{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:31:01Z","timestamp":1760146261724,"version":"build-2065373602"},"reference-count":31,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T00:00:00Z","timestamp":1729468800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union\u2019s Horizon 2020 Research and Innovation Program aqua3S","award":["832876"],"award-info":[{"award-number":["832876"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The detection of complex formations, initially suspected to be oil spills, is investigated using atmospherically corrected multispectral satellite images and deep learning techniques. Several formations have been detected in an inland lake in Northern Greece. Four atmospheric corrections (ACOLITE, iCOR, Polymer, and C2RCC) that are specifically designed for water applications are examined and implemented on Sentinel-2 multispectral satellite images to eliminate the influence of the atmosphere. Out of the four algorithms, iCOR and ACOLITE are able to depict the formations sufficiently; however, the latter is chosen for further processing due to fewer uncertainties in the depiction of these formations as anomalies across the multispectral range. Furthermore, a number of formations are annotated at the pixel level for the 10 m bands (red, green, blue, and NIR), and a deep neural network (DNN) is trained and validated. Our results show that the four-band configuration provides the best model for the detection of these complex formations. Despite not being necessarily related to oil spills, studying these formations is crucial for environmental monitoring, pollution detection, and the advancement of remote sensing techniques.<\/jats:p>","DOI":"10.3390\/rs16203913","type":"journal-article","created":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T10:49:22Z","timestamp":1729507762000},"page":"3913","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Detection of Complex Formations in an Inland Lake from Sentinel-2 Images Using Atmospheric Corrections and a Fully Connected Deep Neural Network"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9327-4187","authenticated-orcid":false,"given":"Damianos F.","family":"Mantsis","sequence":"first","affiliation":[{"name":"Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7615-8400","authenticated-orcid":false,"given":"Anastasia","family":"Moumtzidou","sequence":"additional","affiliation":[{"name":"Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ioannis","family":"Lioumbas","sequence":"additional","affiliation":[{"name":"Thessaloniki Drinking Water & Sewerage Co. S.A. (EYATH S.A.), 54635 Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5234-9795","authenticated-orcid":false,"given":"Ilias","family":"Gialampoukidis","sequence":"additional","affiliation":[{"name":"Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-4919-1417","authenticated-orcid":false,"given":"Aikaterini","family":"Christodoulou","sequence":"additional","affiliation":[{"name":"Thessaloniki Drinking Water & Sewerage Co. S.A. (EYATH S.A.), 54635 Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexandros","family":"Mentes","sequence":"additional","affiliation":[{"name":"Thessaloniki Drinking Water & Sewerage Co. S.A. 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Environ."},{"key":"ref_2","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_3","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_4","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_5","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":"ref_6","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_7","doi-asserted-by":"crossref","unstructured":"Fingas, M.F., and Brown, C.E. (2017). A review of oil spill remote sensing. Sensors, 18.","DOI":"10.3390\/s18010091"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3812","DOI":"10.1109\/TGRS.2012.2185804","article-title":"Polarimetric analysis of backscatter from the Deemwater Horizon oil spills using L-band synthetic aperture radar","volume":"50","author":"Minchew","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4979","DOI":"10.1109\/JSTARS.2016.2559946","article-title":"A combination of traditional and polarimetrric features for oil spill detection using TerraSAR-X","volume":"9","author":"Singha","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_10","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_11","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":"1997","journal-title":"Int. J. Remote Sens."