Перегляд за Автор "Lavreniuk, Alla"

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  • Ескіз
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    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, Nataliia
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    Features’ Selection for Forest State Classification Using Machine Learning on Satellite Data
    (Assembled by Conference Management Services, Inc., 2024) Salii, Yevhenii; Kuzin, Volodymyr; Lavreniuk, Alla; Kussul, Nataliia; Shelestov, Andrii
    This paper discusses the use of advanced computer vision and artificial intelligence techniques for analysing remote sensing data, specifically focusing on the semantic segmentation of forest areas. The goal is to identify forest damage caused by insect pests using multispectral images from Sentinel-2 satellites. The proposed approach involves using genetic algorithms to automatically select informative features based on vegetation indices. A new fitness function is introduced to assess the quality of the selected feature sets. The neural network is then trained and tested using real data. The results of the study show the effectiveness of proposed approach and highlight its advantages over traditional methods. The developed technique allowed to obtain highly informative set of features with minimized redundancy within huge feature space with moderate amount of computation.
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    Generative Adversarial Networks for the Satellite Data Super Resolution Based on the Transformers with Attention
    (2023) Lavreniuk, Mykola; Shumilo, Leonid; Lavreniuk, Alla
    In 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.
  • Ескіз
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    The Land Degradation Estimation Remote Sensing Methods Using RUE-adjusted NDVI
    (IEEE, 2021) Shelestov, Andrii; Shumilo, Leonid; Bilokonska, Yuliia; Lavreniuk, Alla
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    Використання супутникових продуктів для аналізу змін територій природно-заповідного фонду України
    (Проблем и керування та інформатики, 2022) Yailymov, Bohdan; Yailymova, Hanna; Shelestov, Andrii; Lavreniuk, Alla