Improving Spatial Resolution of Satellite Imagery Using Generative Adversarial Networks and Window Functions
Dynamic technological progress has contributed to the development of systems imaging of the Earth’s surface as well as data mining methods. One such example is super-resolution (SR) techniques that allow for the improvement of the spatial resolution of satellite imagery on the basis of a low-resolut...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-12-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/14/24/6285 |
| _version_ | 1827637107088162816 |
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| author | Kinga Karwowska Damian Wierzbicki |
| author_facet | Kinga Karwowska Damian Wierzbicki |
| author_sort | Kinga Karwowska |
| collection | DOAJ |
| description | Dynamic technological progress has contributed to the development of systems imaging of the Earth’s surface as well as data mining methods. One such example is super-resolution (SR) techniques that allow for the improvement of the spatial resolution of satellite imagery on the basis of a low-resolution image (LR) and an algorithm using deep neural networks. The limitation of these solutions is the input size parameter, which defines the image size that is adopted by a given neural network. Unfortunately, the value of this parameter is often much smaller than the size of the images obtained by Earth Observation satellites. In this article, we presented a new methodology for improving the resolution of an entire satellite image, using a window function. In addition, we conducted research to improve the resolution of satellite images acquired with the World View 2 satellite using the ESRGAN network, we determined the number of buffer pixels that will make it possible to obtain the best image quality. The best reconstruction of the entire satellite imagery using generative neural networks was obtained using a Triangular window (for 10% coverage). The Hann-Poisson window worked best when more overlap between images was used. |
| first_indexed | 2024-03-09T15:54:41Z |
| format | Article |
| id | doaj.art-9cd0a066dbc34b24ac90b1b78d1d3e31 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T15:54:41Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-9cd0a066dbc34b24ac90b1b78d1d3e312023-11-24T17:47:11ZengMDPI AGRemote Sensing2072-42922022-12-011424628510.3390/rs14246285Improving Spatial Resolution of Satellite Imagery Using Generative Adversarial Networks and Window FunctionsKinga Karwowska0Damian Wierzbicki1Department of Imagery Intelligence, Faculty of Civil Engineering and Geodesy, Military University of Technology, 00-908 Warsaw, PolandDepartment of Imagery Intelligence, Faculty of Civil Engineering and Geodesy, Military University of Technology, 00-908 Warsaw, PolandDynamic technological progress has contributed to the development of systems imaging of the Earth’s surface as well as data mining methods. One such example is super-resolution (SR) techniques that allow for the improvement of the spatial resolution of satellite imagery on the basis of a low-resolution image (LR) and an algorithm using deep neural networks. The limitation of these solutions is the input size parameter, which defines the image size that is adopted by a given neural network. Unfortunately, the value of this parameter is often much smaller than the size of the images obtained by Earth Observation satellites. In this article, we presented a new methodology for improving the resolution of an entire satellite image, using a window function. In addition, we conducted research to improve the resolution of satellite images acquired with the World View 2 satellite using the ESRGAN network, we determined the number of buffer pixels that will make it possible to obtain the best image quality. The best reconstruction of the entire satellite imagery using generative neural networks was obtained using a Triangular window (for 10% coverage). The Hann-Poisson window worked best when more overlap between images was used.https://www.mdpi.com/2072-4292/14/24/6285remote sensingsatellitesneural network applicationimage processingimage resolution |
| spellingShingle | Kinga Karwowska Damian Wierzbicki Improving Spatial Resolution of Satellite Imagery Using Generative Adversarial Networks and Window Functions Remote Sensing remote sensing satellites neural network application image processing image resolution |
| title | Improving Spatial Resolution of Satellite Imagery Using Generative Adversarial Networks and Window Functions |
| title_full | Improving Spatial Resolution of Satellite Imagery Using Generative Adversarial Networks and Window Functions |
| title_fullStr | Improving Spatial Resolution of Satellite Imagery Using Generative Adversarial Networks and Window Functions |
| title_full_unstemmed | Improving Spatial Resolution of Satellite Imagery Using Generative Adversarial Networks and Window Functions |
| title_short | Improving Spatial Resolution of Satellite Imagery Using Generative Adversarial Networks and Window Functions |
| title_sort | improving spatial resolution of satellite imagery using generative adversarial networks and window functions |
| topic | remote sensing satellites neural network application image processing image resolution |
| url | https://www.mdpi.com/2072-4292/14/24/6285 |
| work_keys_str_mv | AT kingakarwowska improvingspatialresolutionofsatelliteimageryusinggenerativeadversarialnetworksandwindowfunctions AT damianwierzbicki improvingspatialresolutionofsatelliteimageryusinggenerativeadversarialnetworksandwindowfunctions |