Monitoring Forest Health Using Hyperspectral Imagery: Does Feature Selection Improve the Performance of Machine-Learning Techniques?
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Externally hosted supplementary file 1
Doi: https://doi.org/10.5281/zenodo.2635403
Link: https://doi.org/10.5281/zenodo.2635403
Description: Research compendium
Schratz, P.; Muenchow, J.; Iturritxa, E.; Cortés, J.; Bischl, B.; Brenning, A. Monitoring Forest Health Using Hyperspectral Imagery: Does Feature Selection Improve the Performance of Machine-Learning Techniques? Remote Sens. 2021, 13, 4832. https://doi.org/10.3390/rs13234832
Schratz P, Muenchow J, Iturritxa E, Cortés J, Bischl B, Brenning A. Monitoring Forest Health Using Hyperspectral Imagery: Does Feature Selection Improve the Performance of Machine-Learning Techniques? Remote Sensing. 2021; 13(23):4832. https://doi.org/10.3390/rs13234832
Chicago/Turabian StyleSchratz, Patrick, Jannes Muenchow, Eugenia Iturritxa, José Cortés, Bernd Bischl, and Alexander Brenning. 2021. "Monitoring Forest Health Using Hyperspectral Imagery: Does Feature Selection Improve the Performance of Machine-Learning Techniques?" Remote Sensing 13, no. 23: 4832. https://doi.org/10.3390/rs13234832