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dc.creatorGalar Idoate, Mikeles_ES
dc.creatorSesma Redín, Rubénes_ES
dc.creatorAyala, C.es_ES
dc.creatorAranda, C.es_ES
dc.date.accessioned2020-05-19T06:57:57Z
dc.date.available2020-05-19T06:57:57Z
dc.date.issued2019
dc.identifier.issn1682-1750
dc.identifier.urihttps://hdl.handle.net/2454/36919
dc.descriptionTrabajo presentado al PIA19+MRSS19 – Photogrammetric Image Analysis & Munich Remote Sensing Symposium, 2019, Muniches_ES
dc.description.abstractObtaining Sentinel-2 imagery of higher spatial resolution than the native bands while ensuring that output imagery preserves the original radiometry has become a key issue since the deployment of Sentinel-2 satellites. Several studies have been carried out on the upsampling of 20m and 60m Sentinel-2 bands to 10 meters resolution taking advantage of 10m bands. However, how to super-resolve 10m bands to higher resolutions is still an open problem. Recently, deep learning-based techniques has become a de facto standard for single-image super-resolution. The problem is that neural network learning for super-resolution requires image pairs at both the original resolution (10m in Sentinel-2) and the target resolution (e.g., 5m or 2.5m). Since there is no way to obtain higher resolution images for Sentinel-2, we propose to consider images from others sensors having the greatest similarity in terms of spectral bands, which will be appropriately pre-processed. These images, together with Sentinel-2 images, will form our training set. We carry out several experiments using state-of-the-art Convolutional Neural Networks for single-image super-resolution showing that this methodology is a first step toward greater spatial resolution of Sentinel-2 images.en
dc.format.extent8 p.
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.publisherInternational Society for Photogrammetry and Remote Sensingen
dc.relation.ispartofInternational Archives of The Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 2019, XLII-2/W16, 95-102en
dc.rights© Authors 2019. Creative Commons Attribution 4.0 International (CC BY 4.0)en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectSuper-resolutionen
dc.subjectDeep learningen
dc.subjectSentinel-2en
dc.subjectImage enhancementen
dc.subjectConvolutional neural networken
dc.subjectOptical imagesen
dc.titleSuper-resolution for Sentinel-2 imagesen
dc.typeinfo:eu-repo/semantics/conferenceObjecten
dc.typeContribución a congreso / Biltzarrerako ekarpenaes
dc.contributor.departmentUniversidad Pública de Navarra / Nafarroako Unibertsitate Publikoa. ISC - Institute of Smart Citieses_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen
dc.rights.accessRightsAcceso abierto / Sarbide irekiaes
dc.identifier.doi10.5194/isprs-archives-XLII-2-W16-95-2019
dc.relation.publisherversionhttps://doi.org/10.5194/isprs-archives-XLII-2-W16-95-2019
dc.type.versioninfo:eu-repo/semantics/publishedVersionen
dc.type.versionVersión publicada / Argitaratu den bertsioaes


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© Authors 2019. Creative Commons Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's license is described as © Authors 2019. Creative Commons Attribution 4.0 International (CC BY 4.0)