CIOMP OpenIR
An Unsupervised Remote Sensing Single-Image Super-Resolution Method Based on Generative Adversarial Network
N. Zhang; Y. C. Wang; X. Zhang; D. D. Xu and X. D. Wang
2020
发表期刊Ieee Access
ISSN2169-3536
卷号8页码:29027-29039
摘要Image super-resolution (SR) technique can improve the spatial resolution of images without upgrading the imaging system. As a result, SR promotes the development of high resolution (HR) remote sensing image applications. Many remote sensing image SR algorithms based on deep learning have been proposed recently, which can effectively improve the spatial resolution under the constraints of HR images. However, images acquired by remote sensing imaging devices typically have lower resolution. Hence, an insufficient number of HR remote sensing images are available for training deep neural networks. In view of this problem, we propose an unsupervised SR method that does not require HR remote sensing images. The proposed method introduces a generative adversarial network (GAN) that obtains SR images through the generator; then, the SR images are downsampled to train the discriminator with low resolution (LR) images. Our method outperformed several methods in terms of the quality of the obtained SR images as measured by 6 evaluation metrics, which proves the satisfactory performance of the proposed unsupervised method for improving the spatial resolution of remote sensing images.
DOI10.1109/access.2020.2972300
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收录类别SCI ; EI
语种英语
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文献类型期刊论文
条目标识符http://ir.ciomp.ac.cn/handle/181722/64996
专题中国科学院长春光学精密机械与物理研究所
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GB/T 7714
N. Zhang,Y. C. Wang,X. Zhang,et al. An Unsupervised Remote Sensing Single-Image Super-Resolution Method Based on Generative Adversarial Network[J]. Ieee Access,2020,8:29027-29039.
APA N. Zhang,Y. C. Wang,X. Zhang,&D. D. Xu and X. D. Wang.(2020).An Unsupervised Remote Sensing Single-Image Super-Resolution Method Based on Generative Adversarial Network.Ieee Access,8,29027-29039.
MLA N. Zhang,et al."An Unsupervised Remote Sensing Single-Image Super-Resolution Method Based on Generative Adversarial Network".Ieee Access 8(2020):29027-29039.
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