Journal
2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019)
Volume -, Issue -, Pages 3117-3120Publisher
IEEE
DOI: 10.1109/igarss.2019.8898648
Keywords
super-resolution; unsupervised learning; CNN; remote sensing image
Categories
Funding
- National Key Research and Development Program of China [2016YFB0501300, 2016YFB0501302]
- National Natural Science Foundation of China [61501009, 61771031]
- Fundamental Research Funds for the Central Universities
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Single image super-resolution (SISR) is a useful procedure for many remote sensing applications. However, paired high-resolution and low-resolution remote sensing images are actually hard to acquire for supervised learning SR methods. In this paper, we propose an unsupervised network named Cycle-CNN to handle this problem. Our network consists of two generative CNNs for down-sampling and super-resolution separately, and can be trained with unpaired data. Experiments on panchromatic and multi-spectral images of GaoFen-2 satellite indicate that our method achieves state-of-the-art SR results and is robust against noise and blur in the remote sensing images.
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