author = "Asta, Ana Paula Dal and Amaral, Silvana and Monteiro, Ant{\^o}nio 
                         Miguel Vieira",
          affiliation = "{} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Sensoriamento remoto para a caracteriza{\c{c}}{\~a}o intraurbana 
                         de cidades Amaz{\^o}nicas: uma abordagem classificat{\'o}ria 
                         h{\'{\i}}brida para o caso da cidade de Santar{\'e}m (PA)",
            booktitle = "Anais...",
                 year = "2015",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "2661--2668",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 17. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "Urbanization is a key issue when studying scenarios for 
                         sustainable development for the Amazonian region. This paper 
                         presents the potential of integrate multiresolution remote sensing 
                         data to analyze the intra-urban space of Santar{\'e}m, western 
                         Par{\'a} (Brazil). First, the urban spatial patterns were 
                         identified by visual interpretation of a Landsat-TM5 image. These 
                         patterns were then used as input to classify intra-urban land 
                         cover of a SPOT-5 image based on object-based image analysis. 
                         Spectral information and textural and shape characteristics of 
                         image objects enabled to identify eight classes of land cover that 
                         were the base for characterization the urban patterns. An overall 
                         accuracy of 74% and a Kappa coefficient of 0.7 were obtained for 
                         the final classification. This SPOT classification revealed 
                         differences in the proportion of each land cover class composing 
                         each urban pattern, complementary to the first Landsat approach. 
                         Characterizing the intra-urban space of Amazon cities is a local 
                         but important part of the knowledge about the structure and 
                         organization of the Amazon urban territory. This paper provides a 
                         general methodology that could be extended to other regions and 
                         data sources, in order to provide useful information for urban 
                         planning and monitoring.",
  conference-location = "Jo{\~a}o Pessoa",
      conference-year = "25-29 abr. 2015",
                 isbn = "978-85-17-0076-8",
                label = "530",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3JM4A96",
                  url = "http://urlib.net/rep/8JMKD3MGP6W34M/3JM4A96",
           targetfile = "p0530.pdf",
                 type = "Urbaniza{\c{c}}{\~a}o",
        urlaccessdate = "01 dez. 2020"