@InProceedings{NepomucenoVaFrSaSiSaDu:2003:ClDaRa,
               author = "Nepomuceno, Alcina Maria and Valeriano, Dalton de Morisson and 
                         Freitas, Corina da Costa and Santa Rosa, Ant{\^o}nio Nuno de 
                         Castro and Silva, Nilton Correia da and Santos, Jo{\~a}o Roberto 
                         dos and Dutra, Luciano Vieira",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Universidade de Bras{\'{\i}}lia 
                         (UnB). Departamento da Ci{\^e}ncia da Computa{\c{c}}{\~a}o.} 
                         and {Universidade de Bras{\'{\i}}lia (UnB). Instituto de 
                         Geoci{\^e}ncias.} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Classifica{\c{c}}{\~a}o de dados de radar na Banda-P utilizando 
                         rede neural artificial para mapeamento de cobertura da terra na 
                         regi{\~a}o de Santar{\'e}m, Par{\'a}",
            booktitle = "Anais...",
                 year = "2003",
               editor = "Epiphanio, Jos{\'e} Carlos Neves and Fonseca, Leila Maria 
                         Garcia",
                pages = "2249 - 2256",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 11. (SBSR).",
            publisher = "INPE",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "radar, classification, Artificial Neural Networks, land cover.",
             abstract = "The present work is concerned with the potential application of 
                         artificial neural networks for the classification of polarimetric 
                         radar images operating in the P band. The study area is located 
                         near the Tapaj{\'o}s National Forest, in the northern State of 
                         Par{\'a}, Brazil, and comprises regions of dense rain forest 
                         partially disturbed by deforestation and agricultural land use. 
                         The remote sensing data were obtained during the test mission of 
                         the polarimetric imaging radars (P (415 MHz) and X (10 GHz) bands) 
                         of the German company AeroSensing RadarSystem GmbH, promoted by 
                         the Brazilian Army and the National Institute for Space Research 
                         in September 2000. A P band 2.4 km x 7.4 km- image was selected to 
                         assess the capacity of the neural network {"}Fuzzy-ART{"} for the 
                         land cover classes discrimination. Two time-domain filtering 
                         processes were compared regarding their ability to reduce the 
                         speckle noise, both of them operating with neighborhood boxes of 
                         three and five cells. Filtered and non-filtered HH, HV as well VV 
                         P band images were used as inputs by the network to generate 
                         classified images. A confusion matrix based on ground truth data 
                         was employed for the global and partial classification accuracy 
                         analysis, which considered seven land cover classes: exposed soil 
                         (IF), pasture/tillage (PC), recent forest regeneration (RN), 
                         intermediate forest regeneration (RI), old forest regeneration 
                         (RA), very old forest regeneration (RMA), and primary forest (FP). 
                         The results show that the unsupervised neural network-based 
                         classification method was able to accordingly map the land cover 
                         classes in respect to the field observations samples. The results 
                         point to prospective use of data and classification methodology 
                         for fast and accurate radar images interpretation, complying with 
                         the needs of ongoing monitoring and fiscalization activities in 
                         the dense rain forest in a quick and efficient manner.",
  conference-location = "Belo Horizonte",
      conference-year = "5-10 abr. 2003",
           copyholder = "SID/SCD",
                 isbn = "85-17-00017-X",
             language = "Portuguese",
         organisation = "Instituto Nacional de Pesquisas Espaciais",
                  ibi = "ltid.inpe.br/sbsr/2002/11.22.21.04",
                  url = "http://urlib.net/ibi/ltid.inpe.br/sbsr/2002/11.22.21.04",
           targetfile = "16_446.pdf",
                 type = "Radar: Processamento e Aplica{\c{c}}{\~o}es / Radar: Processing 
                         and Applications",
        urlaccessdate = "18 jun. 2024"
}