Browsing by Author "Kashtan, Vita Yu."

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    Aircraft Detection with Deep Neural Networks and Contour-Based Methods
    (National University "Zaporizhzhia Polytechnic", Zaporizhzhia, 2024) Radionov, Y. D.; Kashtan, Vita Yu.; Hnatushenko, Volodymyr V.; Kazymyrenko, O. V.
    ENG: Context. Aircraft detection is an essential task in the military, as fast and accurate aircraft identification allows for timely response to potential threats, effective airspace control, and national security. The use of deep neural networks improves the accuracy of aircraft recognition, which is essential for modern defense and airspace monitoring needs. Objective. The work aims to improve the accuracy of aircraft recognition in high-resolution optical satellite imagery by using deep neural networks and a method of sequential boundary traversal to detect object contours. Method. A method for improving the accuracy of aircraft detection on high-resolution satellite images is proposed. The first stage involves collecting data from the HRPlanesv2 dataset containing high-precision satellite images with aircraft annotations. The second stage consists of preprocessing the images using a sequential boundary detection method to detect object contours. In the third stage, training data is created by integrating the obtained contours with the original HRPlanesv2 images. In the fourth stage, the YOLOv8m object detection model is trained separately on the original HRPlanesv2 dataset and the dataset with the applied preprocessing, which allows the evaluation of the impact of additional processed features on the model performance. Results. Software that implements the proposed method was developed. Testing was conducted on the primary data before preprocessing and the data after its application. The results confirmed the superiority of the proposed method over classical approaches, providing higher aircraft recognition accuracy. The mAP50 index reached 0.994, and the mAP50-95 index reached 0.864, 1% and 4.8% higher than the standard approach. Conclusions. The experiments confirm the effectiveness of the proposed method of aircraft detection using deep neural networks and the process of sequential boundary traversal to detect object contours. The results indicate this approach’s high accuracy and efficiency, which allows us to recommend it for use in research related to aircraft recognition in high-resolution images. Further research could focus on improving image preprocessing methods and developing object recognition technologies in machine learning.
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    Automated Building Damage Detection on Digital Imagery Using Machine Learning
    (Dnipro University of Technology, Ukraine, 2023) Kashtan, Vita Yu.; Hnatushenko, Volodymyr V.
    ENG: Purpose. To develop an automated method based on machine learning for accurate detection of features of a damaged building on digital imagery. Methodology. This article presents an approach that employs a combination of unsupervised machine learning techniques, specifically Principal Component Analysis (PCA), K-means clustering, and Density-Based Spatial Clustering of Applications with Noise (DBSCAN), to identify building damage resulting from military conflicts. The PCA method is utilized to identify principal vectors representing the directions of maximum variance in the data. Subsequently, the K-means method is applied to cluster the feature vector space, with the predefined number of clusters reflecting the number of principal vectors. Each cluster represents a group of similar blocks of image differences, which helps to identify significant features associated with fractures. Finally, the DBSCAN method is employed to identify areas where points with similar characteristics are located. Subsequently, a binary fracture mask is generated, with pixels exceeding the threshold being identified as fractures. Findings. The introduced methodology attains an accuracy rate of 98.13 %, surpassing the performance of conventional methods such as DBSCAN, PCA, and K-means. Furthermore, the method exhibits a recall of 82.38 %, signifying its ability to effectively detect a substantial proportion of positive examples. Precision of 58.54 % underscores the methodology’s capability to minimize false positives. The F1 Score of 70.90 % demonstrates a well-balanced performance between precision and recall. Originality. DBSCAN, PCA and K-means methods have been further developed in the context of automated detection of building destruction in aerospace images. This allows us to significantly increase the accuracy and efficiency of monitoring territories, including those affected by the consequences of military aggression. Practical value. The results obtained can be used to improve automated monitoring systems for urban development and can also serve as the basis for the development of effective strategies for the restoration and reconstruction of damaged infrastructure.
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    Computer Modeling of Territory Flooding in the Event of an Emergency at Seredniodniprovska Hydroelectric Power Plant
    (Dnipro University of Technology, Ukraine, 2022) Ivanov, D. V.; Hnatushenko, Volodymyr V.; Kashtan, Vita Yu.; Garkusha, I. M.
    ENG: Purpose. Computer modeling of territory flooding in the event of an emergency at Seredniodniprovska Hydroelectric Power Plant (HPP). Methodology. The computer model of possible territory flooding at Seredniodniprovska HPP is developed using simulation modeling methods and geometric and hydrological approaches and considers initial boundary conditions of the water-engineering system. Calculations of the wave break height and the half-divided cross-sectional area of the river bed were made and a three-dimensional model of the territory flooding was built using the Python language and ArcGIS Desktop software. Findings. The data for each creation of the hydraulic node, namely the depth and width of the flooded territory, were calculated. This allowed analyzing the macro level considering the triangulation model of the surface. The wave break parameters and flaps (intersections) were taken into account in case of a dam break at a hydroelectric power plant or a rise in the water level. A mathematical model, and a 3D model were developed, and a forecast of the flood zone due to an emergency was made using satellite survey data. Originality. The mathematical method received further development for calculating flood territories in the event of an emergency at Seredniodniprovska Hydroelectric Power Plant, taking into account the parameters of the breakthrough wave and the calculation of cross-sections for the cases when a hydroelectric dam breaks or the water level rises; the method uses one-dimensional and two-dimensional systems of Saint-Venant equations, and geometric and hydrological approaches. A three-dimensional model of the territory flooding is developed to predict possible consequences. Practical value. The obtained results can be used to model the flooding of the territory located near dangerous hydro-technical objects, such as dams, dikes as well as to forecast flooded territories during the construction of drainage and protective structures.
