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@InProceedings{BragaFreiSant:2015:MuClBa,
               author = "Braga, Bruna Cristina and Freitas, Corina da Costa and Sant'Anna, 
                         Sidnei Jo{\~a}o Siqueira",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Multisource classification based on uncertainty maps",
            booktitle = "Proceedings...",
                 year = "2015",
         organization = "International Geoscience and Remote Sensing Symposium (IGARSS)",
            publisher = "IEEE",
             keywords = "Multisource classification, statistical region-based 
                         classification, stochastic distances.",
             abstract = "A new methodology to perform classification of multisource data is 
                         proposed in this work. The technique takes into account the 
                         classification results (a classified image and an uncertainty map) 
                         derived from each data source in order to generate a new 
                         classified image. It is based on a region-based classifier which 
                         employs stochastic distances, statistical tests and reliability of 
                         the classification to produce a final classification. Images 
                         acquired from two distinct and independent data sources (optical 
                         and microwave sensors) were used to validate the proposed 
                         methodology. One LANDSAT5/TM image and one ALOS/PALSAR image from 
                         a region on Brazilian Amazon were classified, firstly individually 
                         and then using the proposed classification technique. The results 
                         showed that the individual classification results can be improved 
                         by the use of our multisource classification technique. It can be 
                         concluded that this new method to combine information derived from 
                         different data sources is strongly promising.",
  conference-location = "Milan, Italy",
      conference-year = "23-31 July",
                  doi = "10.1109/IGARSS.2015.7326508",
                  url = "http://dx.doi.org/10.1109/IGARSS.2015.7326508",
                 isbn = "9781479979295",
             language = "en",
        urlaccessdate = "01 dez. 2020"
}


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