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dc.contributor.authorBayındır, Cihantr_TR
dc.contributor.authorFrost, J. Daviden_US
dc.contributor.authorBarnes, Christopher F.en_US
dc.date.accessioned2018-06-13T09:28:20Z
dc.date.available2018-06-13T09:28:20Z
dc.date.issued2018-01
dc.identifier.issn0364-9059
dc.identifier.issn1558-1691
dc.identifier.otherWOS:000427834200022
dc.identifier.urihttp://hdl.handle.net/11729/1296
dc.identifier.urihttp://dx.doi.org/10.1109/JOE.2017.2714818
dc.description.abstractOil spills are one of the most dangerous catastrophes that threaten the oceans. Therefore, detecting and monitoring oil spills by means of remote sensing techniques that provide large-scale assessments is of critical importance to predict, prevent, and clean oil contamination. In this study, the detection of an oil spill using synthetic aperture radar (SAR) imagery is considered. Detection of the oil spill is performed using change detection algorithms between imagery acquired at different times. The specific algorithms used are the correlation coefficient change statistic and the intensity ratio change statistic algorithms. These algorithms and the probabilistic selection of threshold criteria are reviewed and discussed. A recently offered change detection method that depends on generating change maps of two images in a temporal sequence is used. An initial change map is obtained by cumulatively adding sequences in such a manner that common change areas are excluded and uncommon change areas are included. A final change map is obtained by comparing the first and the last images in the temporal sequence. This method requires at least three images to be employed and can be generalized to longer temporal image sequences. The purpose of this approach is to provide a double-check mechanism to the conventional approach and, thus, reduce the probability of false alarm while enhancing change detection. The algorithms are tested on 2010 Gulf of Mexico oil spill imagery. It is shown that the intensity ratio change statistic is a better tool for identification of the changes due to the oil spill compared to the correlation coefficient change statistic. It is also shown that the proposed method can reduce the probability of false alarm.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.isversionof10.1109/JOE.2017.2714818
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.sourceIEEE Journal Of Oceanic Engineeringen_US
dc.subjectChange detection (CD)en_US
dc.subjectOil spillen_US
dc.subjectRemote sensing of oceansen_US
dc.subjectSynthetic aperture radar (SAR) imagingen_US
dc.titleAssessment and Enhancement of SAR Noncoherent Change Detection of Sea-Surface Oil Spillsen_US
dc.typearticleen_US
dc.contributor.departmentIşık Üniversitesi, Mühendislik Fakültesi, İnşaat Mühendisliği Bölümütr_TR
dc.contributor.departmentIsik University, Faculty of Engineering, Department of Civil Engineeringen_US
dc.contributor.authorID32617
dc.identifier.volume43
dc.identifier.issue1
dc.identifier.startpage211
dc.identifier.endpage220


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