FGF-GAN: A LIGHTWEIGHT GENERATIVE ADVERSARIAL NETWORK FOR PANSHARPENING VIA FAST GUIDED FILTER

Zixiang Zhao, Jiangshe Zhang, Shuang Xu, Kai Sun, Lu Huang, Junmin Liu, Chunxia Zhang

科研成果: 书/报告/会议事项章节会议稿件同行评审

26 引用 (Scopus)

摘要

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.

源语言英语
主期刊名2021 IEEE International Conference on Multimedia and Expo, ICME 2021
出版商IEEE Computer Society
ISBN(电子版)9781665438643
DOI
出版状态已出版 - 2021
已对外发布
活动2021 IEEE International Conference on Multimedia and Expo, ICME 2021 - Shenzhen, 中国
期限: 5 7月 20219 7月 2021

出版系列

姓名Proceedings - IEEE International Conference on Multimedia and Expo
ISSN(印刷版)1945-7871
ISSN(电子版)1945-788X

会议

会议2021 IEEE International Conference on Multimedia and Expo, ICME 2021
国家/地区中国
Shenzhen
时期5/07/219/07/21

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