@InProceedings{DutraRennReisGamb:2023:GeMeSi,
               author = "Dutra, Luciano Vieira and Renn{\'o}, Camilo Daleles and Reis, 
                         Mariane Souza and Gamba, Paolo",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {University of Pavia}",
                title = "A generative method for simultaneous classification of remote 
                         sensing time series data using an ensemble of decision tree 
                         classifiers",
            booktitle = "Anais...",
                 year = "2023",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de and Sanches, Ieda DelArco",
                pages = "e155459",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 20. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "multi-temporal classification, Landsat, land cover trajectory.",
             abstract = "Time series of remote sensing data has become an essential input 
                         for land use and land cover (LULC) studies. The current 
                         availability of multi-temporal data sets, from different sources 
                         and types, demands new classification approaches to explore their 
                         full capacity. In this study, we propose a non-parametric version 
                         of the Compound Maximum a posteriori classifier, based on an 
                         ensemble of Decision Tree Classifiers. This classifier was 
                         designed to avoid the classification of inconsistent class 
                         sequences in time. It was tested in a study area located in 
                         Itaituba, Par{\'a} state, Brazil, by the classifications of five 
                         Landsat images. In our case study, more than 25% of time series 
                         would be classified as invalid transitions. The use of the 
                         proposed approach substitutes these results with the most probable 
                         consistent class trajectory. Improvements in individual 
                         accuracies, when compared to post-classification comparison, have 
                         also been observed.",
  conference-location = "Florian{\'o}polis",
      conference-year = "02-05 abril 2023",
                 isbn = "978-65-89159-04-9",
             language = "en",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/495CK9L",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/495CK9L",
           targetfile = "155459.pdf",
                 type = "An{\'a}lise de s{\'e}ries temporais de imagens de 
                         sat{\'e}lite",
        urlaccessdate = "15 jun. 2024"
}