@MastersThesis{Dutra:1981:ExAtEs,
               author = "Dutra, Luciano Vieira",
                title = "Extra{\c{c}}{\~a}o de atributos espaciais em imagens 
                         multiespectrais",
               school = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                 year = "1981",
              address = "S{\~a}o Jos{\'e} dos Campos",
                month = "1981-03-06",
             keywords = "sele{\c{c}}{\~a}o de atributos, atributos espaciais, 
                         classifica{\c{c}}{\~a}o de padr{\~o}es, imagens 
                         multiespectrais.",
             abstract = "Extra{\c{c}}{\~a}o de atributos {\'e} um fator importante na 
                         determina{\c{c}}{\~a}o da precis{\~a}o que se pode 
                         alcan{\c{c}}ar em tarefas de classifica{\c{c}}{\~a}o de imagens 
                         multiespectrais. Os m{\'e}todos tradicionais de 
                         classifica{\c{c}}{\~a}o ponto a ponto n{\~a}o utilizam toda a 
                         informa{\c{c}}{\~a}o dispon{\'{\i}}vel, pois desprezam o 
                         relacionamento espacial existente entre os pontos da imagem que 
                         pertencem a uma mesma classe. S{\~a}o desenvolvidos m{\'e}todos 
                         para extra{\c{c}}{\~a}o de atributos espaciais de imagens 
                         multiespectrais atrav{\'e}s de filtragem linear e 
                         n{\~a}o-linear. M{\'e}todos de sele{\c{c}}{\~a}o de atributos 
                         s{\~a}o tamb{\'e}m utilizados porque restri{\c{c}}{\~o}es 
                         f{\'{\i}}sicas, custos computacionais e disponibilidade de 
                         padr{\~o}es e treinamento inviabilizam o uso de um grande 
                         n{\'u}mero de atributos extra{\'{\i}}dos de imagem. O 
                         classificador usado sup{\~o}e que essas caracter{\'{\i}}sticas 
                         tem distribui{\c{c}}{\~a}o gaussiana, mas o uso de filtros 
                         n{\~a}o-lineares n{\~a}o garante a normalidade das 
                         caracter{\'{\i}}sticas resultantes. Usam-se, pois, curvas de 
                         transfer{\^e}ncia n{\~a}o-lineares para tentar recuperar o 
                         car{\'a}ter gaussiano dos atributos em quest{\~a}o. A 
                         an{\'a}lise do desempenho dos atributos espaciais, em conjunto 
                         com atributos espectrais, revelou que o uso da 
                         informa{\c{c}}{\~a}o espacial melhora a precis{\~a}o da 
                         classifica{\c{c}}{\~a}o. ABSTRACT: Feature extraction in an 
                         important factor in determining the precision that can be attained 
                         on the classification of multiespectral images. The tradicional 
                         point-but-point classification methods do not use all the 
                         available information since they disregard the spatial 
                         relationship that exists among pixels belongin to the same class. 
                         Methods are developed to extract image spatial features by means 
                         of linear and nom-linear filtering. Feature selection methods are 
                         alsodeveloped, since it is not possible to use all the generated 
                         features because physical restrictions, computacional costs and 
                         availability of traininh patterns do not allow the manipulation of 
                         a large number of extracted image features. The classifier that is 
                         used assumes that the features have a Gaussian distribution 
                         although the use of nonlinear filters does not guarantee the 
                         normality of the resulting features. Therefore, nonlinear transfer 
                         functions are employed as an attempt to restore the Gaussian 
                         character of the involved features. The analysis of the 
                         performance of the spatial features in conjunction with the 
                         spectral ones demonstrated that the use of spatial information 
                         increases the precision of the classification.",
            committee = "Renna e Souza, Celso de (presidente) and Mascarenhas, Nelson 
                         Delfino D'{\'A}vila (orientador) and Sonnenburg, Claudio Roland 
                         and Velasco, Fl{\'a}vio Roberto Dias",
           copyholder = "SID/SCD",
         englishtitle = "x",
             language = "pt",
                pages = "86",
                  ibi = "8JMKD3MGP8W/35E555P",
                  url = "http://urlib.net/ibi/8JMKD3MGP8W/35E555P",
           targetfile = "Luciano Vieira Dutra_INPE-2315-TDL-078.pdf",
        urlaccessdate = "06 maio 2024"
}