@InProceedings{AndradeJrAraúSant:2016:MuClRe,
               author = "Andrade Junior, Edemir Ferreira de and Ara{\'u}jo, Arnaldo de 
                         Albuquerque and Santos, Jefersson Alex dos",
          affiliation = "{Universidade Federal de Minas Gerais} and {Universidade Federal 
                         de Minas Gerais} and {Universidade Federal de Minas Gerais}",
                title = "Multimodal classification of remote sensing images",
            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 = "multimodal classification, remote sensing, data fusion.",
             abstract = "Remote Sensing Images (RSIs) have been used as a major source of 
                         data, particularly with respect to the creation of thematic maps. 
                         This process is usually modeled as a supervised classification 
                         problem where the system needs to learn the patterns of interest 
                         provided by the user and assign a class to the rest of the image 
                         regions. Associated with the nature of RSIs, there are several 
                         challenges that can be highlighted: (1) they are georeferenced 
                         images, i.e., a geographic coordinate is associated with each 
                         pixel; (2) the data commonly captures specific frequencies across 
                         the electromagnetic spectrum instead of the visible spectrum, 
                         which requires the development of specific algorithms to describe 
                         patterns; (3) the detail level of each data may vary, resulting in 
                         images with different spatial and pixel resolution, but covering 
                         the same area; (4) due to the high pixel resolution images, 
                         efficient processing algorithms are desirable. Thus, it is very 
                         common to have images obtained from different sensors, which could 
                         improve the quality of thematic maps generated. However, this 
                         requires the creation of techniques to properly encode and combine 
                         the different properties of the images. Therefore, this M.Sc. 
                         dissertation proposes two techniques for classification of regions 
                         in RSIs that manages to encode features extracted from different 
                         sources of data, spectral and spatial domains. The major objective 
                         is the development of approaches able to exploit the diversity of 
                         these different types of features to improve the accuracy in the 
                         creation of thematic maps.",
  conference-location = "S{\~a}o Jos{\'e} dos Campos, SP, Brazil",
      conference-year = "4-7 Oct. 2016",
             language = "en",
                  ibi = "8JMKD3MGPAW/3MGQ5S5",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3MGQ5S5",
           targetfile = "WTD_CameraReady_EdemirFerreira.pdf",
        urlaccessdate = "2024, Apr. 27"
}