The official code for FGF-GAN: A Lightweight Generative Adversarial Network for Pansharpening via Fast Guided Filter (ICME 2021 Oral).
Zixiang Zhao, Jiangshe Zhang, Shuang Xu, Kai Sun, Lu Huang, Junmin Liu and Chunxia Zhang, "FGF-GAN: A Lightweight Generative Adversarial Network for Pansharpening via Fast Guided Filter," 2021 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2021.
@inproceedings{Zhao_ICME2021_FGFGAN,
author = {Zixiang Zhao and
Jiangshe Zhang and
Shuang Xu and
Kai Sun and
Lu Huang and
Junmin Liu and
Chunxia Zhang},
title = {{FGF-GAN:} {A} Lightweight Generative Adversarial Network for Pansharpening
via Fast Guided Filter},
booktitle = {{ICME}},
pages = {1--6},
publisher = {{IEEE}},
year = {2021}
}
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.
