Перегляд за Автор "Lavreniuk, Mykola"
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Документ Відкритий доступ Advanced Method of Land Cover Classification Based on High Spatial Resolution Data and Convolutional Neural Network(Anhalt University of Applied Sciences, 2022) Shelestov, Andrii; Yailymov, Bohdan; Yailymova, Hanna; Shumilo, Leonid; Lavreniuk, Mykola; Lavreniuk, Alla; Sylantyev, Sergiy; Kussul, NataliiaДокумент Відкритий доступ Automatic Deforestation Detection based on the Deep Learning in Ukraine(IEEE, 2021) Shumilo, Leonid; Lavreniuk, Mykola; Kussul, Nataliia; Shevchuk, BellaДокумент Відкритий доступ Biophysical Impact of Sunflower Crop Rotation on Agricultural Fields(Sustainability, 2022) Kussul, Nataliia; Deininger, Klaus; Shumilo, Leonid; Lavreniuk, Mykola; Ayalew Ali, Daniel; Nivievskyi, OlegДокумент Відкритий доступ Generative Adversarial Networks for the Satellite Data Super Resolution Based on the Transformers with Attention(2023) Lavreniuk, Mykola; Shumilo, Leonid; Lavreniuk, AllaIn 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.Документ Відкритий доступ Is Soil Bonitet an Adequate Indicator for Agricultural Land Appraisal in Ukraine?(Sustainability, 2021) Shumilo, Leonid; Lavreniuk, Mykola; Skakun, Sergii; Kussul, NataliiaДокумент Відкритий доступ Losses Assessment for Winter Crops Based on Satellite Data and Fuzzy Logic(IEEE, 2021) Bilokonska, Yuliia; Yailymova, Hanna; Yailymov, Bohdan; Shelestov, Andrii; Shumilo, Leonid; Lavreniuk, MykolaДокумент Відкритий доступ Super resolution approach for the satellite data based on the generative adversarial networks(IEEE, 2022) Lavreniuk, Mykola; Kussul, Nataliia; Shelestov, Andrii; Lavrenyuk, Alla; Shumilo, LeonidДокумент Відкритий доступ U-Net Model for Logging Detection Based on the Sentinel-1 and Sentinel-2 Data(IEEE, 2021) Shumilo, Leonid; Kussul, Nataliia; Lavreniuk, Mykola