  Pansharpening is a fundamental issue in remote sensing field. This paper
proposes a side information partially guided convolutional sparse coding (SCSC)
model for pansharpening. The key idea is to split the low resolution
multispectral image into a panchromatic image related feature map and a
panchromatic image irrelated feature map, where the former one is regularized
by the side information from panchromatic images. With the principle of
algorithm unrolling techniques, the proposed model is generalized as a deep
neural network, called as SCSC pansharpening neural network (SCSC-PNN).
Compared with 13 classic and state-of-the-art methods on three satellites, the
numerical experiments show that SCSC-PNN is superior to others. The codes are
available at https://github.com/xsxjtu/SCSC-PNN.