},{"key":"ref_12","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_13","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_14","doi-asserted-by":"crossref","first-page":"4819","DOI":"10.1080\/01431161.2010.485147","article-title":"Identification of ocean oil spills in SAR imagery based on fuzzy logic algorithm","volume":"31","author":"Liu","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Guo, H., Wu, D., and An, J. (2017). Discrimination of oil slicks and lookalikes in polarimetric SAR images using CNN. Sensors, 17.","DOI":"10.3390\/s17081837"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Konstantinidou, E.E., Kolokoussis, P., Topouzelis, K., and Moutzouris-Sidiris, I. (2019, January 18\u201321). An open source approach for oil spill detection using Sentinel-1 SAR images. Proceedings of the 7th International Conference on Remote Sensing and Geoinformation of the Environment, Paphos, Cyprus.","DOI":"10.1117\/12.2539256"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.marpolbul.2013.05.022","article-title":"Automatic Synthetic Aperture Radar based oil spill detection and performance estimation via a semi-automatic operation service benchmark","volume":"73","author":"Singha","year":"2013","journal-title":"Mar. Pollut. Bull."},{"key":"ref_18","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":"J. Photogramm. Remote Sens."},{"key":"ref_19","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. J."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zeng, K., and Wang, Y. (2020). A deep convolutional neural network for oil spill detection from spaceborne SAR images. Remote Sens., 12.","DOI":"10.3390\/rs12061015"},{"key":"ref_21","first-page":"4204713","article-title":"Oil spill detection based on deep convolutional neural network using polarimetric scattering information from Sentinel-1 images","volume":"60","author":"Ma","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","first-page":"5213910","article-title":"Oil spill contextual and boundary-supervised detection network based on marine SAR images","volume":"60","author":"Zhu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Daeseong, K., and Jung, H.-S. (2018). Mapping oil spills from dual-polarized SAR images using an artificial neural network: Application to oil spill in the Kerch Strait in November 2007. Sensors, 18.","DOI":"10.3390\/s18072237"},{"key":"ref_24","first-page":"101561","article-title":"Satellite remote sensing to improve source water quality monitoring: A water utility\u2019s perspective","volume":"10","author":"Lioumbas","year":"2023","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Laneve, G., Bruno, M., Mukherjee, A., Messineo, V., Giuseppetti, R., De Pace, R., and D\u2019Ugo, E. (2017). Remote sensing detection of algal blooms in a lake impacted by petroleum hydrocarbons. Remote Sens., 14.","DOI":"10.3390\/rs14010121"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"102520","DOI":"10.1016\/j.mex.2023.102520","article-title":"Novel oil spill indices for Sentinel-2 imagery: A case study of natural seepage in Qaruh Island, Kuwait","volume":"12","author":"Zakzouk","year":"2024","journal-title":"MethodsX"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"101327","DOI":"10.1016\/j.mex.2021.101327","article-title":"Sentinel-2 image transfromation methods for mapping oil spill-A case study with Wakashio oil spill in the Indian Ocean, off Mauritius","volume":"8","author":"Rajendran","year":"2021","journal-title":"MethodsX"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Kolokoussis, P., and Karathanassi, V. (2018). Oil spill detection and mapping using Sentinel-2 imagery. J. Mar. Sci. Eng., 6.","DOI":"10.3390\/jmse6010004"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1080\/22797254.2018.1457937","article-title":"Atmospheric correction of Landsat-8\/OLI and Sentinel-2\/MSI data using iCOR algorithm: Validation for coastal and inland waters","volume":"51","author":"Sterckx","year":"2018","journal-title":"Eur. J. Remote Sens."},{"key":"ref_30","unstructured":"Brockmann, C., Doerffer, R., Peters, M., Kerstin, S., Embacher, S., and Ruescas, A. (2016, January 9\u201313). Evolution of the C2RCC neural network for Sentinel-2 and 3 for the retrieval of ocean color products in normal and extreme optically complex waters. Proceedings of the Living Planet Symposium, Prague, Czech Republic."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.rse.2019.03.010","article-title":"Adaptation of the dark spectrum fitting atmospheric correction for aquatic applications of the Landsat and Sentinel-2 archives","volume":"225","author":"Vanhellemeont","year":"2019","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/20\/3913\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:17:39Z","timestamp":1760113059000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/20\/3913"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,21]]},"references-count":31,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["rs16203913"],"URL":"https:\/\/doi.org\/10.3390\/rs16203913","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2024,10,21]]}}}