@InProceedings{PortesScJuFeCaGl:2011:AvPoCl,
               author = "Portes, Raquel de Castro and Scudeller, Alice Azevedo and Jusksch, 
                         Ivo and Fernandes Filho, Elp{\'{\i}}dio In{\'a}cio and Cardoso, 
                         Irene Maria and Gleriani, Jos{\'e} Marinaldo",
          affiliation = "{Universidade Federal de Vi{\c{c}}osa – UFV} and {Universidade 
                         Federal de Vi{\c{c}}osa – UFV} and {Universidade Federal de 
                         Vi{\c{c}}osa – UFV} and {Universidade Federal de Vi{\c{c}}osa – 
                         UFV} and {Universidade Federal de Vi{\c{c}}osa – UFV} and 
                         {Universidade Federal de Vi{\c{c}}osa – UFV}",
                title = "Avalia{\c{c}}{\~a}o do potencial de classificadores 
                         autom{\'a}ticos para mapeamento de uso e cobertura do solo sob 
                         manejo agroecol{\'o}gico",
            booktitle = "Anais...",
                 year = "2011",
               editor = "Epiphanio, Jos{\'e} Carlos Neves and Galv{\~a}o, L{\^e}nio 
                         Soares",
                pages = "576--583",
         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, Maximum Likelihood, Artificial Neural Netwoks, 
                         SAFs, IKONOS, sensoriamento remoto, M{\'a}xima 
                         verossimilhan{\c{c}}a, redes neurais artificiais e 
                         Bhattacharya.",
             abstract = "The production areas based in agroecology systems are being 
                         implemented in Brazil and present on small farms intercropping 
                         different species of plants and making the diverse agricultural 
                         landscape. The classification of land cover and soil of these 
                         areas requires the use of images with high spatial resolution for 
                         detailed mapping and to identify the best method to rank areas 
                         with heterogeneous patterns of use. This study aimed to evaluate 
                         the potential of automatic classifiers for mapping land cover and 
                         soil under agro-ecological management in the S{\~a}o Joaquim 
                         River basin in Araponga, MG - Brazil. In the methodology were 
                         performed field expeditions to collect the training samples and 
                         validation using GPS. In the laboratory, supervised 
                         classifications were performed on IKONOS image by the algorithms 
                         of Maximum Likelihood and Artificial Neural Networks 
                         (Backpropagation Error) and regions (Bhattacharya). Among the 
                         classifiers evaluated in this experiment, the classification by 
                         regions had the best result, with Kappa of 0.76. The ratings by 
                         Maximum Likelihood and Artificial Neural Networks were 
                         respectively 0.48 and Kappa 0.51. This demonstrates the great 
                         potential that the supervised classification by segmentation have 
                         on classifying areas with many classes of land cover and soil and 
                         heterogeneous intra-class patterns. Thus, the findings of this 
                         study besides being useful for future planning in the watershed, 
                         will serve as universal knowledge to use classification and land 
                         cover in other areas with agro-ecological management.",
  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/3A645NL",
                  url = "http://urlib.net/ibi/3ERPFQRTRW/3A645NL",
           targetfile = "p1357.pdf",
                 type = "Processamento de Imagens",
        urlaccessdate = "04 maio 2024"
}