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13 March 2003 Context modeling for joint spectral and radiometirc distortion minimization in pyramid-based fusion of MS and P image data
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Data fusion based on multiresolution analysis requires the definition of a proper model establishing how the missing highpass information to be injected into the resampled multispectral (MS) bands is extracted from the panchromatic (P) band. Such a model can be global over the whole image or depend on the spatial context. Goal of the model is to make the fused bands the most similar to what the MS sensor would image if it had the same resolution as the broadband one. In this perspective, both radiometric and spectral distortions are jointly considered in the proposed model which has been set up through simulated SPOT 5 data (XS + P) of an urban area including vegetation. A space-varying equalization of sensors is achieved by multiplying the highpass pixel detail extracted from the P image by the ratio between the pixel values in the expanded XS and and in the lowpass version of the P band. Radiometric distortion (RMSE between true and fused XS bands) is abated by almost 20 with respect to the case in which as many scalar cross-gain factors as are the bands are employed. Spectral distortion is measured as the absolute angle between a pixel vector in the reference and fused bands. It can be perceived a change in color hues between the true and fused color-composite images. Thanks to the proposed injection model, the spectral angle of the fused product is identical to that measured between the true and resampled original data. Besides spectral distortions, also spatial distortions, e.g., ringing artifacts and aliasing impairments, which are typical of critically-subsampled multiresolution fusion schemes, are completely missing in this pyramid approach.
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Bruno Aiazzi, Luciano Alparone, Stefano Baronti, Ivan Pippi, and Massimo Selva "Context modeling for joint spectral and radiometirc distortion minimization in pyramid-based fusion of MS and P image data", Proc. SPIE 4885, Image and Signal Processing for Remote Sensing VIII, (13 March 2003);

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