Learning UAV-Based Above-Ground Biomass Regression Models in Sparse Training Data Environments
Résumé
This study aims at recovering above-ground biomass information from ultra-high resolution UAV RGB-NIR orthophotos. We focus on a realistic scenario where a limited number of training samples for a landscape with heterogeneous herbaceous vegetation is given. Consequently, we explore different machine learning methods explicitly addressing the limitations of small training samples and compare their predictions quantitatively and qualitatively. Our results show that random forest models perform similarly well to deep learning models. While simpler machine learning models may, therefore, still be preferable, our study also points the way to promising architectures and regularisation techniques for deep learning approaches. Beyond vegetation cover, accurate regression of other variables, including vegetation height, volume and biomass remains a difficult task regardless of the model choice.
Mots clés
vegetation biomass regression random forest deep learning semi-supervised learning transfer learning UAV
vegetation biomass regression
random forest
deep learning
semi-supervised learning
transfer learning
UAV
vegetation biomass regression random forest deep learning semi-supervised learning transfer learning UAV
Domaines
Sciences de l'environnement| Origine | Fichiers produits par l'(les) auteur(s) |
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