@Article{AlvesFlorPere:2010:MaÁrUr,
               author = "Alves, Claudia Durand and Florenzano, Teresa Gallotti and Pereira, 
                         Madalena Niero",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Mapeamento de {\'a}reas urbanizadas com imagens landsat e 
                         classifica{\c{c}}{\~a}o baseada em objeto / Mapping of urban 
                         areas with LANDSAT images and object based image analysis",
              journal = "Revista Brasileira de Cartografia",
                 year = "2010",
               volume = "1",
                pages = "11",
                month = "out.",
             keywords = "an{\'a}lise baseada em objeto, {\'a}reas urbanizadas, 
                         sensoriamento remoto, object based image analysis, urban areas, 
                         remote sensing.",
             abstract = "Este estudo tem como objetivo o desenvolvimento de uma metodologia 
                         de classifica{\c{c}}{\~a}o autom{\'a}tica de {\'a}reas 
                         urbanizadas cont{\'{\i}}nuas e dispersas que seja 
                         replic{\'a}vel em diferentes regi{\~o}es do Brasil. Com essa 
                         metodologia busca-se o aumento da exatid{\~a}o do mapeamento bem 
                         como reduzir a subjetividade e o tempo empregado no procedimento. 
                         Para este fim, aplicou-se, usando o software Definiens, a 
                         classifica{\c{c}}{\~a}o baseada em Objeto em imagem LANDSAT da 
                         regi{\~a}o de Piracicaba, Limeira e Rio Claro, do estado de 
                         S{\~a}o Paulo, obtida em 2007. Este procedimento consiste na 
                         segmenta{\c{c}}{\~a}o multiresolu{\c{c}}{\~a}o das imagens e 
                         na classifica{\c{c}}{\~a}o baseada na l{\'o}gica fuzzy. Na 
                         avalia{\c{c}}{\~a}o dos resultados foram utilizadas imagens de 
                         alta resolu{\c{c}}{\~a}o, dispon{\'{\i}}veis no Google Earth. 
                         O bom desempenho obtido na classifica{\c{c}}{\~a}o 
                         autom{\'a}tica da {\'a}rea de estudo ({\'{\i}}ndice global de 
                         0,94 e Kappa de 0,72) indica a viabilidade do m{\'e}todo aplicado 
                         para outras {\'a}reas urbanizadas. ABSTRACT: This study attempts 
                         to develop an automatic method to map urban areas and urban 
                         sprawl, and that have the possibility of replicating to different 
                         urbanization regions of Brazil. The objective is to achieve a 
                         methodology for the increase of precision and reduction of 
                         processing time and subjectivity. Object-based Image Analysis with 
                         Definiens software and LANDSAT images (acquired in 2007) of the 
                         Piracicaba, Limeira and Rio Claro region (S{\~a}o Paulo state) 
                         were used. The object based approach is a spatial analysis method 
                         based essentially on segmentation and classification outputs. 
                         Primitive objects are created on given space scale levels with 
                         different resolution, and classification rules are applied based 
                         on a fuzzy rule decision tree classifier. High resolution images 
                         available at Google Earth were used for accuracy assessment. The 
                         good results obtained (Overall Accuracy: 0.94 and Kappa Index: 
                         0.72) from automatic classification of the study area indicate 
                         that the object-based method can be suitable for semi-automatic 
                         urban mapping.",
                 issn = "0560-4613 and 1808-0936",
             language = "pt",
           targetfile = "263-738-1-PB.pdf",
        urlaccessdate = "15 jun. 2024"
}