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dc.contributor.authorLohse, Johannes
dc.contributor.authorDoulgeris, Anthony Paul
dc.contributor.authorDierking, Wolfgang
dc.date.accessioned2020-07-01T11:32:24Z
dc.date.available2020-07-01T11:32:24Z
dc.date.issued2020-06-23
dc.description.abstractAutomated classification of sea-ice types in Synthetic Aperture Radar (SAR) imagery is complicated by the class-dependent decrease of backscatter intensity with Incidence Angle (IA). In the log-domain, this decrease is approximately linear over the typical range of space-borne SAR instruments. A global correction does not consider that different surface types show different rates of decrease in backscatter intensity. Here, we introduce a supervised classification algorithm that directly incorporates the surface-type dependent effect of IA. We replace the constant mean vector of a Gaussian probability density function in a Bayesian classifier with a linearly variable mean. During training, the classifier first retrieves the slope and intercept of the linear function describing the mean value and then calculates the covariance matrix as the mean squared deviation relative to this function. The IA dependence is no longer treated as an image property but as a class property. Based on training and validation data selected from overlapping SAR and optical images, we evaluate the proposed method in several case studies and compare to other classification algorithms for which a global IA correction is applied during pre-processing. Our results show that the inclusion of the per-class IA sensitivity can significantly improve the performance of the classifier.en_US
dc.identifier.citationLohse JP, Doulgeris ap, Dierking WFO. Mapping sea-ice types from Sentinel-1 considering the surface-type dependent effect of incidence angle. Annals of Glaciology. 2020en_US
dc.identifier.cristinIDFRIDAID 1816176
dc.identifier.doi10.1017/aog.2020.45
dc.identifier.issn0260-3055
dc.identifier.issn1727-5644
dc.identifier.urihttps://hdl.handle.net/10037/18738
dc.language.isoengen_US
dc.publisherCambridge University Pressen_US
dc.relation.ispartofLohse, J.P. (2021). On Automated Classification of Sea Ice Types in SAR Imagery. (Doctoral thesis). <a href=https://hdl.handle.net/10037/20606>https://hdl.handle.net/10037/20606</a>.
dc.relation.journalAnnals of Glaciology
dc.relation.projectIDNorges forskningsråd: 237906en_US
dc.relation.projectIDinfo:eu-repo/grantAgreement/RCN/SFI/237906/Norway/Centre for Integrated Remote Sensing and Forecasting for Arctic Operations/CIRFA/en_US
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2020 The Author(s)en_US
dc.subjectVDP::Technology: 500en_US
dc.subjectVDP::Teknologi: 500en_US
dc.subjectVDP::Mathematics and natural science: 400::Geosciences: 450en_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Geofag: 450en_US
dc.titleMapping sea-ice types from Sentinel-1 considering the surface-type dependent effect of incidence angleen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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