@InProceedings{DemarchiSartZimb:2011:EsCaSu,
               author = "Demarchi, Julio Cesar and Sartori, Anderson Antonio da 
                         Concei{\c{c}}{\~a}o and Zimback, C{\'e}lia Regina Lopes",
          affiliation = "{Universidade Estadual Paulista “J{\'u}lio de Mesquita Filho” – 
                         UNESP} and {Universidade Estadual Paulista “J{\'u}lio de Mesquita 
                         Filho” – UNESP} and {Universidade Estadual Paulista “J{\'u}lio de 
                         Mesquita Filho” – UNESP}",
                title = "M{\'e}todos de classifica{\c{c}}{\~a}o de imagens orbitais para 
                         o mapeamento do uso do solo: estudo de caso na Sub-Bacia do 
                         C{\'o}rrego das Tr{\^e}s Barras",
            booktitle = "Anais...",
                 year = "2011",
               editor = "Epiphanio, Jos{\'e} Carlos Neves and Galv{\~a}o, L{\^e}nio 
                         Soares",
                pages = "2644--2651",
         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 = "satellite images classification, land use maps, CBERS-2 images, 
                         classifica{\c{c}}{\~a}o de imagens de satellite, mapas de uso do 
                         solo, imagens CBERS-2.",
             abstract = "The knowledge of the land use and occupation has an essential 
                         importance for the agricultural, regional and environmental 
                         planning. The use of Remote Sensing tools has increased a lot 
                         nowadays, allowing the land use maps creation through many 
                         techniques, as satellite images classification. Under this 
                         context, this work aims to compare different methods of image 
                         classification using CBERS-2 images, bands 2, 3 and 4, for mapping 
                         the land use of Tr{\^e}s Barras stream sub-basin, situated in the 
                         city of Santa Cruz do Rio Pardo, S{\~a}o Paulo State. The image 
                         classification methods used were: Cluster broad (CB), Cluster Fine 
                         (CF), minimum distance, maximum likelihood with equal prior 
                         probability for each signature (MAXLIKE/IP), maximum likelihood 
                         with prior probabilities specified for each signature 
                         (MAXLIKE/EP), parallelepiped and image segmentation. The 
                         classifications accuracy was calculated through Kappa index and 
                         global accuracy. The maps produced and the accuracy indexes 
                         analysis show that the MAXLIKE/EP classification was the most 
                         efficient method used for the goal proposed, and the 
                         parallelepiped method presented the worst accuracy, while the 
                         others classifiers presented intermediated qualities, each one 
                         with its advantages and disadvantages. Some thematic classes 
                         showed confusion among them, specially {"}annual crops{"} and 
                         {"}sugarcane{"}, because of the similarity in their spectral 
                         response, and small representativeness classes were overestimated 
                         in great number of the classification methods used.",
  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/3A3TB4S",
                  url = "http://urlib.net/ibi/3ERPFQRTRW/3A3TB4S",
           targetfile = "p0678.pdf",
                 type = "Uso e Cobertura da Terra",
        urlaccessdate = "15 jun. 2024"
}