@InProceedings{MarinhoLuzBaptSpec:2017:ClSuSo,
               author = "Marinho, Carlos Alberto Branco and Luz, Priscila Maria Colombo da 
                         and Baptista, Gustavo Macedo de Mello and Specht, Alexandre",
                title = "Classifica{\c{c}}{\~a}o supervisionada entre soja Bt e soja 
                         n{\~a}o-Bt, em imagem RGB gerada por drone, a partir da 
                         ferramenta Pixel Explorer",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "1455--1461",
         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 = "The image classification is an important tool used by remote 
                         sensing professionals, but the classification of features that 
                         have very similar features is a work of extreme difficulty, since 
                         the bands of electromagnetic radiation in the portion of the 
                         visible many confuse the analyst and commercial software of 
                         classification and, thus, similar features are commonly classified 
                         as equals. This work aims to demonstrate that it is possible to 
                         perform a supervised classification from images obtained by remote 
                         sensors, even those coming from sensors with low spectral 
                         resolution, as in the case of recreational UAVs, and with it 
                         distinguish not only two different kinds of coverage vegetable, 
                         but differentiate two variations of the same plant species, as is 
                         the case of Bt-soybean evaluated in relation to non-Bt-soybean. 
                         Using the computational tool named Pixel Explorer (PE), developed 
                         in Matlab by the first author of this work, as dissertation 
                         composition and later thesis, a classification was made in an 
                         experimental area of EMBRAPA, resulting in the separation of the 
                         parcels containing two kinds of genetically different soybean, 
                         being classified material composed of images collected by a drone 
                         model: Phanton 3 Professional, with spectral resolution restricted 
                         to bands RGB, with oblique view and without gyro stabilization, 
                         leading to the hypothesis that the result can be even more 
                         reliable if the same methodology is used in images generated by 
                         sensors with high spatial and spectral resolutions and target 
                         nadir for both vegetation and geology.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "59238",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PS4GNG",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PS4GNG",
           targetfile = "59238.pdf",
                 type = "Processamento de imagens",
        urlaccessdate = "09 maio 2024"
}