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    Computer Technology for Satellite Imagery Processing in Nature Management Problem Solving Using Lineament Analysis
    (Український державний університет науки і технологій, ІВК «Системні технології», Дніпро, 2023) Kashtan, Vita Yu.; Nikulin, Serhii; Hnatushenko, Volodymyr V.; Sergieieva, Kateryna; Korobko, Olha; Ivanov, Denys
    ENG: This study focuses on analyzing the techniques used to highlight lineaments in images. Various mask algorithms, including the widely used optimal Kenny detector, were employed to identify brightness boundaries. Additionally, several quality criteria were developed to assess the accuracy of boundary selection. Based on the results of the analysis, conclusions were drawn regarding the effectiveness of different pre-processing methods for space images, along with recommendations to streamline data processing and analysis and enhance the reliability of results. Our analysis of image processing methods for selecting brightness boundaries revealed that the most effective approach involves applying filters to the source images to increase the number of selected boundaries while maintaining their integrity and length.
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    Intelligent Sentinel Satellite Image Processing Technology for Land Cover Mapping
    (Dnipro University of Technology, Dnipro, 2024) Kashtan, Vita Yu.; Hnatushenko, Volodymyr V.; Laktionov, I. S.; Diachenko, H. H.
    ENG: Purpose. This article proposes to develop an intelligent Sentinel satellite image processing technology for land cover mapping using convolutional neural networks. The result will be an image with improved spatial resolution. Methodology. The paper presents a technology using a combination of biquadratic interpolation, histogram alignment, PCA transform, as well as a parallel residual architecture of convolutional neural networks. The technology increases the information content of Sentinel-2 optical images by combining 10 and 20-meter resolution data, resulting in primary 20-meter images with improved spatial resolution. Findings. The root mean square error (RMSE = 3.64) indicates a high accuracy in reproducing the spectral properties of the images. The correlation coefficient (CC = 0.997) confirms a high linear relationship between the estimated and observed images. The low value of Spectral Angle Mapper (SAM = 0.52) with the high Universal Image Quality Index (UIQI = 0.999) indicates high quality and structural similarity between the synthesized and reference images. These results confirm the proposed technology’s effectiveness in enhancing the spatial resolution of Sentinel satellite images. Originality. Traditional pansharpening methods of multispectral images developed for satellite images with panchromatic channels cannot be directly applied to Sentinel multispectral data, because these images do not contain a panchromatic channel. In addition, atmospheric conditions and the presence of clouds affect the quality of optical images, complicating their further thematic processing. The proposed technology, using biquadratic interpolation, histogram alignment, convolutional neural networks, and PCA transformation, removes clouds and enhances the spatial resolution of the primary 20-meter optical satellite image channels of Sentinel-2. This technology reduces color distortion and increases the detail of digital optical images, which allows for more accurate analysis of the state of the earth’s surface. Practical value. The results obtained can be used to improve the methods for processing Sentinel satellite images, which provide high spatial resolution and accurate preservation of spectral characteristics. It provides the foundation for the development of new geographic information systems for land cover monitoring.
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    Machine Learning for Automatic Extraction of Water Bodies Using Sentinel-2 Imagery
    (National University "Zaporizhzhia Polytechnic", Zaporizhzhia, 2024) Kashtan, Vita Yu.; Hnatushenko, Volodymyr V.
    ENG: Context. Given the aggravation of environmental and water problems, there is a need to improve automated methods for extracting and monitoring water bodies in urban ecosystems. The problem of efficient and automated extraction of water bodies is becoming relevant given the large amount of data obtained from satellite systems. The object of study is water bodies that are automatically extracted from Sentinel-2 optical satellite images using machine learning methods. Objective. The goal of the work is to improve the efficiency of the process of extracting the boundaries of water bodies on digital optical satellite images by using machine learning methods. Method. The paper proposes an automated information technology for delineating the boundaries of water bodies on Sentinel-2 digital optical satellite images. The process includes eight stages, starting with data download and using topographic maps to obtain basic information about the study area. Then, the process involved data pre-processing, which included calibrating the images, removing atmospheric noise, and enhancing contrast. Next, the EfficientNet-B0 architecture is applied to identify water features, facilitating optimal network width scaling, depth, and image resolution. ResNet blocks compress and expand channels. It allows for optimal connectivity of large-scale and multi-channel links across layers. After that, the Regional Proposal Network defines regions of interest (ROI), and ROI alignment ensures data homogeneity. The Fully connected layer helps in segmenting the regions, and the Fully connected network creates binary masks for accurate identification of water bodies. The final step of the method is to analyze spatial and temporal changes in the images to identify differences, changes, and trends that may indicate specific phenomena or events. This approach allows automating and accurately identifying water features on satellite images using machine learning. Results. The implementation of the proposed technology is development through Python software development. An assessment of the technology’s accuracy, conducted through a comparative analysis with existing methods, such as water indices and K-means, confirms a high level of accuracy in the period from 2017 to 2023 (up to 98%). The Kappa coefficient, which considers the degree of consistency between the actual and predicted classification, confirms the stability and reliability of our approach, reaching a value of 0.96. Conclusions. The experiments confirm the effectiveness of the proposed automated information technology and allow us to recommend it for use in studies of changes in coastal areas, decision-making in the field of coastal resource management, and land use. Prospects for further research may include new methods that seasonal changes and provide robustness in the selection and mapping of water surfaces.