FG-GAN: Frequency-Guided Generative Adversarial Networks for Unsupervised Pansharpening

Pansharpening of multispectral images aims to merge multispectral (MS) and panchromatic (PAN) images to produce high-resolution multispectral (HRMS) images. Unsupervised pansharpening algorithms, which are widely used in deep learning for pansharpening tasks, commonly employ generative adversarial networks (GANs). However, existing unsupervised methods based on GANs have some limitations: 1) restricted ability for joint preservation of spatial and spectral information, and 2) the receptive field of general convolutions restricts the extraction of long-range dependencies. To address these issues, we propose a frequency-guided generative adversarial networks for unsupervised pansharpening (FG-GAN). Our unsupervised framework uses high-frequency and low-frequency information as prior constraints to guide the training of the FG-GAN’s generator and discriminator networks, thereby enhancing the joint preservation of spatial and spectral details. Furthermore, a graph convolution-based generator network is designed, in which long-range edge dependencies are extracted and propagated by learning the relationships between distant edge feature nodes. Extensive experiments on the Quickbird and Gaofen-2 datasets demonstrate the effectiveness of our design: our method enhances spatial details and reduces the spatial distortion index (Ds) by 44.8%, while achieving the fidelity of spatial-spectral information with a HQNR score of 0.99.