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dc.contributor.authorLohse, Johannes
dc.contributor.authorDoulgeris, Anthony Paul
dc.contributor.authorDierking, Wolfgang Fritz Otto
dc.date.accessioned2021-02-26T08:53:07Z
dc.date.available2021-02-26T08:53:07Z
dc.date.issued2021-02-04
dc.description.abstractRobust and reliable classification of sea ice types in synthetic aperture radar (SAR) images is needed for various operational and environmental applications. Previous studies have investigated the class-dependent decrease in SAR backscatter intensity with incident angle (IA); others have shown the potential of textural information to improve automated image classification. In this work, we investigate the inclusion of Sentinel-1 (S1) texture features into a Bayesian classifier that accounts for linear per-class variation of its features with IA. We use the S1 extra-wide swath (EW) product in ground-range detected format at medium resolution (GRDM), and we compute seven grey level co-occurrence matrix (GLCM) texture features from the HH and the HV backscatter intensity in the linear and logarithmic domain. While GLCM texture features obtained in the linear domain vary significantly with IA, the features computed from the logarithmic intensity do not depend on IA or reveal only a weak, approximately linear dependency. They can therefore be directly included in the IA-sensitive classifier that assumes a linear variation. The different number of looks in the first sub-swath (EW1) of the product causes a distinct offset in texture at the sub-swath boundary between EW1 and the second sub-swath (EW2). This offset must be considered when using texture in classification; we demonstrate a manual correction for the example of GLCM contrast. Based on the Jeffries–Matusita distance between class histograms, we perform a separability analysis for 57 different GLCM parameter settings. We select a suitable combination of features for the ice classes in our data set and classify several test images using a combination of intensity and texture features. We compare the results to a classifier using only intensity. Particular improvements are achieved for the generalized separation of ice and water, as well as the classification of young ice and multi-year ice.en_US
dc.identifier.citationLohse, Doulgeris, Dierking. Incident Angle Dependence of Sentinel-1 Texture Features for Sea Ice Classification. Remote Sensing. 2021en_US
dc.identifier.cristinIDFRIDAID 1887117
dc.identifier.doi10.3390/rs13040552
dc.identifier.issn2072-4292
dc.identifier.urihttps://hdl.handle.net/10037/20605
dc.language.isoengen_US
dc.publisherMDPIen_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.journalRemote Sensing
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 2021 The Author(s)en_US
dc.subjectVDP::Technology: 500::Environmental engineering: 610en_US
dc.subjectVDP::Teknologi: 500::Miljøteknologi: 610en_US
dc.subjectVDP::Mathematics and natural science: 400::Geosciences: 450::Oceanography: 452en_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Geofag: 450::Oseanografi: 452en_US
dc.titleIncident Angle Dependence of Sentinel-1 Texture Features for Sea Ice Classificationen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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