@InProceedings{SilvaJrBaca:2011:ApDiMé,
               author = "Silva Junior, Carlos Antonio da and Bacani, Vitor Matheus",
          affiliation = "{Universidade Estadual de Mato Grosso do Sul - UEMS} and 
                         {Universidade Estadual de Mato Grosso do Sul - UEMS}",
                title = "Aplica{\c{c}}{\~a}o de diferentes m{\'e}todos de 
                         classifica{\c{c}}{\~a}o supervisionada de imagem Landsat- 5/TM 
                         na identifica{\c{c}}{\~a}o de cana-de-a{\c{c}}{\'u}car",
            booktitle = "Anais...",
                 year = "2011",
               editor = "Epiphanio, Jos{\'e} Carlos Neves and Galv{\~a}o, L{\^e}nio 
                         Soares",
                pages = "85--92",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 15. (SBSR).",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "remote sensing, Bhattacharya, Maxver-ICM, Saccharum spp., 
                         accuracy, sensoriamento remoto, Bhattacharya, Maxver-ICM, 
                         Saccharum spp., exatid{\~a}o.",
             abstract = "The sugar-cane, from the family species Saccharum officinarum is 
                         grown in tropical climates, especially in areas where the seasons 
                         are well defined (dry winter and rainy summer). This agriculture 
                         is of great importance for the country's economy, Brazil is the 
                         world's largest producer of that crop. However, sugar-cane 
                         cultivation has favorable characteristics for identification in 
                         satellite images because it is a semi-perennial crop, grown in 
                         large areas. The objective of this work was to evaluate the 
                         performance of supervised classifiers for identifying the culture 
                         of sugar-cane using satellite images of Landsat-5 sensor Thematic 
                         Mapper (TM). The study area is located northwest from the city of 
                         Maracaj{\'u}-MS, Brazil. We propose a suitable method of 
                         classification and image processing to map where there is the 
                         cultivation of sugar-cane. Treatments were made to restore the 
                         image with spatial resolution of 15 meters and radiometric 
                         correction+NDVI. In the rankings, we used the Maxver-ICM algorithm 
                         and Bhattacharya. The different pre-processing and classifiers 
                         applied were subjected to statistical validation using parameters 
                         Kappa and overall accuracy. The results indicated a significant 
                         potential for supervised classifiers in the identification of 
                         sugar-cane. It was concluded that it is possible to obtain 
                         accuracies qualified as very good when used the Maximum 
                         Likelihood-ICM classifier in both methods of treatment.",
  conference-location = "Curitiba",
      conference-year = "30 abr. - 5 maio 2011",
                 isbn = "{978-85-17-00056-0 (Internet)} and {978-85-17-00057-7 (DVD)}",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "3ERPFQRTRW/3A3UGGE",
                  url = "http://urlib.net/ibi/3ERPFQRTRW/3A3UGGE",
           targetfile = "p0317.pdf",
                 type = "Agricultura",
        urlaccessdate = "15 jun. 2024"
}