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1 Ergebnisse
1
Optimal sample size and composition for crop classification..:
Schulthess, Urs
;
Rodrigues, Francelino
;
Taymans, Matthieu
...
Schulthess, U., Rodrigues, F., Taymans, M., Bellemans, N., Bontemps, S., Ortiz-Monasterio, I., Gérard, B. and Defourny, P. 2023. Optimal sample size and composition for crop classification with sen2-agri's random forest classifier. Remote Sensing 15(3):608. https://hdl.handle.net/10883/22489. , 2023
Link:
https://hdl.handle.net/10568/128426
RT Journal T1
Optimal sample size and composition for crop classification with Sen2-Agri's random forest classifier
UL https://suche.suub.uni-bremen.de/peid=base-ftcgiar:oai:cgspace.cgiar.org:10568_128426&Exemplar=1&LAN=DE A1 Schulthess, Urs A1 Rodrigues, Francelino A1 Taymans, Matthieu A1 Bellemans, Nicolas A1 Bontemps, Sophie A1 Ortiz Monasterio, Jose Iván A1 Gerard, Bruno G A1 Defourny, Pierre PB MDPI YR 2023 K1 crops K1 forests K1 machine learning K1 agriculture K1 remote sensing JF Schulthess, U., Rodrigues, F., Taymans, M., Bellemans, N., Bontemps, S., Ortiz-Monasterio, I., Gérard, B. and Defourny, P. 2023. Optimal sample size and composition for crop classification with sen2-agri's random forest classifier. Remote Sensing 15(3):608. https://hdl.handle.net/10883/22489 LK http://dx.doi.org/https://hdl.handle.net/10568/128426 DO https://hdl.handle.net/10568/128426 SF ELIB - SuUB Bremen
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