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Pansharpening is a widely used image enhancement technique for remotesensing. Its principle is to fuse the input high-resolution single-channelpanchromatic (PAN) image and low-resolution multi-spectral image and to obtaina high-resolution multi-spectral (HRMS) image. The existing deep learningpansharpening method has two shortcomings. First, features of two input imagesneed to be concatenated along the channel dimension to reconstruct the HRMSimage, which makes the importance of PAN images not prominent, and also leadsto high computational cost. Second, the implicit information of features isdifficult to extract through the manually designed loss function. To this end,we propose a generative adversarial network via the fast guided filter (FGF)for pansharpening. In generator, traditional channel concatenation is replacedby FGF to better retain the spatial information while reducing the number ofparameters. Meanwhile, the fusion objects can be highlighted by the spatialattention module. In addition, the latent information of features can bepreserved effectively through adversarial training. Numerous experimentsillustrate that our network generates high-quality HRMS images that can surpassexisting methods, and with fewer parameters.

FGF-GAN: A Lightweight Generative Adversarial Network for Pansharpening via Fast Guided Filter