@InProceedings{BragaSantFrei:2017:InImRa,
               author = "Braga, Bruna Cristina and Sant'Anna, Sidnei Jo{\~a}o Siqueira and 
                         Freitas, Corina da Costa",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Integra{\c{c}}{\~a}o de imagens Radarsat-2 e Alos/Palsar para 
                         obten{\c{c}}{\~a}o de classifica{\c{c}}{\~o}es multifontes do 
                         uso e cobertura da terra",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "3315--3322",
         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 = "In this paper two SAR images (acquired by different frequency) are 
                         integrated using a methodology called multisource classification. 
                         The technique allows the generation of different classification 
                         scenarios (classification and reliability map). This different 
                         scenarios were combined in order to obtain better classification 
                         results by using the minimum function compounding the scenario 
                         multisource minimum. One RADARSAT-2 (C-band) and one ALOS/PALSAR 
                         (L-band) images were used in this study. These images were modeled 
                         by the complex Wishart or multi-look intensity pair distributions. 
                         From the fourteen generated multisource scenarios, ten showed 
                         improvement greater than 10% related to the corresponding 
                         individual ratings. It was noted that for these scenarios both 
                         images had been modeled by the same distribution and for four 
                         remaining cases each data were modeled by a specific distribution. 
                         Two multisource scenarios did not presented overall accuracy and 
                         kappa coefficient higher than the individual classification 
                         however they exhibited high values of accuracy for Intermediate 
                         Regeneration class. The results showed that our method is 
                         effective to improve the classification accuracy indexes when SAR 
                         images are multisource integrated.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "59862",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PSLSN9",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSLSN9",
           targetfile = "59862.pdf",
                 type = "Mudan{\c{c}}a de uso e cobertura da terra",
        urlaccessdate = "27 abr. 2024"
}