@InProceedings{SantanaMachSant:2016:CoDeSu,
               author = "Santana, Tiago Moreira H{\"u}bner Can{\c{c}}ado and Machado, 
                         Alexei Manso C{\^o}rrea and dos Santos, Jefersson Alex",
          affiliation = "Computer Science Department, UFMG and Electrical Engineering 
                         Department, PUC Minas and Computer Science Department, UFMG",
                title = "Contextual Description of Superpixels for Aerial Urban Scenes 
                         Classification",
            booktitle = "Proceedings...",
                 year = "2016",
               editor = "Aliaga, Daniel G. and Davis, Larry S. and Farias, Ricardo C. and 
                         Fernandes, Leandro A. F. and Gibson, Stuart J. and Giraldi, Gilson 
                         A. and Gois, Jo{\~a}o Paulo and Maciel, Anderson and Menotti, 
                         David and Miranda, Paulo A. V. and Musse, Soraia and Namikawa, 
                         Laercio and Pamplona, Mauricio and Papa, Jo{\~a}o Paulo and 
                         Santos, Jefersson dos and Schwartz, William Robson and Thomaz, 
                         Carlos E.",
         organization = "Conference on Graphics, Patterns and Images, 29. (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "contextual descriptor, land cover, thematic maps, remote 
                         sensing.",
             abstract = "Remote Sensing Images are one of the main sources of information 
                         about the earth surface. They are widely used to generate thematic 
                         maps that show the land cover. This process is traditionally done 
                         by using supervised classifiers which learn patterns extracted 
                         from few image pixels annotated by the user and then assign a 
                         label to the remaining pixels. However, due to the increasing 
                         spatial resolution of the images, pixelwise classification is not 
                         suitable anymore, even when combined with context. Moreover, 
                         traditional techniques used to aggregate context are unsuitable in 
                         the scenario of thematic maps generation since they depend on a 
                         previous labeling of image pixels/segments and, thus, are 
                         computationally inefficient and require a large amount of training 
                         data. Therefore, the objective of this work is to develop a 
                         description for superpixels which is able to encode their visual 
                         cues and local context without labeling them in order to generate 
                         more accurate land cover thematic maps.",
  conference-location = "S{\~a}o Jos{\'e} dos Campos, SP, Brazil",
      conference-year = "4-7 Oct. 2016",
             language = "en",
                  ibi = "8JMKD3MGPAW/3M9374B",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3M9374B",
           targetfile = "Paper11_Camera_Ready.pdf",
        urlaccessdate = "2024, May 03"
}