SUPER-RESOLUTION RESEARCH ON REMOTE SENSING IMAGES IN THE MEGACITY BASED ON IMPROVED SRGAN
Remote sensing images of Earth observation with high spatial resolution and high temporal resolution are critical for the application of remote sensing technology in Megacities.With the development of Smart City,more demands which are still difficult to be perfectly satisfied on the spatial resoluti...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2022-05-01
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| Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-3-2022/603/2022/isprs-annals-V-3-2022-603-2022.pdf |
| _version_ | 1818211482789216256 |
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| author | L. Xin L. Xin Z. Li Z. Li S. Wang S. Wang |
| author_facet | L. Xin L. Xin Z. Li Z. Li S. Wang S. Wang |
| author_sort | L. Xin |
| collection | DOAJ |
| description | Remote sensing images of Earth observation with high spatial resolution and high temporal resolution are critical for the application of remote sensing technology in Megacities.With the development of Smart City,more demands which are still difficult to be perfectly satisfied on the spatial resolution and temporal resolution of remote sensing images have been put forward.This paper studies the use of SRGAN which means Super-Resolution using a Generative Adversarial Network (a network structure that uses the loss function considering the perceptual loss and the adversarial loss to improve the spatial resolution of remote sensing images) for super-resolution reconstruction of single remote sensing image.It is able to enhance the spatial resolution of remote sensing images and improve the depth and breadth of remote sensing images.We adjust the reasonable parameters and network structure for our research by analysing the SRGAN in the network architecture, the perceptual loss and the adversarial loss.A super-resolution model is obtained by training with aerial photogrammetry images whose spatial resolution are 0.1 meter in Shanghai.We find the improved SRGAN has a good performance in in remote sensing image super-resolution by comparing the super-resoved images with real high-resolution images in visual perception, spatial position mapping accuracy and chromaticity spatial information. In addition, it is proved that the trained model is also effective to deal with Worldview-2 and SuperView-1 satellite images whose spatial resolution are 0.5 m. Our research shows that our method which can effectively realize the super-resolution of remote sensing images has great potential in the application of remote sensing technology such as urban mapping and changes monitoring. |
| first_indexed | 2024-12-12T05:33:12Z |
| format | Article |
| id | doaj.art-1bc2183fe5684587a245e56fd7109fdf |
| institution | Directory Open Access Journal |
| issn | 2194-9042 2194-9050 |
| language | English |
| last_indexed | 2024-12-12T05:33:12Z |
| publishDate | 2022-05-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-1bc2183fe5684587a245e56fd7109fdf2022-12-22T00:36:14ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502022-05-01V-3-202260360910.5194/isprs-annals-V-3-2022-603-2022SUPER-RESOLUTION RESEARCH ON REMOTE SENSING IMAGES IN THE MEGACITY BASED ON IMPROVED SRGANL. Xin0L. Xin1Z. Li2Z. Li3S. Wang4S. Wang5Shanghai Institute of Surveying and Mapping, Shanghai,,No.419 of Wuning Road, Putuo District of Shanghai, ChinaKey Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities, MNR, ChinaShanghai Institute of Surveying and Mapping, Shanghai,,No.419 of Wuning Road, Putuo District of Shanghai, ChinaKey Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities, MNR, ChinaShanghai Institute of Surveying and Mapping, Shanghai,,No.419 of Wuning Road, Putuo District of Shanghai, ChinaKey Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities, MNR, ChinaRemote sensing images of Earth observation with high spatial resolution and high temporal resolution are critical for the application of remote sensing technology in Megacities.With the development of Smart City,more demands which are still difficult to be perfectly satisfied on the spatial resolution and temporal resolution of remote sensing images have been put forward.This paper studies the use of SRGAN which means Super-Resolution using a Generative Adversarial Network (a network structure that uses the loss function considering the perceptual loss and the adversarial loss to improve the spatial resolution of remote sensing images) for super-resolution reconstruction of single remote sensing image.It is able to enhance the spatial resolution of remote sensing images and improve the depth and breadth of remote sensing images.We adjust the reasonable parameters and network structure for our research by analysing the SRGAN in the network architecture, the perceptual loss and the adversarial loss.A super-resolution model is obtained by training with aerial photogrammetry images whose spatial resolution are 0.1 meter in Shanghai.We find the improved SRGAN has a good performance in in remote sensing image super-resolution by comparing the super-resoved images with real high-resolution images in visual perception, spatial position mapping accuracy and chromaticity spatial information. In addition, it is proved that the trained model is also effective to deal with Worldview-2 and SuperView-1 satellite images whose spatial resolution are 0.5 m. Our research shows that our method which can effectively realize the super-resolution of remote sensing images has great potential in the application of remote sensing technology such as urban mapping and changes monitoring.https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-3-2022/603/2022/isprs-annals-V-3-2022-603-2022.pdf |
| spellingShingle | L. Xin L. Xin Z. Li Z. Li S. Wang S. Wang SUPER-RESOLUTION RESEARCH ON REMOTE SENSING IMAGES IN THE MEGACITY BASED ON IMPROVED SRGAN ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | SUPER-RESOLUTION RESEARCH ON REMOTE SENSING IMAGES IN THE MEGACITY BASED ON IMPROVED SRGAN |
| title_full | SUPER-RESOLUTION RESEARCH ON REMOTE SENSING IMAGES IN THE MEGACITY BASED ON IMPROVED SRGAN |
| title_fullStr | SUPER-RESOLUTION RESEARCH ON REMOTE SENSING IMAGES IN THE MEGACITY BASED ON IMPROVED SRGAN |
| title_full_unstemmed | SUPER-RESOLUTION RESEARCH ON REMOTE SENSING IMAGES IN THE MEGACITY BASED ON IMPROVED SRGAN |
| title_short | SUPER-RESOLUTION RESEARCH ON REMOTE SENSING IMAGES IN THE MEGACITY BASED ON IMPROVED SRGAN |
| title_sort | super resolution research on remote sensing images in the megacity based on improved srgan |
| url | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-3-2022/603/2022/isprs-annals-V-3-2022-603-2022.pdf |
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