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Remote Sens. 2016, 8(10), 797; doi:10.3390/rs8100797

A Review of Image Fusion Algorithms Based on the Super-Resolution Paradigm

Department of Information Engineering and Mathematics, University of Siena, via Roma, 56, Siena 53100, Italy
Academic Editors: Gonzalo Pajares Martinsanz, Richard Gloaguen and Prasad S. Thenkabail
Received: 8 August 2016 / Revised: 12 September 2016 / Accepted: 20 September 2016 / Published: 24 September 2016
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Abstract

A critical analysis of remote sensing image fusion methods based on the super-resolution (SR) paradigm is presented in this paper. Very recent algorithms have been selected among the pioneering studies adopting a new methodology and the most promising solutions. After introducing the concept of super-resolution and modeling the approach as a constrained optimization problem, different SR solutions for spatio-temporal fusion and pan-sharpening are reviewed and critically discussed. Concerning pan-sharpening, the well-known, simple, yet effective, proportional additive wavelet in the luminance component (AWLP) is adopted as a benchmark to assess the performance of the new SR-based pan-sharpening methods. The widespread quality indexes computed at degraded resolution, with the original multispectral image used as the reference, i.e., SAM (Spectral Angle Mapper) and ERGAS (Erreur Relative Globale Adimensionnelle de Synthèse), are finally presented. Considering these results, sparse representation and Bayesian approaches seem far from being mature to be adopted in operational pan-sharpening scenarios. View Full-Text
Keywords: image fusion; pan-sharpening; super-resolution; sparse representations image fusion; pan-sharpening; super-resolution; sparse representations
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Garzelli, A. A Review of Image Fusion Algorithms Based on the Super-Resolution Paradigm. Remote Sens. 2016, 8, 797.

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