PANSHARPENING OF HYPERSPECTRAL IMAGES IN URBAN AREAS
Other literature type
Pansharpening has proven to be a valuable method for resolution enhancement of multi-band images when spatially high-resolving
panchromatic images are available in addition. In principle, pansharpening can beneficially be applied to hyperspectral images as well.
But whereas the grey values of multi-spectral images comprise at most relative information about the registered intensities, calibrated
hyperspectral images are supposed to provide absolute reflectivity values of the respective material surfaces. This physical significance
of the hyperspectral data should be preserved within the pansharpening process as much as possible. In this paper we compare several
common pansharpening methods such as Principal Component Fusion, Wavelet Fusion, Gram-Schmidt transform and investigate their
applicability for hyperspectral data. Our focus is on the impact of the pansharpening on material classifications. Apart from applying
common quality measures, we compare the results of material classifications from hyperspectral data, which were pansharpened by
different methods. In addition we propose an alternative pansharpening method which is based on an initial segmentation of the
panchromatic image with an additional use of map vector data.