@InProceedings{ErbertHaer:2003:EsSoTé,
               author = "Erbert, Mauro and Haertel, Vitor",
          affiliation = "{Universidade Luterana do Brasil (ULBRA)} and {Universidade 
                         Federal do Rio Grande do Sul (UFRGS). Centro Estadual de Pesquisas 
                         em Sensoriamento Remoto e Meteorologia (CEPSRM).}",
                title = "Estudo sobre t{\'e}cnicas de regulariza{\c{c}}{\~a}o da matriz 
                         covari{\^a}ncia no processo de classifica{\c{c}}{\~a}o de dados 
                         em alta dimensionalidade",
            booktitle = "Anais...",
                 year = "2003",
               editor = "Epiphanio, Jos{\'e} Carlos Neves and Fonseca, Leila Maria 
                         Garcia",
                pages = "1061--1068",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 11. (SBSR).",
            publisher = "INPE",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "sensoriamento remoto, alta dimensionalidade, an{\'a}lise 
                         discriminante regularizada, m{\'e}todo supervisionado, 
                         reconhecimento de padr{\~o}es.",
             abstract = "High dimensional image data that are now becoming available, offer 
                         new possibilities in image classification, specially when dealing 
                         with classes that present very similar spectral response. High 
                         dimensional image data poses, however, the problem of obtaining 
                         accurate estimates of the parameters required by statistical 
                         classifiers. This problem is caused by the small number of 
                         training samples usually available in real world conditions. 
                         Different approaches have been proposed in the literature aiming 
                         to mitigate this problem. One approach involves the techniques of 
                         regularization of the covariance matrix. This study investigates 
                         the applications of one regularization technique to high 
                         dimensional image data. Tests are performed using AVIRIS data, 
                         covering agricultural fields, and the results are presented and 
                         discussed.",
  conference-location = "Belo Horizonte",
      conference-year = "5-10 abr. 2003",
                 isbn = "85-17-00017-X",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais",
                  ibi = "ltid.inpe.br/sbsr/2002/10.02.09.46",
                  url = "http://urlib.net/ibi/ltid.inpe.br/sbsr/2002/10.02.09.46",
           targetfile = "10_011.pdf",
                 type = "Imageamento Hiperespectral / Hyperspectral Imaging",
        urlaccessdate = "27 abr. 2024"
}