Abstract: The purpose of pan-sharpening is to fuse high-resolution panchromatic images and low-resolution multispectral images generated by the same satellite to generate a high-resolution multispect- ral image.Existing models fail to fully explore the correlation and complementarity between different modalities when fusing multimodal information,resulting in the inability to fully leverage the advantages of multimodal information,thus affecting the quality of pan-sharpening.To address the question of insufficient cross-modal feature extraction and fusion,this paper proposes a dual-stream network based on feature decoupling to capture richer feature information from panchromatic and multispectral images.Specifically,the proposed network utilizes an encoder to decompose the features of panchromatic and multispectral images into global features and local features,thereby improving the models ability to capture long-range dependencies and local details.Subsequently,a cross-modal feature fusion module integrates these features within and across domains,allowing the model to learn more comprehensive and rich feature representations at different levels.Then,a progressive fusion module gradually merges glo- bal and local features to obtain more accurate feature representations.Finally,the fused features are fed into the decoder to generate a high-resolution multispectral image.Experimental results on GaoFen-2 and WorldView-3 demonstrate the superiority of the proposed model compared to many existing models.

Key words: pan-sharpening, feature decoupling, cross-modal feature fusion