An Adaptive Spectral Transformation Approach to Pan-Sharpening

Data transformation of multispectral imagery along the spectral dimension plays an important role in minimizing the spectral distortion of the resultant pan-sharpened image. Although there are various techniques available for spectral transformation, most of them are, data dependent. Hence, there is an ambiguity about the selection of the "appropriate" spectral transformation method. To alleviate this problem, an adaptive spectral transformation approach for pan-sharpening is presented in this paper. The efficiency of the presented method is tested by performing pan-sharpening of the high resolution (IKONOS and Quickbird) and the medium resolution (LandSat7 ETM+) datasets. The evaluation of the pan-sharpened images using global validation indexes reveal that the adaptive spectral approach helps reducing the spectral distortion.