  Pansharpening is a widely used image enhancement technique for remote
sensing. Its principle is to fuse the input high-resolution single-channel
panchromatic (PAN) image and low-resolution multi-spectral image and to obtain
a high-resolution multi-spectral (HRMS) image. The existing deep learning
pansharpening method has two shortcomings. First, features of two input images
need to be concatenated along the channel dimension to reconstruct the HRMS
image, which makes the importance of PAN images not prominent, and also leads
to high computational cost. Second, the implicit information of features is
difficult 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 replaced
by FGF to better retain the spatial information while reducing the number of
parameters. Meanwhile, the fusion objects can be highlighted by the spatial
attention module. In addition, the latent information of features can be
preserved effectively through adversarial training. Numerous experiments
illustrate that our network generates high-quality HRMS images that can surpass
existing methods, and with fewer parameters.
