Comparison of nine hyperspectral pansharpening methods

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 resol ution of the latter. In the last decade many algorithms have been presented in the literature for pansharpening usi ng 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 hypersp ectral data with some of the state of the art methods for multispectral pansharpening, which have been adapted f or hyperspectral data. Nine methods from different classes are analysed: component substitution, multiresol ution 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.