@InProceedings{DutraHubeSoar:2000:FoClSA,
               author = "Dutra, Luciano Vieira and Huber, R. and Soares, Sergio Monteiro",
                title = "Forest Classification from SAR Data using Multiresolutional and 
                         Autoregressive Approaches",
                 year = "2000",
                pages = "419--423",
         organization = "World Multiconference on systemics Cybernetics and Informatics.",
             keywords = "PROCESSAMENTO DIGITAL DE IMAGENS, radar de abertura 
                         sint{\'e}tica, SAR, filtros para bandas, 
                         classifica{\c{c}}{\~a}o de imagens, reconhecimentos de 
                         padr{\~o}es, synthetic aperture radar, band pass filters, image 
                         classification, pattern recognition.",
             abstract = "Two methodologies for rain forest mapping in Amazonia using 
                         textural features derived from JERS- 1 data are presented. The 
                         first one uses a set of pass-band filters, called Laws filters, 
                         combined by Principal Components Transformation so as to match a 
                         filter to the most undulated forest texture. Multiresolutional 
                         filters are derived for resolution levels I to 1/5 of the 
                         original. Texture energy features are obtained from the inverse of 
                         the matched filters for each resolution level. Thresholding is 
                         employed to distinguish between undulated and flat forest. The 
                         final decison requires linking of resolution levels, what is is 
                         accomplished by unweighted or weighted fusion of decisions taken 
                         on different levels. The second method derives two two-dimensional 
                         autoregressive filters. One filter is fitted to undulated forest 
                         and another one fitted to flat forest. The autoregressive approach 
                         also employs an energy feature. In this case, it is calculated 
                         from both inverse filters, which are derived from the 
                         autoregressive matched filters. Separation of the considered 
                         forest types is achieved using smoothed versions of these energy 
                         filters as input to a maximum likelihood classifier. Both 
                         techniques showed good discrimination, with some superiority of 
                         the Multiresolutional approach.",
  conference-location = "Orlando, EU",
      conference-year = "July 2000",
                label = "9469",
           targetfile = "INPE 8530.pdf",
        urlaccessdate = "12 maio 2024"
}