Normalization as a Key Enabler for Transferable Machine Learning in Multi-Temporal Cross-Dataset Satellite Imagery: Evidence in Cloud Detection
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Date
2025
Authors
Полякова, Любов
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Abstract
In this thesis, we explore the use of normalization and standardization to improve the transferability of deep learning models for cloud detection from multi-temporal satellite imagery. Specifically, we evaluate whether applying normalization techniques during preprocessing can reduce the necessity of model fine-tuning when encountering temporally shifted and externally sourced satellite images.
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Keywords
machine learning, neural networks, semantic segmentation, imagery, bachelor`s thesis