Land Cover Classification using Sentinel-1 Radar Mission Interferometry

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.advisorAntropov, Oleg
dc.contributor.authorArsalan-Ul-Haque, Muhammad
dc.contributor.schoolSähkötekniikan korkeakoulufi
dc.contributor.supervisorPraks, Jaan
dc.date.accessioned2017-10-30T07:53:08Z
dc.date.available2017-10-30T07:53:08Z
dc.date.issued2017-10-23
dc.description.abstractSynthetic Aperture Radar (SAR) has been widely used for many years in the field of remote sensing. SAR has valuable contribution due to its ability to provide complementary information to optical systems, penetration of radar waves through volumetric targets and high-resolution. SAR has the ability to operate during day and night. It provides operational services under all weather conditions. SAR imagery has many applications including land cover changes, environmental monitoring, climate change and military surveillance. This work focuses on land cover classification with SAR interferometry (InSAR) technique using Sentinel-1 space radar image pair. Sentinel-1 data were collected over the southern part of Estonia. Two SLC SAR images were acquired from both Sentinel-1A and Sentinel-1B with six days temporal difference. In this study, interferometric coherence and backscattering intensity processing chains have been set up and applied to Sentinel-1 SAR image pair. The Sentinel Application Platform (SNAP) has been used for processing of single pair for Sentinel-1 mission. The SNAP is an European Space Agency (ESA) software. The Sentinel-1 image pair processing has been done using Sentinel-1 Toolbox (S1TBX) which is a part of SNAP. Corine Land Cover (CLC) 2012 database has been used as a reference data with 20 m resolution. The CLC2012 contains land use/cover information for most of the European countries. A single optical image from Sentinel-2A was additionally used for feature extraction. An overall accuracy of 68% to 73% was achieved when performing classification into five classes (Urban, Field, Forest, Peat-land, Water) using supervised classification with k-nearest neighbour (kNN) algorithm. The accuracy assessment was done by using confusion matrices.en
dc.ethesisidAalto 9686
dc.format.extent69+11
dc.format.mimetypeapplication/pdfen
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/28445
dc.identifier.urnURN:NBN:fi:aalto-201710307291
dc.language.isoenen
dc.locationP1fi
dc.programmeNanoRad - Master’s Programme in Nano and Radio Sciences (TS2013)fi
dc.programme.majorSpace Science and Technologyfi
dc.programme.mcodeELEC3039fi
dc.subject.keywordsynthetic aperture radaren
dc.subject.keywordSARen
dc.subject.keywordland cover classificationen
dc.subject.keywordInSARen
dc.subject.keywordinterferometric coherenceen
dc.subject.keywordbackscattering intensityen
dc.titleLand Cover Classification using Sentinel-1 Radar Mission Interferometryen
dc.typeG2 Pro gradu, diplomityöfi
dc.type.ontasotMaster's thesisen
dc.type.ontasotDiplomityöfi
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