@InProceedings{MarettoKorCasFonSan:2015:SpAtSe,
               author = "Maretto, Raian V. and Korting, Thales S. and Castejon, Emiliano F. 
                         and Fonseca, Leila M. G. and Santos, Rafael",
          affiliation = "Funda{\c{c}}{\~a}o de Ci{\^e}ncias, Aplica{\c{c}}{\~o}es e 
                         Tecnologias Espaciais (FUNCATE) and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Spectral attributes selection based on data mining for remote 
                         sensing image classification",
            booktitle = "Anais...",
                 year = "2015",
               editor = "Fileto, Renato and Korting, Thales Sehn",
                pages = "155--161",
         organization = "Simp{\'o}sio Brasileiro de Geoinform{\'a}tica, 16. (GEOINFO)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "Remote sensing images are a rich source of information for 
                         studying large-scale geographic areas. The new satellite 
                         generations have producing huge amounts of data. Data mining 
                         techniques have been emerged last years as powerful tools to help 
                         in the analysis of these data. In the area of remote sensing image 
                         analysis, software like GeoDMA, eCognition, InterIMAGE, and others 
                         are available for end users. These software provides tools to 
                         extract several attributes of the images. These attributes are 
                         then used in image classification and analysis. When dealing with 
                         high resolution multispectral satellites, we have a large quantity 
                         of attributes. In many cases, the attributes are highly 
                         correlated, and consequently may not help to separate the classes 
                         of interest. Thus, this work shows the results of an approach to 
                         analyze the correlation of the attributes between several classes 
                         of interest, selecting those that will better distinguish them. In 
                         this way, it is possible to reduce the amount of data to be used 
                         during classification and analysis, consequently reducing the 
                         computational time for classification.",
  conference-location = "Campos do Jord{\~a}o",
      conference-year = "27 nov. a 02 dez. 2015",
                 issn = "2179-4820",
             language = "en",
                  ibi = "8JMKD3MGPDW34P/3KP362E",
                  url = "http://urlib.net/ibi/8JMKD3MGPDW34P/3KP362E",
           targetfile = "proceedings2015_p16.pdf",
        urlaccessdate = "2024, Apr. 28"
}