@InProceedings{CostaFeiMotPakCos:2009:MéClMu,
               author = "Costa, Gilson Alexandre Ostwald Pedro da and Feitosa, Raul Queiroz 
                         and Mota, Guilherme L{\'u}cio Abelha and Pakzad, Kian and Costa, 
                         Maria Clara de Oliveira",
          affiliation = "{Pontif{\'{\i}}cia Universidade Cat{\'o}lica do Rio de Janeiro 
                         (PUC-Rio)} and {Pontif{\'{\i}}cia Universidade Cat{\'o}lica do 
                         Rio de Janeiro (PUC-Rio)} and {Universidade do Estado do Rio de 
                         Janeiro (UERJ)} and University of Hannover, Institute of 
                         Photogrammetry and GeoInformation and {Pontif{\'{\i}}cia 
                         Universidade Cat{\'o}lica do Rio de Janeiro (PUC-Rio)}",
                title = "Um M{\'e}todo de Classifica{\c{c}}{\~a}o Multitemporal em 
                         Cascata de Imagens de Sensoriamento Remoto",
            booktitle = "Anais...",
                 year = "2009",
               editor = "Epiphanio, Jos{\'e} Carlos Neves and Galv{\~a}o, L{\^e}nio 
                         Soares",
                pages = "1291--1298",
         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 = "cascade classifier, multitemporal analysis, fuzzy markov chain, 
                         classificador em cascata, an{\'a}lise multitemporal, cadeias de 
                         markov nebulosas.",
             abstract = "This paper introduces a new cascade multitemporal classification 
                         method based on Fuzzy Markov Chains. The method does not require 
                         knowledge of the true class at the earlier date; it uses instead 
                         the attributes of the image object being classified at both the 
                         later and the earlier date. The method combines the fuzzy, non 
                         temporal, classification of a geographical region in two points in 
                         time to provide a single unified result. A transformation law 
                         based on class transition possibilities projects the earlier 
                         classification to the later date before combining both results. 
                         Performance analysis was done with a sequence of three LANDSAT 
                         images from the central region of Brazil. The results showed that 
                         the performance gain depends highly on the accuracy of the 
                         monotemporal classifier at the earlier date. While the 
                         monotemporal approach attained an average class accuracy of 
                         approximately 55%, the multitemporal scheme achieved from 65% 
                         accuracy up to 95%, when knowledge of the true class at the 
                         earlier date was used.",
  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@80/2008/11.16.17.59",
                  url = "http://urlib.net/ibi/dpi.inpe.br/sbsr@80/2008/11.16.17.59",
           targetfile = "1291-1298.pdf",
                 type = "An{\'a}lise e Aplica{\c{c}}{\~a}o de Imagens Multitemporais",
        urlaccessdate = "15 jun. 2024"
}