LAUSR

To reduce the loss of detail and spectral information during the network propagation and better extract detail and spectral features at different scales, a novel dual-information compensation network for pansharpening (DCNP) is proposed for fusing multispectral (MS) and panchromatic (PAN) images. In the network, the domain-specific knowledge is considered to design our DCNP architecture by focusing on the two aims of the pansharpening: spatial and spectral preservation. Specifically, a cascaded U-shaped structure is constructed to improve the feature representation ability of the network. To preserve more spatial details in the pansharpened image, the details of the PAN image are extracted based on the Laplacian operator and then compensated into the network. Furthermore, for spectral preservation, the MS image is conducted by the transposed convolution as the compensation information of the network. Experiments on the full- and reduced-scale data indicate that the proposed DCNP achieves significant improvement over state-of-the-art methods in terms of subjective and objective evaluation. Specifically, DCNP improves the PSNR and ERGAS metrics by 12.9% and 44.5%, respectively, compared to the deep learning-based approach with the best average values on Pléiades.