Browsing by Author "Kashtan, Vita"

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    Comparative Analysis of Classification Methods for High-Resolution Optical Satellite Images
    (Khmelnytskyi National University, Khmelnytskyi, 2024) Hnatushenko, Volodymyr V.; Kashtan, Vita; Chumychov, Denys; Nikulin, Serhii
    ENG: High-resolution satellite image classification is used in various applications, such as urban planning, environmental monitoring, disaster management, and agricultural assessment. Traditional classification methods are ineffective due to the complex characteristics of high-resolution multichannel images: the presence of shadows, complex textures, and overlapping objects. This necessitates selecting an efficient classification method for further thematic data analysis. In this study, a comprehensive assessment of the accuracy of the most well-known classification methods (parallelepiped, minimum distance, Mahalanobis distance, maximum similarity, spectral angle map, spectral information difference, binary coding, neural network, decision tree, random forest, support vector machine, K-nearest neighbour, and spectral correlation map) is performed. This study comprehensively evaluates various classification algorithms applied to high-resolution satellite imagery, focusing on their accuracy and suitability for different use cases. To ensure the robustness of the evaluation, high-quality WorldView-3 satellite imagery, known for its exceptional spatial and spectral resolution, was utilized as the dataset. To assess the performance of these methods, error matrices were generated for each algorithm, providing detailed insights into their classification accuracy. The average values along the main diagonal of these matrices, representing the proportion of correctly classified pixels, served as a key metric for evaluating overall effectiveness. Results indicate that advanced machine learning approaches, such as neural networks and support vector machines, consistently outperform traditional techniques, achieving superior accuracy across various classes. Despite their high average accuracy, a deeper analysis revealed that only some algorithms are universally optimal. For instance, some methods, such as random forests or spectral angle mappers, exhibited strength in classifying specific features like vegetation or urban structures but performed less effectively for others. This underscores the importance of tailoring algorithm selection to the specific objectives of individual classification tasks and the unique characteristics of the target datasets. This study can be used to select the most effective method of classifying the earth's surface, depending on the tasks of further thematic analysis of high-resolution satellite imagery. Furthermore, it highlights the potential of integrating machine learning-based approaches to enhance the accuracy and reliability of classification outcomes, ultimately contributing to more practical applications.
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    Detection of Forest Fire Consequences on Satellite Images Using a Neural Network
    (German Society for Photogrammetry, Remote Sensing and Geoinformation, 2023) Hnatushenko, Viktoriia V.; Hnatushenko, Volodymyr V.; Kashtan, Vita; Heipke, Christian
    ENG: The objective of this research is the detection of burnt forest areas from Sentinel-2 imagery. The proposed algorithm uses an approach based on convolutional neural networks (CNN). The functionality of the created system allows solving the task, starting from the moment of receiving the input data, image preprocessing and ending with the export of a hot-spot fire polygonal file describing the area that was burnt. These results are compared to methods based on the dNBR and a variant of BAIS2 called dBAIS2, which are generated from measurements in the near and middle IR channels of the Sentinel images. The proposed algorithm was tested on Sentinel satellite images acquired from June to September 2021for the Tizi Ouzou region, Algeria. We found it to have an overall accuracy of 97%, outperforming the results obtained from dNBR and dBAIS2 by large margins.
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    Information Technology for Detecting Forest Fire Contours Using Optical Satellite Data
    (Український державний університет науки і технологій, ННІ ≪Інститут промислових та бізнес технологій≫, ІВК ≪Системні технології≫, Дніпро, 2023) Kashtan, Vita; Hnatushenko, Volodymyr V.
    ENG: The number of forest fires has increased significantly over the past ten years. It indicates that forest area estimates fires are a very urgent task today. The use of satellite-based data simplifies the process of assessing forest fires. The aim is to develop an information technology for automated forest fire contours detection on digital optical satellite datas in conditions of non-stationarity and uncertainty based on convolutional neural networks. The most popular tools for forest fire analysis are considered. This work proposed using hotspots to identify all fire and smoke pixels for automated forest fire contour detection. It made it possible to obtain contour polygons of the corresponding areas with various attributes: position, size, etc. The results are tested on Sentinel 2 satellite images of the Бvila region. The proposed method has an overall accuracy of 94.3% for the selection of forest fires.