@InProceedings{LimaGuer:2011:UsReNe,
               author = "Lima, Alexandre Gomes de and Guerreiro, Ana Maria Guimar{\~a}es",
          affiliation = "Instituto Federal de Educa{\c{c}}{\~a}o, Ci{\^e}ncia e 
                         Tecnologia do RN - IFRN/DIETINF and {Universidade Federal do Rio 
                         Grande do Norte – UFRN/DCA}",
                title = "Uso das redes neurais de fun{\c{c}}{\~a}o de base radial e 
                         Growing Neural Gas na classifica{\c{c}}{\~a}o de imagens de 
                         sensoriamento remoto",
            booktitle = "Anais...",
                 year = "2011",
               editor = "Epiphanio, Jos{\'e} Carlos Neves and Galv{\~a}o, L{\^e}nio 
                         Soares",
                pages = "7247--7254",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 15. (SBSR).",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "remote sensing, image processing, artificial neural network, 
                         growing neural gas, sensoriamento remoto, processamento de 
                         imagens, rede neural artificial.",
             abstract = "Several works describe the use of artificial neural networks in 
                         remote sensing applications. However, its relatively scarce the 
                         amount of publications about these applications involving the 
                         Growing Neural Gas (GNG) networks. This work describes a hybrid 
                         neural classifier based on the radial base functions (RBF) and GNG 
                         neural networks. The unsupervised learning is performed by GNG 
                         network in order to determine centers and number of hidden neurons 
                         RBF network. The supervised learning is performed by pseudo 
                         inverse matrix algorithm in order to find RBF networks synaptic 
                         weights. The proposed classifier doesnt require the number of 
                         centers be specified in advance. This number starts in two and is 
                         successively increased by GNG network until a desired performance 
                         criterion be achieved. The classification of one multispectral 
                         ETM/Landsat7 image, bands 1, 2, 3 and 4, involving part of city of 
                         Natal-RN is performed for seven ground cover classes. The results 
                         achieved by the proposed classifier and maximum likelihood 
                         Bayesian classifier are compared through the confusion matrix, hit 
                         coefficient, Kappa coefficient and generated images. This set of 
                         data shows a slight superiority from proposed classifier. Further 
                         this satisfactory result, the neural classifier is important to 
                         eliminate the try-and-error procedure usually realized to find RBF 
                         neural networks centers.",
  conference-location = "Curitiba",
      conference-year = "30 abr. - 5 maio 2011",
                 isbn = "{978-85-17-00056-0 (Internet)} and {978-85-17-00057-7 (DVD)}",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "3ERPFQRTRW/39UFJ9S",
                  url = "http://urlib.net/ibi/3ERPFQRTRW/39UFJ9S",
           targetfile = "p0398.pdf",
                 type = "Processamento de Imagens",
        urlaccessdate = "12 maio 2024"
}