@InProceedings{SilvaFonsKort:2017:MuApLa,
               author = "Silva, Alexsandro C{\^a}ndido de Oliveira and Fonseca, Leila 
                         Maria Garcia and Korting, Thales Sehn",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "A multitemporal approach for land use mapping using Bayesian 
                         Networks",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "5928--5935",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 18. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "It is possible to trace the phenological profile of targets on the 
                         Earths surface through multitemporal remote sensing data. 
                         Different features can be computed from multitemporal data to 
                         classify land use classes. In this context, this paper presents a 
                         new method to map the land use based on the probabilistic analysis 
                         of multitemporal features using Bayesian Networks. Elementary 
                         statistical measures were computed from NDVI/MODIS and EVI/MODIS 
                         time series of pasture, sugarcane, annual agriculture and other 
                         uses classes for 2012/2013 and 2013/2014 crop years in southern 
                         Goi{\'a}s state, Brazil. The models output is composed by layers 
                         representing the occurrence probability of each class over the 
                         study area. A thematic map was built from output layers and the 
                         classification was evaluated by the Monte Carlo simulation. In our 
                         preliminary results, we obtained classification accuracy values 
                         within Kappa index range from 0.51 to 0.63. Annual agriculture and 
                         other land use classes were more easily distinguished and more 
                         confusion happened between pasture and sugarcane classes. Although 
                         the accuracy values were not high, the proposed model presented a 
                         potential for land use classification and it can be improved.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "59341",
             language = "en",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PSMBUE",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSMBUE",
           targetfile = "59341.pdf",
                 type = "Classifica{\c{c}}{\~a}o e minera{\c{c}}{\~a}o de dados",
        urlaccessdate = "27 abr. 2024"
}