Multiscale Fusion of Panchromatic and Multispectral Images Based on Adaptive Iterative Filtering

Abstract

This paper proposes an efficient and high-fidelity image fusion method based on adaptive smoothing filtering for panchromatic (PAN) and multispectral (MS) image fusion. The scale ratio reflects the ratio of spatial resolution between the panchromatic image and the multispectral image. When facing a multiscale fusion task, traditional methods are unable to simultaneously handle the problems of spectral resolution loss resulting from high scale ratios and the issue of reduced spatial resolution due to low scale ratios. To adapt to the fusion of panchromatic and multispectral satellite images of different scales, this paper improves the problem of the insufficient filtering of high-frequency information of remote sensing images of different scales by the classic filter-based intensity modulation (SFIM) model. It uses Gaussian convolution kernels instead of traditional mean convolution kernels and builds a Gaussian pyramid to adaptively construct convolution kernels of different scales to filter out high-frequency information of high-resolution images. It can adaptively process panchromatic multispectral images of different scales, iteratively filter the spatial information in panchromatic images, and ensure that the scale transformation is consistent with the definition of multispectral images. Using 15 common fusion methods, this paper compares the experimental results of ZY-3 with scale ratio 2.7 and SV-1 with scale ratio 4 data. The results show that the method proposed in this paper retains good spatial information for image fusion at different scales and has good spectral preservation.