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dc.contributor.authorSalgueiro Romero, Luis Fernando
dc.contributor.authorMarcello, Javier
dc.contributor.authorVilaplana Besler, Verónica
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.accessioned2020-10-07T17:02:27Z
dc.date.available2020-10-07T17:02:27Z
dc.date.issued2020-07-28
dc.identifier.citationSalgueiro, L.; Marcello, J.; Vilaplana, V. Super-resolution of Sensinel-2 imagery using generative adversarial networks. "Remote sensing", 28 Juliol 2020, vol. 12, núm. 15, p. 2424:1-2424:25.
dc.identifier.issn2072-4292
dc.identifier.urihttp://hdl.handle.net/2117/329988
dc.description.abstractSentinel-2 satellites provide multi-spectral optical remote sensing images with four bands at 10 m of spatial resolution. These images, due to the open data distribution policy, are becoming an important resource for several applications. However, for small scale studies, the spatial detail of these images might not be sufficient. On the other hand, WorldView commercial satellites offer multi-spectral images with a very high spatial resolution, typically less than 2 m, but their use can be impractical for large areas or multi-temporal analysis due to their high cost. To exploit the free availability of Sentinel imagery, it is worth considering deep learning techniques for single-image super-resolution tasks, allowing the spatial enhancement of low-resolution (LR) images by recovering high-frequency details to produce high-resolution (HR) super-resolved images. In this work, we implement and train a model based on the Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) with pairs of WorldView-Sentinel images to generate a super-resolved multispectral Sentinel-2 output with a scaling factor of 5. Our model, named RS-ESRGAN, removes the upsampling layers of the network to make it feasible to train with co-registered remote sensing images. Results obtained outperform state-of-the-art models using standard metrics like PSNR, SSIM, ERGAS, SAM and CC. Moreover, qualitative visual analysis shows spatial improvements as well as the preservation of the spectral information, allowing the super-resolved Sentinel-2 imagery to be used in studies requiring very high spatial resolution
dc.description.sponsorshipThis research has been supported by the ARTEMISAT-2 (CTM2016-77733-R) and MALEGRA (TEC2016-75976-R) projects, funded by the Spanish Agencia Estatal de Investigación (AEI), by the Fondo Europeo de Desarrollo Regional (FEDER) and the Spanish Ministerio de Economía y Competitividad, respectively. L.S. would like to acknowledge the BECAL (Becas Carlos Antonio López) scholarship for the financial support.
dc.language.isoeng
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Teledetecció
dc.subject.lcshArtificial satellites in remote sensing
dc.subject.otherSuper-resolution
dc.subject.otherGenerative adversarial network
dc.subject.otherDeep learning
dc.subject.otherSentinel-2
dc.subject.otherWorldView
dc.titleSuper-resolution of Sensinel-2 imagery using generative adversarial networks
dc.typeArticle
dc.subject.lemacSatèl·lits artificials en teledetecció
dc.contributor.groupUniversitat Politècnica de Catalunya. GPI - Grup de Processament d'Imatge i Vídeo
dc.identifier.doi10.3390/rs12152424
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://www.mdpi.com/2072-4292/12/15/2424
dc.rights.accessOpen Access
local.identifier.drac29232531
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/1PE/TEC2016-75976-R
local.citation.authorSalgueiro, L.; Marcello, J.; Vilaplana, V.
local.citation.publicationNameRemote sensing
local.citation.volume12
local.citation.number15
local.citation.startingPage2424:1
local.citation.endingPage2424:25


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