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@InProceedings{DutraScAbNeFrAn:2009:LaCoCl,
               author = "Dutra, Luciano Vieira and Scofield, Graziela Balda and Aboud Neta, 
                         Sumaia Resegue and Negri, Rog{\'e}rio Galante and Freitas, Corina 
                         da Costa and Andrade, Daniel",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais} and {Instituto 
                         Nacional de Pesquisas Espaciais} and {Instituto Nacional de 
                         Pesquisas Espaciais} and {Instituto Nacional de Pesquisas 
                         Espaciais} and {Instituto Nacional de Pesquisas Espaciais} and 
                         {Instituto Nacional de Pesquisas Espaciais}",
                title = "Land Cover Classification in Amazon using Alos Palsar Full 
                         Polarimetric Data",
            booktitle = "Anais...",
                 year = "2009",
               editor = "Epiphanio, Jos{\'e} Carlos Neves and Galv{\~a}o, L{\^e}nio 
                         Soares",
                pages = "7259--7264",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 14. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "land cover classification. polarimetry, segmentation, sar, radar 
                         Palsar, Alos.",
             abstract = "The ALOS PALSAR sensor can provide full polarimetric SAR data (HH, 
                         HV and VV) but the full polarimetric mode is only available 
                         experimentally. Here, several supervised classifiers have been 
                         studied to determine how much the use of full polarized (HH, VV 
                         and HV, no phase information) PALSAR data information can improve, 
                         or not, the overall classification accuracy in comparison with the 
                         standard products, which, for PALSAR instrument, is the HH (like 
                         JERS-1) or the dual polarization product HH-HV. The study area, 
                         Tapaj{\'o}s National Forest at the south of Santar{\'e}m City, 
                         in the Brazilian Amazon, Par{\'a} State, has being object of 
                         intensive scientific observation for more than 15 years. Several 
                         types of supervised classifiers are tested for having, as much as 
                         possible, an assessment rather independent of the classifier type. 
                         Initial results indicate that, no phase considered, the dual 
                         polarization product HH-HV is the better channels combination for 
                         mapping the set of tropical classes composed by the primary 
                         forest, secondary forest, bare soil, agriculture and degraded 
                         forest. Also, it was observed that one year regeneration areas are 
                         not discriminated in any PALSAR combination, which indicates the 
                         utility of maintaining the complementary use of optical images 
                         when possible, because, in the optical combination, the one year 
                         regeneration class still shows different from secondary forest. 
                         Region based classification, particularly one developed to take in 
                         account as much as possible the radar statistical behavior, 
                         generally presented better performance.",
      accessionnumber = "0",
  conference-location = "Natal",
      conference-year = "25-30 abr. 2009",
                 isbn = "978-85-17-00044-7",
             language = "en",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "dpi.inpe.br/sbsr@80/2008/11.17.18.24",
                  url = "http://urlib.net/rep/dpi.inpe.br/sbsr@80/2008/11.17.18.24",
           targetfile = "7259-7264.pdf",
                 type = "Radar: Pesquisa, Desenvolvimento e Aplica{\c{c}}{\~o}es",
        urlaccessdate = "22 jan. 2021"
}


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