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Learning from hyperspectral remote sensing data for machine learning algorithms in earth science
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Datum
2024
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Gesellschaft für Informatik e.V.
Zusammenfassung
Machine Learning in Earth sciences heavily depends on sufficient training data for proper generalization. Since in-situ ground truth data is rarely available and cost-intensive to obtain, this study presents a new approach of deriving training data from hyperspectral remote sensing satellites by physical spectral signatures to use them for data-driven models with operationally available multispectral data. Examples include monitoring of crop rotation, winter greening, soil organic matter, and detection of plastic covered greenhouses (PCGs).