@InProceedings{GutiérrezSeijDuiv:2011:OpLaCo,
               author = "Guti{\'e}rrez, Jesus Aguirre and Seijmonsbergen, Arie and 
                         Duivenvoorden, Joost",
          affiliation = "{Universiteit van Amsterdam - Netherlands} and {Universiteit van 
                         Amsterdam - Netherlands} and {Universiteit van Amsterdam - 
                         Netherlands}",
                title = "Optimizing land cover change detection using combined pixel-based 
                         and object-based image classification in a mountainous area in 
                         Mexico",
            booktitle = "Anais...",
                 year = "2011",
               editor = "Epiphanio, Jos{\'e} Carlos Neves and Galv{\~a}o, L{\^e}nio 
                         Soares",
                pages = "6556--6563",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 15. (SBSR).",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "accuracy assessment, Landsat, segmentation, post-classification, 
                         remote sensing.",
             abstract = "Inventories of past and present land cover changes form the basis 
                         for future conservation strategies and landscape management. In 
                         this study Landsat images of a mountainous area in Mexico are used 
                         in an objectbased and pixel-based image classification. The land 
                         cover categories with the highest individual classification 
                         accuracies determined with these two methods are extracted and 
                         merged into combined land cover classifications. Seven land cover 
                         categories were extracted and combined into single combined best 
                         classification layers. Comparison of the overall classification 
                         accuracies for 1999 and 2006 of the pixel-based (0.74 and 0.81), 
                         object-based (0.77 and 0.71) and the combined (0.88 and 0.87) 
                         classifications shows that the combination method produces better 
                         results. These combined classifications then form the input for 
                         change detection between the two years, by applying 
                         post-classification object-based change analysis using image 
                         differencing. It is concluded that post-classification 
                         object-based change detection analysis leads to an improved land 
                         cover change detection result with an overall accuracy of 0.77. 
                         This approach has the potential to be applied in similar mountain 
                         areas using medium resolution imagery for land cover change 
                         analysis.",
  conference-location = "Curitiba",
      conference-year = "30 abr. - 5 maio 2011",
                 isbn = "{978-85-17-00056-0 (Internet)} and {978-85-17-00057-7 (DVD)}",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "3ERPFQRTRW/3A6LQCB",
                  url = "http://urlib.net/ibi/3ERPFQRTRW/3A6LQCB",
           targetfile = "p0149.pdf",
                 type = "Mudan{\c{c}}a de Uso e Cobertura da Terra",
        urlaccessdate = "12 maio 2024"
}