Name
Abstract | ||
|---|---|---|
Remote sensing image fusion (also known as pan-sharpening) aims to generate a high resolution multi-spectral image from inputs of a high spatial resolution single band panchromatic (PAN) image and a low spatial resolution multi-spectral (MS) image. In this paper, we propose PSGAN, a generative adversarial network (GAN) for remote sensing image pan-sharpening. To the best of our knowledge, this is the first attempt at producing high quality pan-sharpened images with GANs. The PSGAN consists of two parts. Firstly, a two-stream fusion architecture is designed to generate the desired high resolution multi-spectral images, then a fully convolutional network serving as a discriminator is applied to distinct "real" or "pan-sharpened" MS images. Experiments on images acquired by Quickbird and GaoFen-1 satellites demonstrate that the proposed PSGAN can fuse PAN and MS images effectively and significantly improve the results over the state of the art traditional and CNN based pan-sharpening methods. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/icip.2018.8451049 | 2018 25TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) |
Keywords | DocType | Volume |
Image fusion, pan-sharpening, GAN, deep learning, remote sensing | Conference | abs/1805.03371 |
ISSN | Citations | PageRank |
1522-4880 | 1 | 0.35 |
References | Authors | |
9 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiangyu Liu | 1 | 51 | 14.10 |
| Yunhong Wang | 2 | 3816 | 278.50 |
| Qingjie Liu | 3 | 92 | 18.60 |