Generalized space-time classifiers for monitoring sugarcane areas in Brazil

Remote Sensing of Environment

Volumn 215, Issue , 2018, Pages 438-451

  Luciano, Ana Cláudia dos Santos ^a,b   Picoli, Michelle Cristina Araújo ^c   Rocha, Jansle Vieira ^b   Franco, Henrique Coutinho Junqueira ^a   Sanches, Guilherme Martineli ^a   Leal, Manoel Regis Lima Verde ^a,b   le Maire, Guerric ^a,b,d,e  

^a BRAZILIAN CENTER FOR RESEARCH IN ENERGY AND MATERIALS CNPEM   (Brazil)

^b UNIVERSITY OF CAMPINAS   (Brazil)

^c INSTITUTO NACIONAL DE PESQUISAS ESPACIAIS   (Brazil)

^d CIRAD   (France)

^e UNIV MONTPELLIER   (France)

Author keywords

Classifier extension; Machine learning; Map update; Object based classification; Random Forest; Sugarcane

Indexed keywords

ARTIFICIAL INTELLIGENCE; CALIBRATION; CROPS; CULTIVATION; DECISION TREES; LAND USE; LEARNING ALGORITHMS; LEARNING SYSTEMS; MAPPING; SUGAR CANE;

CHANGE DETECTION; CLASSIFICATION METHODOLOGIES; DICE COEFFICIENT; OBJECT-BASED CLASSIFICATIONS; OPERATIONAL MONITORING; RANDOM FORESTS; RELIABLE RESULTS; VISUAL INTERPRETATION;

CLASSIFICATION (OF INFORMATION);

ACCURACY ASSESSMENT; ALGORITHM; CROP PRODUCTION; DATA SET; DETECTION METHOD; IMAGE CLASSIFICATION; LAND USE CHANGE; LANDSAT; MACHINE LEARNING; MAPPING METHOD; METHODOLOGY; SATELLITE IMAGERY; SPATIOTEMPORAL ANALYSIS; SUGAR CANE; TIME SERIES ANALYSIS;

BRAZIL; SAO PAULO [BRAZIL];

EID: 85049343861     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2018.06.017     Document Type: Article

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