This paper presents a novel multi-sensor image fusion algorithm, which extends pan-sharpening of multispectral (MS) data through intensity modulation to the integration of MS and SAR imagery. The method relies on SAR texture, extracted by ratioing the despeckled SAR image to its lowpass approximation. SAR texture is used to modulate the generalized intensity (GI) of the MS image, which is given by a linear transform extending Intensity-Hue-Saturation (IHS) transform to an arbitrary number of bands. Before modulation, the GI is enhanced by injection of highpass details extracted from the available Pan image by means of the "à-trous" wavelet decomposition. The texture-modulated pan-sharpened GI replaces the GI calculated from the resampled original MS data; then the inverse transform is applied to obtain the fusion product. Experimental results are presented on Landsat-7/ETM+ and ERS-2 images of an urban area. The results demonstrate accurate spectral preservation on vegetated regions, bare soil, and also on textured areas (buildings and road network) where SAR texture information enhances the fusion product, which can be usefully applied for both visual analysis and classification purposes.
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