Single-frame super-resolution in remote sensing: a practical overview
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Other documents of the author: Fernandez-Beltran, Ruben; Latorre Carmona, Pedro; Pla, Filiberto
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comunitat-uji-handle2:10234/7038
comunitat-uji-handle3:10234/8634
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Title
Single-frame super-resolution in remote sensing: a practical overviewDate
2017Publisher
Taylor & FrancisBibliographic 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.Type
info:eu-repo/semantics/articlePublisher version
http://www.tandfonline.com/doi/abs/10.1080/01431161.2016.1264027Version
info:eu-repo/semantics/acceptedVersionSubject
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 ... [+]
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. [-]
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International Journal of Remote Sensing, 2017, vol. 38, no 1Rights
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