@InProceedings{AngeloFerr:2017:DeMuIm,
               author = "Angelo, Neide Pizzolato and Ferreira, Rute Henrique da Silva",
                title = "Detec{\c{c}}{\~a}o de mudan{\c{c}}as em imagens multitemporais 
                         de sensoriamento remoto empregando SVM e pertin{\^e}ncia de 
                         pixels",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "352--359",
         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 investigates an approach to the problem of change 
                         detection in multitemporal remote sensing images using Support 
                         Vector Machines (SVM) based on RBF kernel (Radial Basis Function) 
                         combined with a new relevance metric called Delta b (\b). 
                         The methodology is based on the difference of the fraction images 
                         produced for each date. In images of natural scenes the difference 
                         in soil and vegetation fractions tends to have a symmetrical 
                         distribution around the mean of its pixels. This fact can be used 
                         to model two normal multivariate distributions: change and 
                         non-change. The Expectation-Maximization (EM) algorithm is 
                         implemented for estimating the parameters (mean vector, covariance 
                         matrix, and prior probability) associated with these two 
                         distributions. Random samples are extracted from these two 
                         distributions and used to train a SVM classifier based on RBF 
                         kernel. The proposed methodology is tested using multi-temporal 
                         data sets of multispectral images Landsat-TM covering the same 
                         scene, located in Roraima state, in two different dates. Test 
                         samples are obtained by the use of Change Vector Analysis (CVA) 
                         and used to validate the estimation method of pertinence. It is 
                         expected that this methodology could be applied to detection of 
                         change for multispectral and hyperspectral multitemporal images 
                         used in remote sensing.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "59609",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PS43RE",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PS43RE",
           targetfile = "59609.pdf",
                 type = "Processamento de imagens",
        urlaccessdate = "27 abr. 2024"
}