@Article{GarciaSantMuraKux:2012:AnPoIm,
               author = "Garcia, C{\'e}sar Edwin and Santos, Jo{\~a}o Roberto dos and 
                         Mura, Jos{\'e} Claudio and Kux, Hermann Johann Heinrich",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)}",
                title = "An{\'a}lise do potencial de imagem TerraSAR-X para mapeamento 
                         tem{\'a}tico no sudoeste da Amaz{\^o}nia brasileira / Analysis 
                         of the potential use from TerraSAR-X images for thematic mapping 
                         in SW Brazilian Amazon region",
              journal = "Acta Amazonica",
                 year = "2012",
               volume = "42",
               number = "2",
                pages = "205--214",
                 note = "{Setores de Atividade: Informa{\c{c}}{\~a}o e 
                         comunica{\c{c}}{\~a}o.} and {Informa{\c{c}}{\~o}es Adicionais: 
                         Abstract} and The objective of this work was to analyze the 
                         potential use of SAR polarimetric images from the TerraSAR-X 
                         sensor system, at StripMap mode, to map land use and land cover in 
                         SW Brazilian Amazon. Amplitude images at polarizations AHH, AVV 
                         and A<HH.VV*>, derived from the co-variance matrix, as well as the 
                         entropy derived from the decomposition of targets by eigenvalues, 
                         are parts of the datasets investigated individually or in combined 
                         form. Two classifiers were used: the first is based and on 
                         statistical functions of maximum likelihood (MAXVER), and the 
                         second is the contextual method (Context). The thematic results 
                         from these classifications were evaluated by a confusion matrix 
                         and by the Kappa index. Summarizing we can state and that the 
                         components A<HH.VV*> and A<entropia>, gave a significant 
                         contribution to the image classification procedure, considering 
                         specially the Context method, whose performance reached 78% of 
                         Global Accuracy and a Kappa index of 0.70..",
             keywords = "mapeamento florestal, radar, classifica{\c{c}}{\~a}o 
                         polarim{\'e}trica, Amaz{\^o}nia, forest mapping, radar, 
                         polarimetric classification, Amazon.",
             abstract = "O presente trabalho tem como objetivo analisar o potencial de 
                         imagens SAR polarim{\'e}tricas do sensor TerraSAR-X, no modo 
                         StripMap, para mapear o uso e cobertura da terra na regi{\~a}o 
                         sudoeste da Amaz{\^o}nia brasileira. No procedimento 
                         metodol{\'o}gico imagens de amplitude nas 
                         polariza{\c{c}}{\~o}es AHH e AVV, A<HH.VV*> derivada da matriz 
                         de covari{\^a}ncia, bem como da entropia AEntropia derivada da 
                         decomposi{\c{c}}{\~a}o de alvos por auto-valores fizeram parte, 
                         de forma individual ou combinada, do conjunto de dados 
                         investigados. Na classifica{\c{c}}{\~a}o das imagens foram 
                         empregados dois classificadores: um baseado nas 
                         fun{\c{c}}{\~o}es estat{\'{\i}}sticas de m{\'a}xima 
                         verossimilhan{\c{c}}a (MAXVER); e outro, o m{\'e}todo contextual 
                         (Context). Os resultados tem{\'a}ticos dessas 
                         classifica{\c{c}}{\~o}es foram avaliados atrav{\'e}s da matriz 
                         de confus{\~a}o e pelo {\'{\i}}ndice Kappa. De forma 
                         sintetizada pode-se afirmar que as componentes A<HH.VV*> e 
                         AEntropia, t{\^e}m significativa contribui{\c{c}}{\~a}o no 
                         procedimento classificat{\'o}rio, sobretudo pelo m{\'e}todo 
                         Context, cujo desempenho alcan{\c{c}}ou com 78% de exatid{\~a}o 
                         global e {\'{\i}}ndice Kappa de 0,70. ABSTRACT: The objective of 
                         this work was to analyze the potential use of SAR polarimetric 
                         images from the TerraSAR-X sensor system, at StripMap mode, to map 
                         land use and land cover in SW Brazilian Amazon. Amplitude images 
                         at polarizations AHH, AVV, A<HH.VV*>, derived from the co-variance 
                         matrix, as well as the entropy AEntropia, derived from the 
                         decomposition of targets by eigenvalues, are parts of the datasets 
                         investigated individually or in combined form. Two classifiers 
                         were used: the first is based on statistical functions of maximum 
                         likelihood (MAXVER), and the second is the contextual method 
                         (Context). The thematic results from these classifications were 
                         evaluated by a confusion matrix and by the Kappa index. 
                         Summarizing we can state that the components A<HH.VV*> and 
                         AEntropia, gave a significant contribution to the image 
                         classification procedure, considering specially the Context 
                         method, whose performance reached 78% of Global Accuracy and a 
                         Kappa index of 0.70.",
                  doi = "10.1590/S0044-59672012000200004",
                  url = "http://dx.doi.org/10.1590/S0044-59672012000200004",
                 issn = "0044-5967",
                label = "lattes: 3233696672067020 5 GarciaSanMurKuxKux:2012:AnPoIm",
             language = "pt",
           targetfile = "An{\'a}lise do potencial de imagem TerraSAR-X para.pdf",
        urlaccessdate = "30 abr. 2024"
}