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.
Highlights
Recent trends in image fusion, including remote sensing applications, involve the super-resolution (SR) paradigm and, more generally, apply constrained optimization algorithms to solve the ill-posed problem of spectral-spatial and spatio-temporal image resolution enhancement
Multitemporal pan-sharpening denotes the process by which MS and Pan datasets that are used to perform the data fusion task are acquired from the same platform, but at different times or from different platforms
Specific assumptions on the image formation process and model simplifications to make the problem mathematically tractable are normally required to solve the ill-posed problems that are usually encountered through constrained optimization algorithms
Summary
Recent trends in image fusion, including remote sensing applications, involve the super-resolution (SR) paradigm and, more generally, apply constrained optimization algorithms to solve the ill-posed problem of spectral-spatial (pan-sharpening) and spatio-temporal image resolution enhancement. Pan-sharpening denotes the merging of a monochrome image acquired by a broadband panchromatic (Pan) instrument with a multispectral (MS) image featuring a spectral diversity of bands and acquired over the same area, with a spatial resolution greater for the former. This can be seen as a particular problem of data fusion, in which the goal is to combine the spatial details resolved by the. Experimental comparisons on true and simulated images are presented in terms of computational time and quality indexes computed at the spatial resolution of the original multispectral images, i.e., SAM (Spectral Angle Mapper) and ERGAS (Erreur Relative Globale Adimensionnelle de Synthèse, from its French acronym)
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