@inproceedings{9d0b7738c0174ae1ad25e3eb5176eee6,
title = "Unsupervised Remote Sensing Image Super-Resolution Using Cycle CNN",
abstract = "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.",
keywords = "CNN, remote sensing image, super-resolution, unsupervised learning",
author = "Pengrui Wang and Haopeng Zhang and Feng Zhou and Zhiguo Jiang",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 ; Conference date: 28-07-2019 Through 02-08-2019",
year = "2019",
month = jul,
doi = "10.1109/IGARSS.2019.8898648",
language = "英语",
series = "International Geoscience and Remote Sensing Symposium (IGARSS) ",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3117--3120",
booktitle = "2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings",
address = "美国",
}