@InProceedings{MarettoKörCasFonSan:2016:SpAtSe,
               author = "Maretto, Raian and K{\"o}rting, Thales Sehn and Castejon, 
                         Emiliano Ferreira and Fonseca, Leila Maria Garcia and Santos, 
                         Rafael Duarte Coelho dos",
          affiliation = "{} 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",
                 year = "2016",
         organization = "Workshop de Computa{\c{c}}{\~a}o Aplicada, 16. (WORCAP)",
             abstract = "Remote sensing images are a rich source of information for 
                         studying large-scale geographic areas. The increased accessibility 
                         of the new generation high-spatial resolution multispectral 
                         sensors has improved the level of complexity required in the 
                         analysis techniques. In particular, many traditional per-pixel 
                         analysis may not be suitable to high-spatial resolution imagery, 
                         due to its high-frequency components and the horizontal layover 
                         caused by off-nadir look angles [Im et al. 2008]. Aiming to 
                         overcome this problem, in the last decades, several approaches and 
                         platforms have been developed with algorithms that consider 
                         contextual information and pixel region properties [K{\"o}rting 
                         et al. 2013; Syed et al. 2005; Walter 2004]. Current software can 
                         extract several statistical, spatial, color, texture or 
                         topological attributes. However, most of them often do not help to 
                         distinguish between the classes of interest, due to its high 
                         correlation. Thus, the attributes selection phase often relies on 
                         ad hoc decisions about what of them can better describe the 
                         classes. The huge number of attributes available makes a detailed 
                         exploratory time-consuming and dependent on expertise 
                         [K{\"o}rting et al. 2013]. Many works have proved that data 
                         mining techniques can be useful to this purpose [Dash and Liu 
                         1997; Kohavi and Kohavi 1997; Laliberte et al. 2012]. In this 
                         context, the main objective of this work is to analyze the 
                         correlation of the spectral attributes between a set of classes of 
                         interest, in order to verify what of them best distinguish these 
                         classes. A case study is presented over a small region of the city 
                         of S{\~a}o Jos{\'e} dos Campos, using a WorldView-2 image. It is 
                         important to emphasize that although this study is in a 
                         preliminary stage, the results are promising and reached 
                         improvements in the accuracy of the classification, even as a good 
                         reduction in the computational time.",
  conference-location = "S{\~a}o Jos{\'e} dos Campos, SP",
      conference-year = "25-26 out.",
             language = "en",
           targetfile = "maretto_spectral.pdf",
        urlaccessdate = "03 maio 2024"
}