Abstract: The aim of remote sensing image fusion is to obtain high-resolution multispectral images with the same spectral resolution as multispectral images and the same spatial resolution as panchromatic images.Although deep learning has achieved remarkable results in remote sensing image fusion,the network cannot fully extract the rich spatial information in the image due to the limitation of the deep model network,which leads to the lack of spatial information in the fused image and low quality of the fusion result.Therefore,this paper introduces multi-scale blocks,where image features at different scales can be learned by convolutional kernels of different sizes,thus increasing the richness of the extracted features.Dense convolutional blocks are then introduced to achieve feature reuse through dense connections,reducing the loss of shallow feature information when the network is deep.In the feature fusion stage,the proposed method uses feature maps from different levels of the network as input to the feature fusion layer to improve the quality of the fused images.Comparison experiments are performed with six fusion algorithms on GE1 and QB datasets,and the experimental results show that the fused images of the proposed method retain spatial and spectral information better,and outperform the comparison methods in both subjective and objective evaluations.

Key words: Remote sensing image fusion, Deep learning, Multispectral images, Multiscale convolution block, Dense connection