@InProceedings{SchultzBeViEbFoLuAt:2014:DaMiOb,
               author = "Schultz, Bruno and Bertani, Gabriel and Vieira, Matheus Alves and 
                         Eberhardt, Isaque Daniel Rocha and Formaggio, Antonio Roberto and 
                         Luiz, Alfredo Jos{\'e} Barreto and Atzberger, Clement",
          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 {Brazilian Agricultural Research Corporation 
                         (EMBRAPA)} and {University of Natural Resources and Life 
                         Sciences}",
                title = "Data Mining and Object Based Image Analysis applied to soybean 
                         areas classification through time-series TM/ETM+ images",
            booktitle = "Abstracts...",
                 year = "2014",
         organization = "Geographic Object-Based Image Analysis GEOBIA.",
                 note = "Setores de Atividade: Agricultura, Pecu{\'a}ria e Servi{\c{c}}os 
                         Relacionados.",
             keywords = "Multiresolution Segmentation, J48, Agricultural statistics, 
                         Soybean Cultures.",
             abstract = "This study was conducted in order to map soybean plantations 
                         through the use of temporal series of ETM + / Landsat-7, together 
                         with the approach Object Based Image Analysis (OBIA) and Data 
                         Mining (DM). This approach allowed using the knowledge about the 
                         characteristics of Soybean cycle in the classification process. 
                         The study area corresponds to three cities in the state of 
                         S{\~a}o Paulo, namely: Guara, Ipu{\~a} and San Joaquin Barra. To 
                         generate image objects was used the Multiresolution Segmentation 
                         algorithm, implemented on the E-cognition platform. The knowledge 
                         model was obtained from the J48 algorithm, which generated a 
                         decision tree and was implemented in the WEKA platform. The 
                         training set used to generate the decision tree corresponds to the 
                         areas identified in Soybean growth stages on the following dates: 
                         September and October (2000); February and March (2001). After 
                         obtaining the knowledge model a thematic map of soybean was 
                         generated through the Hierarchical Classification Algorithm in 
                         E-cognition platform. The map had overall accuracy and kappa 
                         coefficient equal to 98.69% and 0.97, respectively. The results 
                         show that the classification of soybeans areas, performed through 
                         the application of the approach DM + OBIA in temporal series of TM 
                         / ETM +, can be considered efficient, and it is a promising 
                         alternative to the process of agricultural monitoring.",
  conference-location = "Thessaloniki",
      conference-year = "2014",
                label = "lattes: 2084527326378812 4 SchultzBeViEbFoLuAt:2014:DaMiOb",
             language = "en",
           targetfile = "schultz_data mining.pdf",
                  url = "http://geobia2014.web.auth.gr/geobia14/",
        urlaccessdate = "08 maio 2024"
}