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Comparison of nine hyperspectral pansharpening methods

Loncan, L. ; Bioucas-Dias, J. ; Almeida , L. ; Briottet, X. ; Chanussot, J. ; Dobigeon, ; Fabre, S. ; Liao, W. ; Licciardi, G. ; Simões, M. ; Tourneret, J.-Y. ; Veganzones, A. ; Vivone, G. ; Wei, Q. ; Yokoya, N.

Comparison of nine hyperspectral pansharpening methods, Proc IEEE International Geoscience and Remote Sensing Symp.- IGARSS, Milan, Italy, Vol. PP, pp. 1 - 4, July, 2015.

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Pansharpening first aims at fusing a panchromatic image with a multispectral image to generate an image with the high spatial resolution of the former and the spectral resolution of the latter. In the last decade many algorithms have been presented in the literature for pansharpening using multispectral data. With the increasing availability of hyperspectral systems these methods are now extending to hyperspectral images. In this work, we attempt to
compare new pansharpening techniques designed for hyperspectral data with some of the state of the art methods for multispectral pansharpening, which have been adapted for hyperspectral data. Nine methods from different
classes are analysed: component substitution, multiresolution analysis, hybrid, Bayesian and matrix decomposition approaches. These techniques are evaluated with the Wald’s Procol on one dataset to characterize their performances
and their robustness.