Super resolution approach for the satellite data based on the generative adversarial networks

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dc.contributor.authorLavreniuk, Mykola
dc.contributor.authorKussul, Nataliia
dc.contributor.authorShelestov, Andrii
dc.contributor.authorLavrenyuk, Alla
dc.contributor.authorShumilo, Leonid
dc.date.accessioned2022-10-19T14:14:38Z
dc.date.available2022-10-19T14:14:38Z
dc.date.issued2022
dc.description.abstractenIn the past few years, medium and high-resolution data became freely available for downloading. It provides great opportunity for researchers not to select between solving the task with high-resolution data on small territory or on global scale, but with low-resolution satellite images. Due to high spectral and spatial resolution of the data, Sentinel-1 and Sentinel-2 are very popular sources of information. Nevertheless, in practice if we would like to receive final product in 10 m resolution we should use bands with 10 m resolution. Sentinel-2 has four such bands, but also has other bands, especially red-edge 20 m resolution bands that are useful for vegetation analysis and often are omitted due to lower resolution. Thus, in this study we propose methodology for enhancing resolution (super-resolution) of the existing low-resolution images to higher resolution images. The main idea is to use advanced methods of deep learning - Generative Adversarial Networks (GAN) and train it to increase the resolution for the satellite images. Experimental results for the Sentinel-2 data showed that this approach is efficient and could be used for creating high resolution products.uk
dc.format.pagerangeP. 1095-1098uk
dc.identifier.citationSuper Resolution Approach for the Satellite Data Based on the Generative Adversarial Networks / Mykola Lavreniuk, Nataliia Kussul, Andrii Shelestov, Alla Lavrenyuk, Leonid Shumilo // IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, 17-22 July, 2022, Kuala Lumpur, Malaysia : Proceedings. - Kuala Lumpur: IEEE, 2022. - P. 1095-1098.uk
dc.identifier.doihttps://doi.org/10.1109/IGARSS46834.2022.9884460
dc.identifier.urihttps://ela.kpi.ua/handle/123456789/50430
dc.language.isoenuk
dc.publisherIEEEuk
dc.publisher.placeKuala Lumpuruk
dc.sourceIGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, 17-22 July, 2022, Kuala Lumpur, Malaysia : Proceedingsuk
dc.subjectdeep learninguk
dc.subjectGenerative Adversarial Networksuk
dc.subjectsuper-resolutionuk
dc.subjectSentinel-2uk
dc.titleSuper resolution approach for the satellite data based on the generative adversarial networksuk
dc.typeArticleuk

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