@InProceedings{FormaggioVieRenAguMel:2010:ObImAn,
               author = "Formaggio, A. R. and Vieira, M. A. and Renn{\'o}, C. D. and 
                         Aguiar, D. A. and Mello, M. P.",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Object-based image analysis and data mining for mapping sugarcane 
                         with landsat imagery in brazil",
            booktitle = "Proceedings...",
                 year = "2010",
               editor = "Coillie, E. A. Addink and F. M. B. Van",
         organization = "GEOBIA 2010. Geographic Object-Based Image Analysis.",
            publisher = "ISPRS Working Groups",
             keywords = "Sugarcane mapping, Artificial Intelligence, Object-based Image 
                         Analysis, Data Mining, Landsat images.",
             abstract = "Mapping of sugarcane planted area is an important information for 
                         decision making, mainly when the search for alternatives to 
                         mitigate greenhouse gas emissions has indicated the use of 
                         biofuels as a viable option. Thus, the aim of this research was to 
                         develop a methodology in order to automate the sugarcane mapping 
                         task when remote sensing data are used. Thus the integration of 
                         two major approaches of Artificial Intelligence, Object-Based 
                         Image Analysis (OBIA) and Data Mining (DM), were tested in a study 
                         area located in S{\~a}o Paulo state, which is well representative 
                         of the agriculture of large regions of Brazil and other countries. 
                         OBIA was used to emulate the interpreter knowledge in the process 
                         of sugarcane mapping, and DM techniques were employed for 
                         automatic generation of knowledge model. A time series of four 
                         Landsat images was acquired in order to represent the wide 
                         variability of the patterns during sugarcane crop season. 
                         Definiens Developer® multiresolution segmentation algorithm 
                         produced the objects and properly trained decision tree applied to 
                         the Landsat data for the generation of the thematic map with 
                         sugarcane as the main class of interest. An overall accuracy of 
                         94% (Kappa = 0,87) was obtained, showing that OBIA and DM are very 
                         efficient and promising in the direction of automating the 
                         sugarcane classification process with Landsat multitemporal time 
                         series.",
  conference-location = "Ghent, Belgium",
      conference-year = "29 June - 2 July",
                 issn = "1682-1777",
             language = "en",
           targetfile = "Formaggio_Full paper.pdf",
               volume = "38-4/C7",
        urlaccessdate = "30 abr. 2024"
}