@InProceedings{AndradeJrAraúSant:2016:BoApRe,
               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 = "A Boosting-based Approach for Remote Sensing Multimodal Image 
                         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 = "IEEE Computer Society´s Conference Publishing Services",
              address = "Los Alamitos",
             keywords = "Multimodal Classification, Remote Sensing, Data Fusion.",
             abstract = "Remote Sensing Images (RSI) 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 learning task 
                         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. 
                         Thus, it is common to have images obtained from different sensors, 
                         which could improve the quality of thematic maps. However, this 
                         requires the creation of techniques to properly encode and combine 
                         the different properties of the images. So, this paper proposes a 
                         boosting-based technique for classification of regions in RSI that 
                         manages to encode features extracted from different sources of 
                         data, spectral and spatial domains. The approach is evaluated in 
                         an urban and a coffee crop recognition scenarios, achieving 
                         statistically better results in comparison with the baselines in 
                         urban classification and better results at some baselines for the 
                         coffee crop recognition.",
  conference-location = "S{\~a}o Jos{\'e} dos Campos, SP, Brazil",
      conference-year = "4-7 Oct. 2016",
                  doi = "10.1109/SIBGRAPI.2016.064",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2016.064",
             language = "en",
                  ibi = "8JMKD3MGPAW/3M5KJ22",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3M5KJ22",
           targetfile = "106_Camera_Ready.pdf",
        urlaccessdate = "2024, Apr. 28"
}