Mostra el registre d'ítem simple
Super-resolution of Sensinel-2 imagery using generative adversarial networks
dc.contributor.author | Salgueiro Romero, Luis Fernando |
dc.contributor.author | Marcello, Javier |
dc.contributor.author | Vilaplana Besler, Verónica |
dc.contributor.other | Universitat Politècnica de Catalunya. Doctorat en Teoria del Senyal i Comunicacions |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions |
dc.date.accessioned | 2020-10-07T17:02:27Z |
dc.date.available | 2020-10-07T17:02:27Z |
dc.date.issued | 2020-07-28 |
dc.identifier.citation | Salgueiro, 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.issn | 2072-4292 |
dc.identifier.uri | http://hdl.handle.net/2117/329988 |
dc.description.abstract | Sentinel-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.sponsorship | This 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.iso | eng |
dc.publisher | Multidisciplinary Digital Publishing Institute (MDPI) |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Spain |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject | Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Teledetecció |
dc.subject.lcsh | Artificial satellites in remote sensing |
dc.subject.other | Super-resolution |
dc.subject.other | Generative adversarial network |
dc.subject.other | Deep learning |
dc.subject.other | Sentinel-2 |
dc.subject.other | WorldView |
dc.title | Super-resolution of Sensinel-2 imagery using generative adversarial networks |
dc.type | Article |
dc.subject.lemac | Satèl·lits artificials en teledetecció |
dc.contributor.group | Universitat Politècnica de Catalunya. GPI - Grup de Processament d'Imatge i Vídeo |
dc.identifier.doi | 10.3390/rs12152424 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://www.mdpi.com/2072-4292/12/15/2424 |
dc.rights.access | Open Access |
local.identifier.drac | 29232531 |
dc.description.version | Postprint (published version) |
dc.relation.projectid | info:eu-repo/grantAgreement/MINECO/1PE/TEC2016-75976-R |
local.citation.author | Salgueiro, L.; Marcello, J.; Vilaplana, V. |
local.citation.publicationName | Remote sensing |
local.citation.volume | 12 |
local.citation.number | 15 |
local.citation.startingPage | 2424:1 |
local.citation.endingPage | 2424:25 |
Fitxers d'aquest items
Aquest ítem apareix a les col·leccions següents
-
Articles de revista [2.526]
-
Articles de revista [211]
-
Articles de revista [118]