@InProceedings{FuchshuberVascMiraLand:2013:AvTéCl,
               author = "Fuchshuber, Eduardo Monteiro and Vasconcelos, Adriano de Oliveira 
                         and Miranda, Fernando Pellon de and Landau, Luiz",
                title = "Avalia{\c{c}}{\~a}o de t{\'e}cnicas de 
                         classifica{\c{c}}{\~a}o autom{\'a}tica de dados 
                         multi-polarim{\'e}tricos na banda-L do sensor R99B-SAR para o 
                         mapeamento de {\'a}reas inundadas do Lago de Coari, Amaz{\^o}nia 
                         Central",
            booktitle = "Anais...",
                 year = "2013",
               editor = "Epiphanio, Jos{\'e} Carlos Neves and Galv{\~a}o, L{\^e}nio 
                         Soares",
                pages = "8397--8404",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 16. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "Studies in Central Amazonia using remote sensing data can 
                         contribute to an understanding on a regional scale of its 
                         physiographic characteristics, providing support for the 
                         preparation of maps depicting the sensitivity to oil spills of the 
                         complex ecosystems existent in the region. The study area herein 
                         reported is remote, difficult to access, and permanently 
                         cloud-covered. In addition, water level variation in the drainage 
                         basin can reach as much as 17 meters between wet and dry seasons. 
                         Therefore, it is necessary to map the cover types most sensitive 
                         to oil spills based on image datasets suitable to portray such a 
                         seasonal change. In this context, the present paper used the 
                         algorithm USTC (Unsupervised Semivariogram Textural Classifier), 
                         complemented by object-based segmentation and classification 
                         techniques, to process digitally calibrated L-band images acquired 
                         by the Multipolarimetric R99B-SAR system. These data were obtained 
                         in the region of Coari (AM) as part of the mission entitled 
                         Multi-Application Purpose SAR (MAPSAR). The Brazilian-German 
                         MAPSAR mission is a proposal for a light L-band SAR sensor, based 
                         on INPE´s Multi-Mission Platform (500 kg class spacecraft). 
                         Application of the USTC algorithm in defining super classes for 
                         object-based classification constitutes an innovative approach for 
                         digital processing of SAR data. To analyze and compare the 
                         accuracy of results of USTC and object-based classification, we 
                         used the confusion matrix (error matrix) and Kappa index. Research 
                         results enhanced macrophyte stands and flooded forests, which are 
                         the cover types most sensitive to oil spills in the fluvial 
                         scenario of Central Amazonia.",
  conference-location = "Foz do Igua{\c{c}}u",
      conference-year = "13-18 abr. 2013",
                 isbn = "{978-85-17-00066-9 (Internet)} and {978-85-17-00065-2 (DVD)}",
                label = "685",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "3ERPFQRTRW34M/3E7GFEG",
                  url = "http://urlib.net/ibi/3ERPFQRTRW34M/3E7GFEG",
           targetfile = "p0685.pdf",
                 type = "Radar: Pesquisa, Desenvolvimento e Aplica{\c{c}}{\~o}es",
        urlaccessdate = "01 maio 2024"
}