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dc.contributor.authorDoulgeris, Anthony Paul
dc.contributor.authorEltoft, Torbjørn
dc.date.accessioned2013-12-12T12:03:05Z
dc.date.available2013-12-12T12:03:05Z
dc.date.issued2013
dc.description.abstractThis work extends upon our simple feature-based multichannel SAR segmentation method to incorporate highly desirable statistical properties into a computationally simple approach. The desirable properties include Markov random field contextual smoothing and goodness-of-fit testing to automatically obtain the significant number of classes. To achieve this we need to find an explicit class model to fit these non-Gaussian, non-symmetric or skewed feature space clusters. We take the skewed scale mixture of Gaussian scheme to model our classes and approximate it by a number of constrained Gaussians, thereby retaining much of the speed and simplicity of the original feature space method. The algorithm will be demonstrated on a real data and compared to an automatic Gaussian model.en
dc.identifier.citationInternational Geoscience and Remote Sensing Symposium (IGARSS2013),en
dc.identifier.cristinIDFRIDAID 1043504
dc.identifier.urihttps://hdl.handle.net/10037/5612
dc.identifier.urnURN:NBN:no-uit_munin_5303
dc.language.isoengen
dc.rights.accessRightsopenAccess
dc.subjectVDP::Mathematics and natural science: 400::Mathematics: 410::Statistics: 412en
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Matematikk: 410::Statistikk: 412en
dc.titleAn Advanced Non-Gaussian Feature Space Method for POL-SAR Image Segmentationen
dc.typeConference objecten
dc.typeKonferansebidragen


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