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dc.contributor.advisorDoulgeris, Anthony
dc.contributor.authorKiærbech, Åshild
dc.date.accessioned2019-07-15T11:16:28Z
dc.date.available2019-07-15T11:16:28Z
dc.date.issued2019-06-03
dc.description.abstractUnsupervised clustering methods on remote sensing images have shown good results. However, this type of machine learning needs additional labelling to be an end-to-end classification in the same manner as traditional supervised classification. The automation of the labelling needs further exploration. We want to investigate the robustness of a supervised automatic labelling scheme by comparing a segmentation with additional automatic labelling against a supervised classification method. Using synthetic aperture radar (SAR) satellite images of sea ice from Sentinel-1, an automatic Expectation Maximization method with a Gaussian mixture model is used for the segmentation, taking into consideration the incidence angle variation within a SAR image. The additional labelling is a likelihood majority vote related to the Mahalanobis distance measure. The Bayesian Maximum Likelihood (ML) is used as the fully supervised reference method. The experiments of comparison are done using various amounts of training data and different percentages of mislabelling in the training data set. The classification results are compared both visually and using classification accuracy. As training data size increases, the accuracy of the ML method tends to decay faster than for the segment-then-label approach, particularly when sample sizes per class are less than a hundred. As more contamination is introduced, the decay is not distinct, probably due to the large within-class variations in the training set. Based on the results, the ML method generally gets a higher overall classification accuracy, but there are weak tendencies for the segment-then-label method to be more robust to decreasing training data size and more mislabelling.en_US
dc.identifier.urihttps://hdl.handle.net/10037/15761
dc.language.isoengen_US
dc.publisherUiT Norges arktiske universiteten_US
dc.publisherUiT The Arctic University of Norwayen_US
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2019 The Author(s)
dc.subject.courseIDFYS-3941
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420::Simulering, visualisering, signalbehandling, bildeanalyse: 429en_US
dc.subjectVDP::Mathematics and natural science: 400::Information and communication science: 420::Simulation, visualization, signal processing, image processing: 429en_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Matematikk: 410::Analyse: 411en_US
dc.subjectVDP::Mathematics and natural science: 400::Mathematics: 410::Analysis: 411en_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Matematikk: 410::Statistikk: 412en_US
dc.subjectVDP::Mathematics and natural science: 400::Mathematics: 410::Statistics: 412en_US
dc.subjectAutomatic labellingen_US
dc.subjectClassificationen_US
dc.subjectImage segmentationen_US
dc.subjectExpectation Maximizationen_US
dc.subjectGaussian Mixture Modelen_US
dc.subjectMaximum Likelihooden_US
dc.subjectSea iceen_US
dc.subjectSentinel-1en_US
dc.subjectSARen_US
dc.subjectRemote sensingen_US
dc.subjectSatellite imagesen_US
dc.titleAn investigation of the robustness of distance measure-based supervised labelling of segmented remote sensing imagesen_US
dc.typeMaster thesisen_US
dc.typeMastergradsoppgaveen_US


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