@Article{VieiraFoReAtAgMe:2012:ObBaIm,
               author = "Vieira, Matheus Alves and Formaggio, Antonio Roberto and 
                         Renn{\'o}, Camilo Daleles and Atzberger, Clement and Aguiar, 
                         Daniel Alves de and Mello, Marcio Pupin",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and University of Natural Resources 
                         and Life Sciences (BOKU), Institute of Surveying, Remote Sensing 
                         and Land Information (IVFL), Peter Jordan Strasse 82, Vienna, 
                         1190, Austria and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Object Based Image Analysis and Data Mining applied to a remotely 
                         sensed Landsat time-series to map sugarcane over large areas",
              journal = "Remote Sensing of Environment",
                 year = "2012",
               volume = "123",
                pages = "553--562",
                month = "Aug.",
             keywords = "Object Based Image Analysis (OBIA), Data Mining (DM), Sugarcane, 
                         Time-series imagery, Landsat, Image segmentation.",
             abstract = "The aim of this research was to develop a methodology for 
                         contributing in the automation of sugarcane mapping over large 
                         areas, with time-series of remotely sensed imagery. To this end, 
                         two major techniques were combined: Object Based Image Analysis 
                         (OBIA) and Data Mining (DM). OBIA was used to represent the 
                         knowledge needed to map sugarcane, whereas DM was applied to 
                         generate the knowledge model. To derive the image objects, the 
                         segmentation algorithm implemented in Definiens Developer® was 
                         used. The data mining algorithm used was J48, which generates 
                         decision trees (DT) from a previously prepared training set. The 
                         study area comprises three municipalities located in the northwest 
                         of S{\~a}o Paulo state, all of which are good representatives of 
                         the agricultural conditions in the Southern and Southeastern 
                         regions of Brazil. A time series of Landsat TM and ETM+ images was 
                         acquired in order to represent the wide range of pattern variation 
                         along the sugarcane crop cycle. After training, the DT was applied 
                         to the Landsat time series, thus generating the desired thematic 
                         map with sugarcane ready to harvest. Classification accuracy was 
                         calculated over a set of 500 points not previously used during the 
                         training stage. Using error matrix analysis and Kappa statistics, 
                         tests for statistical significance were derived. The statistics 
                         indicated that the classification achieved an overall accuracy of 
                         94% and a Kappa coefficient of 0.87. Results show that the 
                         combination of OBIA and DM techniques is very efficient and 
                         promising for the sugarcane classification process.",
                  doi = "10.1016/j.rse.2012.04.011",
                  url = "http://dx.doi.org/10.1016/j.rse.2012.04.011",
                 issn = "0034-4257",
                label = "lattes: 1958394372634693 5 VieiraFoReAtAgMe:2012:ObBaIm",
             language = "en",
           targetfile = "Vieira_MA.pdf",
        urlaccessdate = "30 abr. 2024"
}