@InProceedings{Augusto-SilvaValSanAlcSte:2013:AnClMa,
               author = "Augusto-Silva, P{\'e}tala Bianchi and Val{\'e}rio, Larissa 
                         Patr{\'{\i}}cio and Santos, Thiago Batista dos and 
                         Alc{\^a}ntara, Enner Herenio de and Stech, Jos{\'e} Luiz",
          affiliation = "{} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "An{\'a}lise de classificadores para mapeamento de uso e cobertura 
                         do solo",
            booktitle = "Anais...",
                 year = "2013",
               editor = "Epiphanio, Jos{\'e} Carlos Neves and Galv{\~a}o, L{\^e}nio 
                         Soares",
                pages = "2424--2430",
         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 = "Land Use and Land Cover (LULC) maps have been developed in order 
                         to guide decision-making upon spatial data. This allows the 
                         construction of indicators for assessing the support capacity of 
                         the environment. In the present work, a map of land cover and use 
                         was developed with a TM-Landsat-5 image beyond a multispectral 
                         classification scheme. Methods of classification available in the 
                         software SPRING were tested to evaluate their performance in 
                         organize the study area based on five thematic classes defined by 
                         FAO. A reference map was developed by an interpreter based on 
                         visual classification aided with high-resolution images from 
                         Google Earth. Sample points were selected for comparison between 
                         the reference map and the ones automatically classified, and the 
                         performance of the classifying methods was evaluated based on the 
                         percentage of rights. The Isoseg has the best percentage (83,79%) 
                         of rights, but since it is a non-supervised classificator, it 
                         separates the scene into much more themes than the others, so we 
                         have to remap these themes into the original classes of FAO. Thats 
                         why Bhattacharya was considered the best method with 77,25% of 
                         rights. A class named antrophic was the one with the worst 
                         performance probably because some objects can get mixed up with 
                         the spectral response of soil prepared for cultivation. The 
                         methods in general can perform well, but in fact they should be 
                         used with the aid of interpreter knowledge and knowing the 
                         precision its required for the work.",
  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 = "115",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "3ERPFQRTRW34M/3E7G92T",
                  url = "http://urlib.net/ibi/3ERPFQRTRW34M/3E7G92T",
           targetfile = "p0115.pdf",
                 type = "Classifica{\c{c}}{\~a}o e Minera{\c{c}}{\~a}o de Dados",
        urlaccessdate = "04 maio 2024"
}