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Oil spill segmentation in SAR images using convolutional neural networks. A comparative analysis with clustering and logistic regression algorithms
dc.contributor.author | Cantorna Berdullas, Diego | |
dc.contributor.author | Dafonte, Carlos | |
dc.contributor.author | Iglesias, Alfonso | |
dc.contributor.author | Arcay, Bernardino | |
dc.date.accessioned | 2024-07-02T12:46:13Z | |
dc.date.available | 2024-07-02T12:46:13Z | |
dc.date.issued | 2019-11 | |
dc.identifier.citation | D. Cantorna, C.. Dafonte, A. Iglesias, and B. Arcay, "Oil spill segmentation in SAR images using convolutional neural networks. A comparative analysis with clustering and logistic regression algorithms", Applied Soft Computing Journal, Vol. 84, Nov. 2019, article number 105716, doi: 10.1016/j.asoc.2019.105716 | es_ES |
dc.identifier.uri | http://hdl.handle.net/2183/37639 | |
dc.description.abstract | [Abstract]: Synthetic aperture radar (SAR) images are a valuable source of information for the detection of marine oil spills. For their effective analysis, it is important to have segmentation algorithms that can delimit possible oil spill areas. This article addresses the application of clustering, logistic regression and convolutional neural network algorithms for the detection of oil spills in Envisat and Sentinel-1 satellite images. Large oil spills do not occur frequently so that the identification of a pixel as oil is relatively uncommon. Metrics based on Precision–Recall curves have been employed because they are useful for problems with an imbalance in the number of samples from the classes. Although logistic regression and clustering algorithms can be considered useful for oil spill segmentation, the combination of convolutional techniques and neural networks achieves the best results with low computing time. A convolutional neural network has been integrated into a decision support system in order to facilitate decision-making and data analysis of possible oil spill events. | es_ES |
dc.description.sponsorship | The authors would like to thank the Spanish Maritime Safety Agency (SASEMAR) for providing us access to a dataset of in situ verified oil spills. The authors are also grateful to the European Space Agency (ESA) and the Copernicus Open Access Hub for allowing us to use SAR images and tools. Part of this work was supported by the Xunta de Galicia, Spain (Potencial Crecemento ED431B 2018/42 and Centro Singular ED431G/01) and the European Union (European Regional Development Fund-ERDF); we also used IT infrastructure that was acquired through the RTI2018-095076-B-C22 and ESP2016-80079-C2-2-R projects, financed by the Spanish Ministry of Science, Innovation and Universities and the Ministry of Economy, Industry and Competitiveness, Spain. | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431B 2018/42 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G/01 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier Ltd | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-095076-B-C22/ES/MINERIA DE DATOS DE GAIA PARA ESTUDIAR LA VIA LACTEA II | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/ESP2016-80079-C2-2-R/ES/MINERIA DE DATOS DE GAIA PARA ESTUDIAR LA VIA LACTEA | es_ES |
dc.relation.uri | https://doi.org/10.1016/j.asoc.2019.105716 | es_ES |
dc.rights | Atribución-NoComercial-SinDerivadas 3.0 España | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
dc.subject | Deep learning | es_ES |
dc.subject | Image segmentation | es_ES |
dc.subject | Oil spill | es_ES |
dc.subject | Remote sensing | es_ES |
dc.subject | SAR | es_ES |
dc.title | Oil spill segmentation in SAR images using convolutional neural networks. A comparative analysis with clustering and logistic regression algorithms | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Applied Soft Computing Journal | es_ES |
UDC.volume | 84 | es_ES |
UDC.startPage | 105716 | es_ES |
dc.identifier.doi | 10.1016/j.asoc.2019.105716 |
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