Abstract: SFIM is a commonly used panchromatic-multispectral fusion algorithm with good spectral injection capability， but the poor quality of spatial information incorporation is due to the poor acquisition of ideal low-resolution panchromatic images. Aiming at the shortcomings of SFIM， a SFIM model combining IHS and Gaussian filtering is proposed. The method uses an adaptive linear combination to obtain the I-Component of the multispectral image. Then， with the average gradient of the best adjusted I-component as the standard， the ideal low-resolution panchromatic image is determined by Gaussian filtering of the downsampled panchromatic image. Finally， the multispectral image and the low-resolution panchromatic image are upsampled to the same size as the panchromatic image， and the SFIM transform is performed to obtain the fusion results. The experiments are carried out on the data of GF-2 and ZY3-1. The experimental results show that the algorithm better overcomes the shortcomings of IHS and SFIM and better performs in both qualitative and quantitative analysis. It has better spectral retention and injects more detailed spatial detail information， which effectively improves the quality of fused image detail information. This experiment can provide a useful value reference for the study of panchromatic-multispectral image fusion.

Key words:  , image fusion； SFIM； IHS； Gaussian filter； average gradient