref","first-page":"2355","DOI":"10.1109\/JSTARS.2013.2251864","article-title":"Satellite oil spill detection using artificial neural networks","volume":"6","author":"Singha","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Song, D., Ding, Y., Li, X., Zhang, B., and Xu, M. (2017). Ocean oil spill classification with RADARSAT-2 SAR based on an optimized wavelet neural network. Remote Sens., 9.","DOI":"10.3390\/rs9080799"},{"key":"ref_23","first-page":"636513","article-title":"Large-scale feature selection using evolved neural networks","volume":"Volume 6365","author":"Stathakis","year":"2006","journal-title":"Image and Signal Processing for Remote Sensing XII, Proceedings of the International Society for Optics and Photonics, Stockholm, Sweden, 2006"},{"key":"ref_24","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 (TCNNA)","volume":"35","author":"Zimmer","year":"2009","journal-title":"Can. J. Remote Sens."},{"key":"ref_25","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."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Gallego, A.J., Gil, P., Pertusa, A., and Fisher, R.B. (2019). Semantic Segmentation of SLAR Imagery with Convolutional LSTM Selectional AutoEncoders. Remote Sens., 11.","DOI":"10.3390\/rs11121402"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Orfanidis, G., Ioannidis, K., Avgerinakis, K., Vrochidis, S., and Kompatsiaris, I. (2018, January 7\u201310). A deep neural network for oil spill semantic segmentation in SAR images. Proceedings of the 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece.","DOI":"10.1109\/ICIP.2018.8451113"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Krestenitis, M., Orfanidis, G., Ioannidis, K., Avgerinakis, K., Vrochidis, S., and Kompatsiaris, I. (2019, January 8\u201311). Early Identification of Oil Spills in Satellite Images Using Deep CNNs. Proceedings of the International Conference on Multimedia Modeling, Thessaloniki, Greece.","DOI":"10.3390\/rs11151762"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_30","unstructured":"Iglovikov, V., and Shvets, A. (2018). Ternausnet: U-net with vgg11 encoder pre-trained on imagenet for image segmentation. arXiv."},{"key":"ref_31","unstructured":"Iglovikov, V., Mushinskiy, S., and Osin, V. (2017). Satellite imagery feature detection using deep convolutional neural network: A kaggle competition. arXiv."},{"key":"ref_32","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_33","doi-asserted-by":"crossref","unstructured":"Chaurasia, A., and Culurciello, E. (2017, January 10\u201313). Linknet: Exploiting encoder representations for efficient semantic segmentation. Proceedings of the 2017 IEEE Visual Communications and Image Processing (VCIP), St. Petersburg, FL, USA.","DOI":"10.1109\/VCIP.2017.8305148"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_35","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_36","unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A.L. (2014). Semantic image segmentation with deep convolutional nets and fully connected crfs. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","volume":"40","author":"Chen","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_38","unstructured":"Kr\u00e4henb\u00fchl, P., and Koltun, V. (2011). Efficient inference in fully connected crfs with gaussian edge potentials. Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_40","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., and Wei, Y. (2017, January 22\u201329). Deformable convolutional networks. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.89"},{"key":"ref_43","unstructured":"Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., and Zisserman, A. (2019, March 28). The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results. Available online: http:\/\/www.pascal-network.org\/challenges\/VOC\/voc2012\/workshop\/index.html."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., and Schiele, B. (2016, January 27\u201330). The cityscapes dataset for semantic urban scene understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.350"},{"key":"ref_45","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18\u201323). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/15\/1762\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:10:01Z","timestamp":1760188201000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/15\/1762"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,26]]},"references-count":46,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2019,8]]}},"alternative-id":["rs11151762"],"URL":"https:\/\/doi.org\/10.3390\/rs11151762","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,7,26]]}}}