@InProceedings{GoltzPinhRudoFons:2009:ClOrOb,
               author = "Goltz, Elizabeth and Pinho, Carolina Moutinho Duque de and 
                         Rudorff, Bernardo Friedrich Theodor and Fonseca, Leila Maria 
                         Garcia",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais/SP} and {Instituto 
                         Nacional de Pesquisas Espaciais/SP} and {Instituto Nacional de 
                         Pesquisas Espaciais/SP} and {Instituto Nacional de Pesquisas 
                         Espaciais/SP}",
                title = "Classifica{\c{c}}{\~a}o orientada a objeto na 
                         identifica{\c{c}}{\~a}o de {\'a}reas de reforma de 
                         cana-de-a{\c{c}}{\'u}car",
            booktitle = "Anais...",
                 year = "2009",
               editor = "Epiphanio, Jos{\'e} Carlos Neves and Galv{\~a}o, L{\^e}nio 
                         Soares",
                pages = "199--206",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 14. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "multitemporal, agriculture, hierarchical net, mutitemporal, 
                         agricultura, rede hier{\'a}rquica.",
             abstract = "Remote sensing images and geoprocessing techniques have been used 
                         yearly, since the crop year 2003/04, to map and estimate sugarcane 
                         area in S{\~a}o Paulo State, Brazil. The image classification is 
                         carried out visually by an experienced team of interpreters 
                         resulting in high quality thematic maps. However, this work is 
                         time-consuming and often quite tedious due to the large area 
                         covered by sugarcane which was 4.9 million ha in 2008 for S{\~a}o 
                         Paulo State. The annual update of the sugarcane maps consists 
                         basically of two steps: 1) to add new areas that are coming from 
                         the sugarcane cultivation expansion; and 2) to subtract sugarcane 
                         fields that are being renewed and consequently skipped for harvest 
                         during one crop year. In order find alternatives to automate the 
                         visual classification the goal of this work was to develop an 
                         oriented-based classification methodology using hierarchical net 
                         to identify sugarcane areas that are being renewed. Landsat-5 TM 
                         images acquired at favorable dates were selected to identify the 
                         renewed sugarcane fields using the Definies Developer software to 
                         perform segmentation, sample selection, hierarchical net 
                         construction (selection and customized features) and final 
                         classification. The result showed that the multiresolution 
                         segmentation was able to recognize the renewed sugarcane areas 
                         with images acquired at three dates. Comparing the visual 
                         interpreted map to the hierarchical net map it was verified that 
                         almost all renewed areas were correctly classified.",
  conference-location = "Natal",
      conference-year = "25-30 abr. 2009",
                 isbn = "978-85-17-00044-7",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "dpi.inpe.br/sbsr@80/2008/11.17.15.40",
                  url = "http://urlib.net/ibi/dpi.inpe.br/sbsr@80/2008/11.17.15.40",
           targetfile = "199-206.pdf",
                 type = "Agricultura",
        urlaccessdate = "18 jun. 2024"
}