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@Article{LucianoPiRoFrSaLeMa:2018:GeSpCl,
               author = "Luciano, Ana Cl{\'a}udia dos Santos and Picoli, Michelle Cristina 
                         Ara{\'u}jo and Rocha, Jansle Vieira and Franco, Henrique Coutinho 
                         Junqueira and Sanches, Guilherme Martineli and Leal, Manoel Regis 
                         Lima Verde and Maire, Guerric le",
          affiliation = "{Centro Nacional de Pesquisa em Energia e Materiais (CNPEM)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Universidade Estadual de Campinas (UNICAMP)} and {Centro Nacional 
                         de Pesquisa em Energia e Materiais (CNPEM)} and {Centro Nacional 
                         de Pesquisa em Energia e Materiais (CNPEM)} and {Centro Nacional 
                         de Pesquisa em Energia e Materiais (CNPEM)} and CIRAD, UMR",
                title = "Generalized space-time classifiers for monitoring sugarcane areas 
                         in Brazil",
              journal = "Remote Sensing of Environment",
                 year = "2018",
               volume = "215",
                pages = "438--451",
                month = "Sept.",
             keywords = "Object-based classification, Classifier extension, Random Forest, 
                         Sugarcane, Map update, Machine learning.",
             abstract = "Spatially and temporally accurate information on crop areas is a 
                         prerequisite for monitoring the multiannual dynamics of crop 
                         production. Satellite images have proven their high potential for 
                         mapping crop areas at large scales, even at the crop-species 
                         level, when a classifier is calibrated on the same image with 
                         reference data corresponding to the same period. For operational 
                         monitoring purposes, however, it is critical to develop 
                         generalized classification methodologies applicable to large 
                         scales and different years. Generalized classifiers were presented 
                         in this study as follows: a) simple cross-year calibration and 
                         application (M1); b) multiyear calibrations (M2); and c) map 
                         updating through change detection with multiyear calibrations 
                         (M3). These three methods were developed in a classical frame of 
                         object-based classifications for a time series of Landsat images 
                         with the Random Forest machine learning algorithm. Therein, we 
                         tested these methods for sugarcane classification in Sao Paulo 
                         state, Brazil, as sugarcane is an economically important crop that 
                         has developed substantially in the past decades. Eight years of 
                         sugarcane reference maps were used to calibrate and validate the 
                         classifiers at four different sites. The cross-year application of 
                         M1 provided a low average accuracy Dice coefficient (DC) of 0.84, 
                         while it was, on average, 0.94 for the classical same-year 
                         calibration. When the classifier was trained on a multiyear 
                         dataset (M2), the accuracies achieved average values of 0.91 in 
                         independent years. The map updating method M3 showed promising 
                         results but was not able to reach the accuracy of visual 
                         interpretation methods for detecting annual sugarcane land use 
                         change. The multiyear classifier M2 was applied to four 
                         contrasting sites and provided reliable results for new sites and 
                         years for sugarcane classification. Calibration of the machine 
                         learning algorithm on a multiyear dataset of standardized and 
                         gap-filled satellite images and reference data proved to give an 
                         accurate and space-time generalized classifier, reducing the time, 
                         cost and resources for mapping sugarcane areas at large scales.",
                  doi = "10.1016/j.rse.2018.06.017",
                  url = "http://dx.doi.org/10.1016/j.rse.2018.06.017",
                 issn = "0034-4257",
             language = "en",
           targetfile = "luciano-generalized.pdf",
        urlaccessdate = "25 nov. 2020"
}


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