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dc.contributor.advisorDoulgeris, Anthony
dc.contributor.authorQuigley, Cornelius
dc.date.accessioned2018-01-02T15:42:28Z
dc.date.available2018-01-02T15:42:28Z
dc.date.issued2017-05-31
dc.description.abstractAccording to the scientific consensus, the Arctic is currently in a state of unprecedented change. In recent years, climate change has been identified as the main cause of Arctic sea ice decline. For this reason, the need to have access to timely and cost effective data is of great importance. Remote sensing via Earth orbiting satellites offers extensive data coverage in areas that are typically inaccessible due to their remote location and extreme weather conditions. As well as this, having knowledge of sea ice conditions aids in exploration and navigation. This thesis is concerned with classifying thin sea ice in Storfjorden using data acquired from both MODIS and Radarsat-2 in order to determine if data from either sources can be considered complimentary to each other. For this purpose, four comparisons were made. These included comparing MODISs 36 band data set with data from Radarsat-2 ScanSAR Narrow and Wide modes. As well as this, a comparison between MODISs 36 band data and data from Radarsat-2 QuadPol mode was made. HEM thickness measurements are also available from a helicopter campaign around the same time the data was taken. From laser altimeter data that accompanied the thickness measurements, a roughness characteristic was derived that was compared against the HEM measured thicknesses. All MODIS data were screened for corrupted bands. The resulting bands were transformed into a new space via Principal Component Analysis (PCA). The first few components that contained most of the variance of the transformed data set were kept for segmentation. The SAR data was multilooked and feature extracted. The features that were chosen are a set of six basic features that have shown reasonably good results in the segmentation of sea ice previously and are known as the Extended Polarimetric Feature Space (EPFS). This set of features is composed of five polarimetric features plus a feature for non-Gaussianity. All features were segmented using a Mixture of Gaussian algorithm with Markov Random Field based contextual smoothing. The segmented results were compared visually by using all a priori knowledge about the fjord sourced from weather charts and scientific papers. The best results were found for the comparison between MODIS data and Radarsat-2 ScanSAR Wide data. This comparison shows that for a low number of clusters, the segmentation algorithm finds the same surface classes in the MODIS data as it does for the Radarsat-2 data. However, for progressively higher number of clusters of the MODIS data, MODIS reveals information related to the largescale ice types present in the fjord that the SAR is insensitive to. A literature review of segmentation methods is also presented. This review was conducted by designing a set of search terms related to the segmentation of sea ice data in the Arctic. The results suggest that segmentation of optical data for Arctic sea ice is a relatively under studied area compared to SAR.en_US
dc.identifier.urihttps://hdl.handle.net/10037/11895
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 2017 The Author(s)
dc.subject.courseIDFYS-3900
dc.subjectVDP::Mathematics and natural science: 400::Mathematics: 410en_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Matematikk: 410en_US
dc.subjectVDP::Mathematics and natural science: 400::Physics: 430::Electromagnetism, acoustics, optics: 434en_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Fysikk: 430::Elektromagnetisme, akustikk, optikk: 434en_US
dc.subjectSynthetic Aperture Radaren_US
dc.subjectSea iceen_US
dc.subjectEarth observationen_US
dc.subjectclassificationen_US
dc.titleA comparison between optical and SAR classification results for thin sea ice in Storfjordenen_US
dc.typeMaster thesisen_US
dc.typeMastergradsoppgaveen_US


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