@InProceedings{SantosGlerVell:2017:MoLiMi,
               author = "Santos, Jo{\~a}o Fl{\'a}vio Costa dos and Gleriani, Jos{\'e} 
                         Marinaldo and Velloso, Sidney Geraldo Silveira",
                title = "Modelo linear de mistura espectral com dados de sensores de 
                         diferentes resolu{\c{c}}{\~o}es espaciais do CBERS4",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "5202--5208",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 18. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "Spectral mixture models have been used for a wide variety of 
                         applications and the linear model has been widely used. In order 
                         to improve the performance of the models, new approaches have been 
                         used as nonlinear (logistic) functions in MLP (Multi-Layer 
                         Perceptron) networks, nonlinear modeling expressing the 
                         interaction of the photon with neighboring covers, or use of 
                         endmenbers from spectral libraries. With the launch of the 
                         CBERS-4, and the presence of PAN-MS and MUX sensors with three 
                         channels with the same spectral resolution, but with different 
                         spatial resolutions, there are new research possibilities. We have 
                         the opportunity to verify if the use of purer endmenbers, where 
                         data acquired with the same illumination/observation geometry and 
                         atmospheric conditions, can improve the fit of the model, or if 
                         the spatial resolution interferes with the fit of the model. It 
                         was verified that the use of endmembers collected in PAN-MS images 
                         used in the MUX image did not improve the fit of the model, 
                         probably by forcing the modeling of non-existent mixed pixel 
                         values in 20m spatial resolution, linear mixture model generated 
                         with PAN-MS data resulted in a better fit.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "59454",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PSM4EN",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSM4EN",
           targetfile = "59454.pdf",
                 type = "CBERS",
        urlaccessdate = "27 abr. 2024"
}