An Unsupervised Remote Sensing Single-Image Super-Resolution Method Based on Generative Adversarial Network
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 propos...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8986554/ |
| _version_ | 1830089891515465728 |
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| author | Ning Zhang Yongcheng Wang Xin Zhang Dongdong Xu Xiaodong Wang |
| author_facet | Ning Zhang Yongcheng Wang Xin Zhang Dongdong Xu Xiaodong Wang |
| author_sort | Ning Zhang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-16T16:53:29Z |
| format | Article |
| id | doaj.art-3285110d58a741799a298d2c0f469e02 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T16:53:29Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-3285110d58a741799a298d2c0f469e022022-12-21T22:23:57ZengIEEEIEEE Access2169-35362020-01-018290272903910.1109/ACCESS.2020.29723008986554An Unsupervised Remote Sensing Single-Image Super-Resolution Method Based on Generative Adversarial NetworkNing Zhang0https://orcid.org/0000-0002-1920-0649Yongcheng Wang1https://orcid.org/0000-0002-1647-2956Xin Zhang2https://orcid.org/0000-0002-4026-4284Dongdong Xu3https://orcid.org/0000-0002-1528-4363Xiaodong Wang4https://orcid.org/0000-0001-5997-4174Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, ChinaImage 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.https://ieeexplore.ieee.org/document/8986554/Image super-resolutionunsupervised learningremote sensinggenerative adversarial network |
| spellingShingle | Ning Zhang Yongcheng Wang Xin Zhang Dongdong Xu Xiaodong Wang An Unsupervised Remote Sensing Single-Image Super-Resolution Method Based on Generative Adversarial Network IEEE Access Image super-resolution unsupervised learning remote sensing generative adversarial network |
| title | An Unsupervised Remote Sensing Single-Image Super-Resolution Method Based on Generative Adversarial Network |
| title_full | An Unsupervised Remote Sensing Single-Image Super-Resolution Method Based on Generative Adversarial Network |
| title_fullStr | An Unsupervised Remote Sensing Single-Image Super-Resolution Method Based on Generative Adversarial Network |
| title_full_unstemmed | An Unsupervised Remote Sensing Single-Image Super-Resolution Method Based on Generative Adversarial Network |
| title_short | An Unsupervised Remote Sensing Single-Image Super-Resolution Method Based on Generative Adversarial Network |
| title_sort | unsupervised remote sensing single image super resolution method based on generative adversarial network |
| topic | Image super-resolution unsupervised learning remote sensing generative adversarial network |
| url | https://ieeexplore.ieee.org/document/8986554/ |
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