Résultats pour : "échantillonnage aléatoire"

Roadside collection of training data for cropland mapping is viable when environmental and management gradients are surveyed. Waldner François, Bellemans Nicolas, Hochman Zvi, Newby Terrence, de Abelleyra Diego, Verón Santiago R., Bartalev Sergey, Lavreniuk Mykola, Kussul Nataliia, Le Maire Guerric, Simoes Margareth, Skakun Sergii, Defourny Pierre. 2019. International Journal of Applied Earth Observation and Geoinformation, 80 : 82-93.

A generalized space-time OBIA classification scheme to map sugarcane areas at regional scale, using Landsat images time-series and the random forest algorithm. dos Santos Luciano Ana Cláudia, Picoli Michelle Cristina Araújo, Vieira Rocha Jansle, Garbellini Duft Daniel, Camargo Lamparelli Rubens Augusto, Lima Verde Leal Manoel Regis, Le Maire Guerric. 2019. International Journal of Applied Earth Observation and Geoinformation, 80 : 127-136.