Show simple item record

dc.contributor.authorCristea, Anca
dc.contributor.authorVan Houtte, Jeroen
dc.contributor.authorDoulgeris, Anthony Paul
dc.date.accessioned2021-04-26T06:31:31Z
dc.date.available2021-04-26T06:31:31Z
dc.date.issued2020-05-28
dc.description.abstractSynthetic aperture radar systems perform signal acquisition under varying incidence angles and register an implicit intensity decay from near to far range. Owing to the geometrical interaction between microwaves and the imaged targets, the rates at which intensities decay depend on the nature of the targets, thus rendering single-rate image correction approaches only partially successful. The decay, also known as the incidence angle effect, impacts the segmentation of wide-swath images performed on absolute intensity values. We propose to integrate the target-specific intensity decay rates into a nonstationary statistical model, for use in a fully automatic and unsupervised segmentation algorithm. We demonstrate this concept by assuming Gaussian distributed log-intensities and linear decay rates, a fitting approximation for the smooth systematic decay observed for extended flat targets. The segmentation is performed on Sentinel-1, Radarsat-2, and UAVSAR wide-swath scenes containing open water, sea ice, and oil slicks. As a result, we obtain segments connected throughout the entire incidence angle range, thus overcoming the limitations of modeling that does not account for different per-target decays. The model simplicity also allows for short execution times and presents the segmentation approach as a potential operational algorithm. In addition, we estimate the log-linear decay rates and examine their potential for a physical interpretation of the segments.en_US
dc.identifier.citationCristea A, Van Houtte, Doulgeris ap. Integrating Incidence Angle Dependencies Into the Clustering-Based Segmentation of SAR Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020;13(1)en_US
dc.identifier.cristinIDFRIDAID 1816179
dc.identifier.doi10.1109/JSTARS.2020.2993067
dc.identifier.issn1939-1404
dc.identifier.issn2151-1535
dc.identifier.urihttps://hdl.handle.net/10037/21036
dc.language.isoengen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.journalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
dc.relation.projectIDNorges forskningsråd: 237906en_US
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 2020 The Author(s)en_US
dc.subjectVDP::Technology: 500en_US
dc.subjectVDP::Teknologi: 500en_US
dc.titleIntegrating Incidence Angle Dependencies Into the Clustering-Based Segmentation of SAR Imagesen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


File(s) in this item

Thumbnail

This item appears in the following collection(s)

Show simple item record