Generative Adversarial Networks for the Satellite Data Super Resolution Based on the Transformers with Attention

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dc.contributor.authorLavreniuk, Mykola
dc.contributor.authorShumilo, Leonid
dc.contributor.authorLavreniuk, Alla
dc.date.accessioned2023-11-08T08:27:41Z
dc.date.available2023-11-08T08:27:41Z
dc.date.issued2023
dc.description.abstractIn recent years, free access to high and medium resolution data has become available, providing researchers with the opportunity to work with low resolution satellite images on a global scale. Sentinel-1 and Sentinel-2 are popular sources of information due to their high spectral and spatial resolution. To obtain a final product with a resolution of 10 meters, we have to use bands with a resolution of 10 meters. Other satellite data with lower resolution, such as Landsat-8 and Landsat-9, can improve the results of land monitoring, but their harmonization requires a process known as super-resolution. In this study, we propose a method for improving the resolution of low-resolution images using advanced deep learning techniques called Generative Adversarial Networks (GANs). The state-of-the-art neural networks, namely transformers, with the combination of channel attention and self-attention blocks were employed at the base of the GANs. Our experiments showed that this approach can effectively increase the resolution of Landsat satellite images and could be used for creating high resolution products.uk
dc.format.pagerangePp.6294-6297uk
dc.identifier.citationLavreniuk, M. Generative Adversarial Networks for the Satellite Data Super Resolution Based on the Transformers with Attention / Mykola Lavreniuk, Leonid Shumilo, Alla Lavreniuk // In IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium. – 2023. – Pp. 6294-6297. – Bibliogr.: 25 ref.uk
dc.identifier.doihttps://doi.org/10.1109/IGARSS52108.2023.10281826
dc.identifier.urihttps://ela.kpi.ua/handle/123456789/62059
dc.language.isoenuk
dc.relation.ispartofProceedings of International Geoscience and Remote Sensing Symposium (IGARSS) 2023uk
dc.subjectdeep learninguk
dc.subjectGANuk
dc.subjecttransformersuk
dc.subjectattentionuk
dc.subjectsuper-resolutionuk
dc.subjectSentinel-2uk
dc.subjectLandsat-8/9uk
dc.titleGenerative Adversarial Networks for the Satellite Data Super Resolution Based on the Transformers with Attentionuk
dc.typeArticleuk

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