  Pansharpening aims at fusing a panchromatic image with a multispectral one,
to generate an image with the high spatial resolution of the former and the
high 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 being adapted to hyperspectral images. In this work, we 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. Eleven methods from different classes (component
substitution, multiresolution analysis, hybrid, Bayesian and matrix
factorization) are analyzed. These methods are applied to three datasets and
their effectiveness and robustness are evaluated with widely used performance
indicators. In addition, all the pansharpening techniques considered in this
paper have been implemented in a MATLAB toolbox that is made available to the
community.
