Sirvi Autor "Tamkivi, Karl Hendrik" 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.