Abstract: In remote sensing imaging,PAN images have higher spatial resolution,while MS images contain more spectral information.Therefore,it is an important technique to fuse them to obtain high-resolution multispectral images.The spatial details of panchromatic sharpening are limited because CNNS often fail to accurately capture long-range spatial features.In order to fully extract the spatial information of panchromatic images and the spectral information of multispectral images,this paper proposes a dual-branch attention network for remote sensing image fusion tasks.Different from the previous methods that use pure convolutional neural networks to extract spatial and spectral information,this method introduces a spatial attention module and a channel attention module into the convolutional block to focus on spatial and spectral information respectively,and performs information interaction between layers to fully extract spatial information and spectral information.At the same time,based on the Transformer architecture,this paper builds the global branch of the Transformer to fully learn the spatial and spectral features in the image,and finally obtains the multispectral image with high spatial resolution after decoding.Full-resolution and reduced-resolution experiments are carried out on the IKONOS and WorldView-2 datasets.Experimental results show that the proposed method achieves better results than other methods in terms of objective indicators and subjective vision.

Key words: Deep learning, Convolutional neural network, Multispectral and panchromatic image, Attention mechanism, Image fusion