@InProceedings{CasarotiCentPrun:2017:CoUsCo,
               author = "Casaroti, Carla Jaqueline and Centeno, Jorge Antonio Silva and 
                         Prunzel, Jaqueline",
                title = "Compara{\c{c}}{\~a}o do uso combinado de vari{\'a}veis 
                         espectrais e {\'{\i}}ndices de vegeta{\c{c}}{\~a}o calculados 
                         a partir das bandas Red e Red Edge para classifica{\c{c}}{\~a}o 
                         de uma imagem RapidEye",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "3584--3591",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 18. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "This paper consists on describing the steps involving two 
                         classifications, using the OBIA (Object-oriented Image Analysis) 
                         approach along with a RapidEye high spatial resolution image, in 
                         order to compare the classification accuracy using the usual red 
                         band and the red edge band, to classify the vegetation land cover. 
                         To classify the geographic objects yielded from the 
                         multiresolution segmentation, spectral descriptors from the bands 
                         and NDVIs (Normalized Difference Vegetation Index) from the usual 
                         band red and the band red edge, as well as a Digital Elevation 
                         Model (DEM) were used. To make the descriptors'' choice, a 
                         selection was made towards the attributes, which could better 
                         separate the classes of interest regarding the samples. The two 
                         classifications were performed, using the selected descriptors to 
                         each one, and then the global accuracy as well as the coefficient 
                         Kappa and confusion matrix were compared. The global accuracy from 
                         the first classification using the usual red band was of 87% and 
                         the other one was 90%, indicating that, the red edge band could 
                         improve in 3% the classification accuracy when used. As main steps 
                         of the released methodology we had: classes of interest 
                         definition, choice of the segmentation parameters, class 
                         descriptors selection for the two classifications, and at last the 
                         two classifications.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "59284",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PSLT84",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSLT84",
           targetfile = "59284.pdf",
                 type = "Classifica{\c{c}}{\~a}o e minera{\c{c}}{\~a}o de dados",
        urlaccessdate = "27 abr. 2024"
}