Identificador persistente para citar o vincular este elemento:
http://hdl.handle.net/10553/113964
Campo DC | Valor | idioma |
---|---|---|
dc.contributor.author | Salgueiro, Luis | en_US |
dc.contributor.author | Marcello Ruiz, Francisco Javier | en_US |
dc.contributor.author | Vilaplana, Verónica | en_US |
dc.date.accessioned | 2022-03-08T08:24:20Z | - |
dc.date.available | 2022-03-08T08:24:20Z | - |
dc.date.issued | 2021 | en_US |
dc.identifier.issn | 2072-4292 | en_US |
dc.identifier.uri | http://hdl.handle.net/10553/113964 | - |
dc.description.abstract | Sentinel-2 satellites have become one of the main resources for Earth observation images because they are free of charge, have a great spatial coverage and high temporal revisit. Sentinel-2 senses the same location providing different spatial resolutions as well as generating a multi-spectral image with 13 bands of 10, 20, and 60 m/pixel. In this work, we propose a single-image super-resolution model based on convolutional neural networks that enhances the low-resolution bands (20 m and 60 m) to reach the maximal resolution sensed (10 m) at the same time, whereas other approaches provide two independent models for each group of LR bands. Our proposed model, named Sen2-RDSR, is made up of Residual in Residual blocks that produce two final outputs at maximal resolution, one for 20 m/pixel bands and the other for 60 m/pixel bands. The training is done in two stages, first focusing on 20 m bands and then on the 60 m bands. Experimental results using six quality metrics (RMSE, SRE, SAM, PSNR, SSIM, ERGAS) show that our model has superior performance compared to other state-of-the-art approaches, and it is very effective and suitable as a preliminary step for land and coastal applications, as studies involving pixel-based classification for Land-Use-Land-Cover or the generation of vegetation indices. | en_US |
dc.language | eng | en_US |
dc.relation | Procesado Avanzado de Datos de Teledetección Para la Monitorización y Gestión Sostenible de Recursos Marinos y Terrestres en Ecosistemas Vulnerables. | en_US |
dc.relation.ispartof | Remote Sensing | en_US |
dc.source | Remote Sensing [ISSN 2072-4292], v. 13(24), 5007, (Diciembre 2021) | en_US |
dc.subject | 250407 Geodesia por satélites | en_US |
dc.subject | 332401 Satélites artificiales | en_US |
dc.subject.other | Sentinel-2 | en_US |
dc.subject.other | Super-resolution | en_US |
dc.subject.other | Convolutional neural network | en_US |
dc.subject.other | Deep learning | en_US |
dc.title | Single-Image Super-Resolution of Sentinel-2 Low Resolution Bands with Residual Dense Convolutional Neural Networks | en_US |
dc.type | info:eu-repo/semantics/article | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.3390/rs13245007 | en_US |
dc.identifier.scopus | 2-s2.0-85121470362 | - |
dc.identifier.isi | WOS:000737252600001 | - |
dc.contributor.orcid | #NODATA# | - |
dc.contributor.orcid | #NODATA# | - |
dc.contributor.orcid | #NODATA# | - |
dc.identifier.issue | 24 | - |
dc.relation.volume | 13(24) | en_US |
dc.investigacion | Ingeniería y Arquitectura | en_US |
dc.type2 | Artículo | en_US |
dc.description.notas | This article belongs to the Special Issue Advanced Super-Resolution Methods in Remote Sensing | en_US |
dc.description.numberofpages | 20 | en_US |
dc.utils.revision | Sí | en_US |
dc.identifier.ulpgc | Sí | en_US |
dc.contributor.buulpgc | BU-ING | en_US |
dc.description.sjr | 1,283 | |
dc.description.jcr | 5,349 | |
dc.description.sjrq | Q1 | |
dc.description.jcrq | Q1 | |
dc.description.scie | SCIE | |
dc.description.miaricds | 10,6 | |
item.fulltext | Con texto completo | - |
item.grantfulltext | open | - |
crisitem.project.principalinvestigator | Marcello Ruiz, Francisco Javier | - |
crisitem.author.dept | GIR IOCAG: Procesado de Imágenes y Teledetección | - |
crisitem.author.dept | IU de Oceanografía y Cambio Global | - |
crisitem.author.dept | Departamento de Señales y Comunicaciones | - |
crisitem.author.orcid | 0000-0002-9646-1017 | - |
crisitem.author.parentorg | IU de Oceanografía y Cambio Global | - |
crisitem.author.fullName | Marcello Ruiz, Francisco Javier | - |
Colección: | Artículos |
Citas SCOPUSTM
11
actualizado el 08-sep-2024
Citas de WEB OF SCIENCETM
Citations
9
actualizado el 08-sep-2024
Visitas
99
actualizado el 06-jul-2024
Descargas
183
actualizado el 06-jul-2024
Google ScholarTM
Verifica
Altmetric
Comparte
Exporta metadatos
Los elementos en ULPGC accedaCRIS están protegidos por derechos de autor con todos los derechos reservados, a menos que se indique lo contrario.