@InProceedings{MacielVinhCâma:2015:AlClSe,
               author = "Maciel, Adeline Marinho and Vinhas, L{\'u}bia and C{\^a}mara, 
                         Gilberto",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Algoritmos de clustering para separa{\c{c}}{\~a}o de culturas 
                         agr{\'{\i}}colas e tipos de uso e cobertura da Terra utilizando 
                         dados de sensoriamento remoto",
            booktitle = "Anais...",
                 year = "2015",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "4620--4627",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 17. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "Remote sensing data are useful in different areas of research and 
                         application, among them agriculture, which can be used for 
                         monitoring crops and even of the support the productivity 
                         prediction of certain crops. For this, one of the most desired 
                         features is the ability to separate, or classify, in remote 
                         sensing images, the different crops observed in a given region. In 
                         order to obtain a good classification is common that are used 
                         multiple radiometric attributes available in remote sensing data. 
                         Among the various techniques and algorithms for classification are 
                         those based in clustering. However, due the high correlation among 
                         radiometric attributes and even the difficult to implement 
                         classifiers based in multiple attributes is necessary to study how 
                         reduce the dimensionality of the attributes used in the data 
                         classification. This is a work in progress that aims to exercise 
                         the use of feature selection algorithms, for reduce the 
                         dimensionality of attributes checking which attributes are more 
                         correlated with a class of interest, and of clustering algorithms 
                         in the separation of crops from other types of land use and cover, 
                         using remote sensing data. The results show that some data are 
                         easily separated by the clustering algorithms, because they have a 
                         high similarity between its individuals, but other elements 
                         require more attributes that can add more information to 
                         discriminate them from others.",
  conference-location = "Jo{\~a}o Pessoa",
      conference-year = "25-29 abr. 2015",
                 isbn = "978-85-17-0076-8",
                label = "903",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3JM4D4C",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3JM4D4C",
           targetfile = "p0903.pdf",
                 type = "Classifica{\c{c}}{\~a}o e minera{\c{c}}{\~a}o de dados",
        urlaccessdate = "27 abr. 2024"
}