Abstract: During the imaging process of remote sensing satellites,high-resolution panchromatic images and low-resolution multispectral images are usually obtained.To make full use of the advantages of these two types of images,this paper proposes a panchromatic sharpening method based on double-side guided filtering and multi-feature recalibration,aiming to generate high-resolution multispectral images that can maintain both spatial and spectral information.This method designs a dual-stream U-Net encoder-decoder network architecture.The multi-scale features of multispectral and panchromatic images are extracted by parallel branches,which avoids the information loss caused by direct feature fusion.A double-side guided filtering module is proposed,which can enhance feature interaction through the parallel feature-guided path and adaptive weight fusion mechanism.A multi-feature recalibration module is developed.This module combines a multi-directional edge detector and multi-head feature extraction mechanism and enhances the reconstruction ability of spatial details through a dynamic feature recalibration strategy,which effectively avoids the artifacts caused by excessive enhancement.Experiments show that the proposed method has good band adaptability.It can not only effectively process 4-band GeoEye1 and PLeiades1 data,but also perform well on 8-band WorldView2 and WorldView3 data sets.Quantitative evaluation results show that the proposed method achieves a CC value of 0.957 3 on the WorldView3 dataset;on the GeoEye1 dataset,the PSNR value reaches 38.093 8 dB,with the ERGAS value decreasing by 94.9% and the Q4 value increasing by 2.3% compared to the best-competing method.It is superior to the existing techniques in subjective visual effect and objective evaluation index and provides an effective solution for remote sensing image panchromatic sharpening tasks.

Key words: Remote sensing image fusion, Pansharpening, Deep learning, Double-side guided filter, Multi-feature recalibration