A generalized space-time OBIA classification scheme to map sugarcane areas at regional scale, using Landsat images time-series and the random forest algorithm

Ana Cláudia dos Santos Luciano,Michelle Cristina Araújo Picoli,Jansle Vieira Rocha,Daniel Garbellini Duft,Rubens Augusto Camargo Lamparelli,Manoel Regis Lima Verde Leal,Guerric Le Maire

International Journal of Applied Earth Observation and Geoinformation（2019）

Cited 31|Views25

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Abstract

•Operational spatially generalized sugarcane classifier is crucial at regional scale.•Space and time generalization were tested for three approaches in SP State, Brazil.•Multi-site calibration from Landsat imagery performs better for mapping large areas.•Produced maps have similar precision than existing governamental statistics.

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Key words

Classifier extension,Data mining,Machine learning,Sugarcane mapping

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