Sirvi Autor "Komisarenko, Viacheslav, juhendaja" järgi

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Enhancing Mowing Event Detection by Mitigating Semi-Transparent Cloud Anomalies in Optical Satellite Image Time Series
(Tartu Ülikool, 2025) Tamkivi, Karl Hendrik; Komisarenko, Viacheslav, juhendaja; Shtym, Tetiana, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
Cloud contamination in optical satellite imagery poses a major challenge in remote sensing, particularly for applications that rely on high-quality temporal data and where infrequent satellite revisits make each observation valuable. Traditional pixel-based cloud detection methods often struggle with semi-transparent clouds, which can be difficult to distinguish from atmospheric effects or land surface variations. This thesis introduces a time series-based approach for detecting semi-transparent cloud contamination in Sentinel-2 optical time series for Danish grasslands. A supervised anomaly detection model was trained to estimate cloud anomaly probabilities, which were then integrated into an existing mowing event detection framework through loss function modifications, custom network layers, or post-processing techniques. The results demonstrate that incorporating cloud anomaly probabilities improved model reliability by reducing false positives caused by cloud contamination. The findings highlight the potential of uncertainty-aware learning for enhancing event detection and other remote sensing applications affected by optical data contamination.
Snow cover detection in Estonia from SAR images using machine learning methods
(Tartu Ülikool, 2020) Äkke, Kerstin; Komisarenko, Viacheslav, juhendaja; Gruno, Anti, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
Usability of optical satellite data for monitoring snow cover can be limited in regions with frequent high cloud coverage. Synthetic aperture radar (SAR) could theoretically be used to monitor snow regardless of clouds or lack of illumination. There are several factors that complicate the task in Estonia such as dense vegetation and quickly changing snow conditions. So far most studies on using SAR for snow detection have been done in mountainous regions and over short time periods. The aim of this study was to test applicability of a method that combines most common features for snow detection extracted from SAR images in a machine learning model. This method had shown good transferability in mountain regions, however the modelling results on Estonian data were unsatisfactory. Analysis of features derived from SAR images revealed poor separability of snow free and snow covered classes. This suggest the main issue is with the feature extraction methods rather than machine learning. Possibly the processing chain could be optimized for Estonia and other regions with flat topography and predominantly dense vegetation. This thesis did not result in a usable model, but could serve as a basis for further studies.