dc.contributor.author | Fernandez-Beltran, Ruben | |
dc.contributor.author | Latorre Carmona, Pedro | |
dc.contributor.author | Pla, Filiberto | |
dc.date.accessioned | 2017-03-09T15:55:13Z | |
dc.date.available | 2017-03-09T15:55:13Z | |
dc.date.issued | 2017 | |
dc.identifier.citation | Ruben Fernandez-Beltran, Pedro Latorre-Carmona & Filiberto Pla (2017) Latent topic-based super-resolution for remote sensing, Remote Sensing Letters, 8:6, 498-507 | ca_CA |
dc.identifier.issn | 2150-704X | |
dc.identifier.issn | 2150-7058 | |
dc.identifier.uri | http://hdl.handle.net/10234/166612 | |
dc.description.abstract | This letter presents a novel single-image Super-Resolution (SR)
approach based on latent topics specially designed to remote sensing
imagery. The proposed approach pursues to super-resolve topics
uncovered from low-resolution images instead of super-resolving
image patches themselves. An experimental comparison is con-
ducted using nine di
ff
erent SR methods over four aerial image data-
sets. Experiments revealed the potential of latent topics in remote
sensing SR by reporting that the proposed approach is able to
provide a competitive advantage especially in low noise conditions. | ca_CA |
dc.description.sponsorShip | This work was supported by the Spanish Ministry of Economy under the projects ESP2013-48458-
C4-3-P and ESP2016-79503-C2-2-P, by Generalitat Valenciana through project PROMETEO-II/2014/
062, and by Universitat Jaume I through project P11B2014-09. | ca_CA |
dc.format.extent | 10 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | Taylor & Francis | ca_CA |
dc.relation.isPartOf | Remote Sensing Letters, 2017, vol. 8, núm. 6 | ca_CA |
dc.rights | © 2017 Informa UK Limited, trading as Taylor & Francis Group.
"This is an Accepted Manuscript of an Article published in Remote Sensing Letters, 2017, vol. 8, núm. 6, available online at: http://www.tandfonline.com/doi/full/10.1080/2150704X.2017.1287974" | ca_CA |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | * |
dc.subject | Super-resolution | ca_CA |
dc.subject | Latent topics | ca_CA |
dc.subject | LDA | ca_CA |
dc.subject | Image quality assessment | ca_CA |
dc.title | Latent topic-based super-resolution for remote sensing | ca_CA |
dc.type | info:eu-repo/semantics/article | ca_CA |
dc.identifier.doi | http://dx.doi.org/10.1080/2150704X.2017.1287974 | |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_CA |
dc.relation.publisherVersion | http://www.tandfonline.com/doi/full/10.1080/2150704X.2017.1287974 | ca_CA |
dc.type.version | info:eu-repo/semantics/acceptedVersion | |