Comparison of two methods of the fusion of remote sensing images with fidelity of spectral information

In recent years, as the availability of remote sensing imageries of varying resolution has increased, merging images of differing spatial and spectral resolutions has become a significant operation in the field of remote sensing. With the development of quantitative remote sensing, not only improving spatial details but also preserving the spectral information of multispectral bands were required. The principle, algorithm and methods of two fusion algorithms, SFIM (Smoothing Filter-based Intensity Modulation) and Gram-Schmidt (Gram-Schmidt transform), were described, also advantage and disadvantage of them were discussed. In a case of Ikonos image in city, visual judgment, quantitative statistical parameters and graph method were used to qualitatively and quantitatively assess these two algorithms, also compared to the traditional fusion methods of IHS transform and PC (principal component) transform. The results showed that there was no distinct difference in spatial details improved. However in terms of spectral information fidelity, both IHS and PC method were the worst, Gram-Schmidt method was better, while SFIM method was the best.