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dc.contributor.authorDu, Sen
dc.contributor.authorMallorquí Franquet, Jordi Joan
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Teoria del Senyal i Comunicacions
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.date.accessioned2022-04-01T11:10:31Z
dc.date.available2022-04-01T11:10:31Z
dc.date.issued2021
dc.identifier.citationDu, S.; Mallorqui, J.J. An improvement of offset tracking for cross hair (CH) and patch like (PL) elimination and reliability estimation for large deformation monitoring with SAR data. A: IEEE International Geoscience and Remote Sensing Symposium. "IGARSS 2021, 2021 IEEE International Geoscience and Remote Sensing Symposium: 12-16 July, 2021, virtual symposium, Brussels, Belgium: proceedings". Institute of Electrical and Electronics Engineers (IEEE), p. 4516-4519. ISBN 978-1-6654-0369-6. DOI 10.1109/IGARSS47720.2021.9553298.
dc.identifier.isbn978-1-6654-0369-6
dc.identifier.urihttp://hdl.handle.net/2117/365186
dc.description.abstractSAR based offset tracking (OT) is an efficient tool for ground deformation observation, and signal to noise ratio (SNR) is its common error indicator. However, ground feature variations often weaken the accuracy of OT. In addition, SNR shows the signal reliability instead of result accuracy. Based on amplitude selection, cubic spline interpolation and double offset detecting, an improved OT method has been proposed in this paper. Subsidence caused by mining and GPS data have been employed to evaluate the performance of this scheme with TerraSAR-X data. The results indicate that patch like (PL) and cross hair(CH) are reduced efficiently, and the error estimated by the proposed method has a higher correlation with real error than SNR in the mountainous area.
dc.description.sponsorshipThis research work has been supported by the China Scholarship Council (CSC NO. 201806420035), by the Spanish Ministry of Economy, Industry and Competitiveness (MINECO), the State Research Agency (AEI) and the European Funds for Regional Development (EFRD) under project TEC2017- 85244-C2-1-P. CommSensLab, which is Unidad de Excelencia Maria de Maeztu MDM-2016-0600 financed by the Agencia Estatal de Investigacion, ´ Spain. TerraSAR-X images have been provided by the German Aerospace Center (DLR) in the framework of Project GEO0389 of the TerraSAR-X scientific program.
dc.format.extent4 p.
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Teledetecció
dc.subject.lcshRemote sensing
dc.subject.lcshSynthetic aperture radar
dc.subject.lcshGlobal Positioning System
dc.subject.otherOffset tracking
dc.subject.otherError estimation
dc.subject.otherCross hair and patch like
dc.subject.otherSAR
dc.subject.otherGround deformation detection
dc.titleAn improvement of offset tracking for cross hair (CH) and patch like (PL) elimination and reliability estimation for large deformation monitoring with SAR data
dc.typeConference report
dc.subject.lemacTeledetecció
dc.subject.lemacRadar
dc.subject.lemacSistema de posicionament global
dc.contributor.groupUniversitat Politècnica de Catalunya. RSLAB - Grup de Recerca en Teledetecció
dc.identifier.doi10.1109/IGARSS47720.2021.9553298
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/9553298
dc.rights.accessOpen Access
local.identifier.drac32544845
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TEC2017-85244-C2-2-P/ES/SENSORES PARA APLICACIONES MULTI-ESCALA EN TELEDETECCION/
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/1PE/MDM-2016-0600
local.citation.authorDu, S.; Mallorqui, J.J.
local.citation.contributorIEEE International Geoscience and Remote Sensing Symposium
local.citation.publicationNameIGARSS 2021, 2021 IEEE International Geoscience and Remote Sensing Symposium: 12-16 July, 2021, virtual symposium, Brussels, Belgium: proceedings
local.citation.startingPage4516
local.citation.endingPage4519


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