Learning from hyperspectral remote sensing data for machine learning algorithms in earth science

Christoph Jörges, Sandra Dotzler2024-04-082024-04-082024978-3-88579-738-82944-7682https://dl.gi.de/handle/20.500.12116/43892Machine 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).enhyperspectral imagingplasticulturemachine learningland use classificationremote sensingLearning from hyperspectral remote sensing data for machine learning algorithms in earth scienceText/Conference Paper10.18420/giljt2024_611617-5468