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@InProceedings{BragaFreiSant:2015:ClMuIm,
               author = "Braga, Bruna Cristina and Freitas, Corina da Costa and Sant'Anna, 
                         Sidnei Jo{\~a}o Siqueira",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Classifica{\c{c}}{\~a}o multifontes de imagens de sensoriamento 
                         remoto baseada em mapas de incertezas",
            booktitle = "Anais...",
                 year = "2015",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "4474--4481",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 17. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "The image classification is one of the most remote sensing tools 
                         used for mapping the Earths surface. There are several sensors for 
                         image acquisition, for instance the optical and the microwave. 
                         These sensors can generate data carrying distinct information. The 
                         jointly use of information provided by different sources is a big 
                         challenge. Therefore, it is necessary to develop specific 
                         techniques for processing and analyzing images generated by these 
                         distinguished sensors. Taking into account the information 
                         gathered by different sensors, in this work a statistic way to mix 
                         two classification results is evaluated. The information from an 
                         optical and a microwave sensors are mixed in order to improve the 
                         overall accuracy of the individual land-use and land-cover maps. 
                         The data were classified by a region based classifier employing 
                         stochastic distances and its statistic hypothesis test. In this 
                         classification process, besides the classified image is also 
                         generated an uncertainty map. The uncertainty map (it indicates 
                         the classification reliability) is used to generate three 
                         different classification scenarios. The first and second scenarios 
                         are related to the optical and microwave image classifications, 
                         respectively. The third scenario is result of mixing the 
                         classifications information provided by the both sensors. The 
                         overall accuracy were statistically equals to the optical 
                         classification result. The use of information provided by this 
                         distinct sources showed a positive impact in the final 
                         classification results because it diminishes the uncertainty. On 
                         the other hands it is important to perform more studies related to 
                         multisource classification to better evaluate this methodology.",
  conference-location = "Jo{\~a}o Pessoa",
      conference-year = "25-29 abr. 2015",
                 isbn = "978-85-17-0076-8",
                label = "876",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3JM4CQS",
                  url = "http://urlib.net/rep/8JMKD3MGP6W34M/3JM4CQS",
           targetfile = "p0876.pdf",
                 type = "Processamento de imagens",
        urlaccessdate = "04 dez. 2020"
}


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