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Single-frame super-resolution in remote sensing: a practical overview
dc.contributor.author | Fernandez-Beltran, Ruben | |
dc.contributor.author | Latorre Carmona, Pedro | |
dc.contributor.author | Pla, Filiberto | |
dc.date.accessioned | 2017-01-09T11:12:16Z | |
dc.date.available | 2017-01-09T11:12:16Z | |
dc.date.issued | 2017 | |
dc.identifier.citation | FERNANDEZ-BELTRAN, Ruben; LATORRE-CARMONA, Pedro; PLA, Filiberto. Single-frame super-resolution in remote sensing: a practical overview. International Journal of Remote Sensing, 2017, vol. 38, no 1, p. 314-354. | ca_CA |
dc.identifier.uri | http://hdl.handle.net/10234/165265 | |
dc.description.abstract | Image acquisition technology is improving very fast from a performance point of view. However, there are physical restrictions that can only be solved using software processing strategies. This is particularly true in the case of super resolution (SR) methodologies. SR techniques have found a fertile application field in airborne and space optical acquisition platforms. Single-frame SR methods may be advantageous for some remote-sensing platforms and acquisition time conditions. The contributions of this article are basically two: (1) to present an overview of single-frame SR methods, making a comparative analysis of their performance in different and challenging remote-sensing scenarios, and (2) to propose a new single-frame SR taxonomy, and a common validation strategy. Finally, we should emphasize that, on the one hand, this is the first time, to the best of our knowledge, that such a review and analysis of single SR methods is made in the framework of remote sensing, and, on the other hand, that the new single-frame SR taxonomy is aimed at shedding some light when classifying some types of single-frame SR methods. | ca_CA |
dc.description.sponsorShip | This work was supported by the Spanish Ministry of Economy under the project ESP2013 - 48458-C4-3-P, by Generalitat Valenciana through project PROMETEO-II/2014/062, and by Universitat Jaume I through project P11B2014-09. | ca_CA |
dc.format.extent | 36 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 | International Journal of Remote Sensing, 2017, vol. 38, no 1 | ca_CA |
dc.rights | Copyright © 2016 Taylor & Francis | ca_CA |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | * |
dc.subject | Super-Resolution | ca_CA |
dc.subject | Remote Sensing | ca_CA |
dc.subject | Image Reconstruction | ca_CA |
dc.subject | Image Learning | ca_CA |
dc.subject | Image Quality Assessment | ca_CA |
dc.title | Single-frame super-resolution in remote sensing: a practical overview | ca_CA |
dc.type | info:eu-repo/semantics/article | ca_CA |
dc.identifier.doi | http://dx.doi.org/10.1080/01431161.2016.1264027 | |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_CA |
dc.relation.publisherVersion | http://www.tandfonline.com/doi/abs/10.1080/01431161.2016.1264027 | ca_CA |
dc.type.version | info:eu-repo/semantics/acceptedVersion |
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