@InProceedings{LuLiMoFrDuSa:2012:CoMaLi,
               author = "Lu, Dengsheng and Li, Guiying and Moran, Emilio and Freitas, 
                         Corina da Costa and Dutra, Luciano Vieira and Sant’Anna, Sidnei 
                         Jo{\~a}o Siqueira",
          affiliation = "{} and {} and {} and undefined and undefined and undefined",
                title = "A comparison of maximum likelihood classifier and object-based 
                         method based on multiple sensor datasets for land-use/cover 
                         classification in the Brazilian Amazon",
            booktitle = "Proceedings...",
                 year = "2012",
               editor = "Feitosa, Raul Queiroz and Costa, Gilson Alexandre Ostwald Pedro da 
                         and Almeida, Cl{\'a}udia Maria de and Fonseca, Leila Maria Garcia 
                         and Kux, Hermann Johann Heinrich",
                pages = "20--24",
         organization = "International Conference on Geographic Object-Based Image 
                         Analysis, 4. (GEOBIA).",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "Land use/cover classification, maximum likelihood classifier, 
                         object-based method, Brazilian Amazon, Landsat, ALOS PALSAR, 
                         texture, data fusion.",
             abstract = "Majority of land use/cover classification studies are based on the 
                         use of spectral signatures at the per-pixel level, while ignoring 
                         spatial features inherent in an image. The maximum likelihood 
                         classifier (MLC) may be the most common classification method in 
                         practice, but the object-based classification (OBC) method has 
                         been obtained increasingly attention due to its capability of 
                         incorporating spatial information in a classification procedure. 
                         This paper provides a comparison of MLC and OBC based on different 
                         datasets Landsat Thematic Mapper (TM), ALOS PALSAR L-band, and 
                         their combinations. Through comparative analysis of the 
                         classification results, we found that the OBC method cannot 
                         significantly improve overall land use/cover classification 
                         accuracy comparing with the maximum likelihood classification, but 
                         it indeed improve some vegetation classes having complex forest 
                         stand structure, and the OBC method is especially valuable for 
                         higher spatial resolution images. Also the OBC method has better 
                         performance than MLC when a combination of Landsat TM and PALSAR 
                         L-band data as extra bands is used.",
  conference-location = "Rio de Janeiro",
      conference-year = "May 7-9, 2012",
                 isbn = "978-85-17-00059-1",
             language = "en",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP8W/3BT6U45",
                  url = "http://urlib.net/rep/8JMKD3MGP8W/3BT6U45",
           targetfile = "011.pdf",
                 type = "Forest Analysis",
        urlaccessdate = "24 jan. 2021"
}