Sincohmap: land-cover and vegetation mapping using multi-temporal sentinel-1 interferometric coherence

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dc.contributor	Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.contributor	Universitat Politècnica de Catalunya. RSLAB - Grup de Recerca en Teledetecció
dc.contributor.author	Vicente Guijalba, F.
dc.contributor.author	Jacob, Alexander
dc.contributor.author	López-Sánchez Sánchez, Juan Manuel
dc.contributor.author	López Martínez, Carlos
dc.contributor.author	Duro Calvo, Javier
dc.contributor.author	Pottier, Eric
dc.contributor.author	Mallorquí Franquet, Jordi Joan
dc.date	2018
dc.identifier.citation	Vicente, F. [et al.]. Sincohmap: land-cover and vegetation mapping using multi-temporal sentinel-1 interferometric coherence. A: IEEE International Geoscience and Remote Sensing Symposium. "2018 IEEE International Geoscience & Remote Sensing Symposium: proceedings: July 22–27, 2018 Valencia, Spain". Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 6631-6634.
dc.identifier.citation	978-1-5386-7150
dc.identifier.citation	10.1109/IGARSS.2018.8517926
dc.identifier.uri	http://hdl.handle.net/2117/129328
dc.language.iso	eng
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation	https://ieeexplore.ieee.org/document/8517926
dc.relation	info:eu-repo/grantAgreement/AEI/2PE/TEC2017-85244-C2-2-P
dc.relation	info:eu-repo/grantAgreement/ES/1PE/MDM-2016-0600
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Teledetecció
dc.subject	Remote sensing
dc.subject	SInCohMap
dc.subject	SAR
dc.subject	InSAR
dc.subject	Coherence
dc.subject	Sentinel-1
dc.subject	Land-cover
dc.subject	Mapping
dc.subject	Vegetation
dc.subject	Classification
dc.subject	Teledetecció
dc.title	Sincohmap: land-cover and vegetation mapping using multi-temporal sentinel-1 interferometric coherence
dc.type	info:eu-repo/semantics/publishedVersion
dc.type	info:eu-repo/semantics/conferenceObject
dc.description.abstract	InSAR coherence is a promising parameter for land-cover classification and mapping. The ESA SEOM SInCohMap project is devised to test and analyze multi-temporal InSAR coherence potentialities exploiting dense multitemporal data from the Sentinel-1 constellation. In the framework of the project, this paper shows the first classification results using machine learning algorithms over a two-year period of InSAR coherence data. The evaluation is performed on the test site of Doñana (Seville, Southwestern Spain), mainly an agricultural area where different land covers can be identified. Classification results exploiting InSAR coherence shows accuracies around 80 % for this site.
dc.description.abstract	Peer Reviewed

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