@InCollection{SantosSiFeQuCaSa:2017:ClMeAs,
               author = "Santos, Lorena Alves dos and Sim{\~o}es, Rolf Ezequiel de 
                         Oliveira and Ferreira, Karine Reis and Queiroz, Gilberto Ribeiro 
                         and Camara, Gilberto and Santos, Rafael Duarte Coelho dos",
                title = "Clustering methods to asses land cover samples of modis vegetation 
                         indexes time series",
            booktitle = "Computational Science and Its Applications – ICCSA 2017",
            publisher = "Springer",
                 year = "2017",
               editor = "Gervasi, Osvaldo and Murgante, Beniamino and Misra, Sanjay and 
                         Borruso, Giuseppe and Torre, Carmelo M. and Rocha, Ana Maria A. C. 
                         and Taniar, David and Apduhan, Bernady O. and Stankova, Elena and 
                         Cuzzocrea, Alfredo",
                pages = "662--673",
             keywords = "Time series clustering, MODIS vegetation indexes, Land cover 
                         change classification, Self-Organizing Map (SOM).",
             abstract = "MODIS vegetation indexes time series have been widely used to 
                         build land cover change maps on large scales. In this scope, to 
                         obtain good quality maps using supervised classification methods, 
                         it is crucial to select representative training samples of land 
                         cover change classes. In this paper, we evaluate two clustering 
                         methods, Hierarchical and Self-Organizing Map (SOM), to assess 
                         land cover samples of MODIS vegetation indexes time series. As we 
                         show, these techniques are suitable tools for assisting users to 
                         select representative land cover change samples from MODIS 
                         vegetation indexes time series. We present the accuracy of both 
                         methods for a case study in Ipiranga do Norte municipality in Mato 
                         Grosso state, Brazil.",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                 isbn = "978-331962406-8",
             language = "en",
          seriestitle = "Lecture Notes in Computer Science , 10409",
        urlaccessdate = "27 abr. 2024"
}