Splitting the {Hyperspectral}-{Multispectral} {Image} {Fusion} {Problem} into {Weighted} {Pan}-sharpening {Problems} - {The} {Spectral} {Grouping} {Concept}

Due to the trade-off between spatial and spectral resolution in optical imaging systems, hyperspectral instruments provide data of comparatively low spatial resolution. In order to develop higher resolution hyperspectral data via post-processing, image fusion algorithms can be applied to combine this data with imagery of lower spectral but higher spatial resolution. The similar but simpler pan-sharpening problem, which refers to the fusion of low resolution multispectral with higher resolution panchromatic imagery, has been extensively studied and solved in the past two decades. This paper aims at making the large number of sophisticated pan-sharpening methods usable to solving the more challenging hyperspectral-multispectral image fusion problem. We propose a generic algorithm – called Spectral Grouping – to devide the hyperspectral-multispectral image fusion problem autonomously into weighted pan-sharpening problems using the relative spectral responses of both instruments.     «

Due to the trade-off between spatial and spectral resolution in optical imaging systems, hyperspectral instruments provide data of comparatively low spatial resolution. In order to develop higher resolution hyperspectral data via post-processing, image fusion algorithms can be applied to combine this data with imagery of lower spectral but higher spatial resolution. The similar but simpler pan-sharpening problem, which refers to the fusion of low resolution multispectral with higher resolution p...     »