Normalization as a Key Enabler for Transferable Machine Learning in Multi-Temporal Cross-Dataset Satellite Imagery: Evidence in Cloud Detection
| dc.contributor.advisor | Кундік, Кирило | en_US |
| dc.contributor.author | Полякова, Любов | en_US |
| dc.date.accessioned | 2025-09-05T13:00:15Z | |
| dc.date.available | 2025-09-05T13:00:15Z | |
| dc.date.issued | 2025 | |
| dc.description.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. | en_US |
| dc.identifier.uri | https://ekmair.ukma.edu.ua/handle/123456789/36482 | |
| dc.language.iso | en_US | en_US |
| dc.status | first published | en_US |
| dc.subject | machine learning | en_US |
| dc.subject | neural networks | en_US |
| dc.subject | semantic segmentation | en_US |
| dc.subject | imagery | en_US |
| dc.subject | bachelor`s thesis | en_US |
| dc.title | Normalization as a Key Enabler for Transferable Machine Learning in Multi-Temporal Cross-Dataset Satellite Imagery: Evidence in Cloud Detection | en_US |
| dc.type | Other | en_US |
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