@Article{FranciscoAlme:2012:EvPeSt,
               author = "Francisco, Cristiane Nunes and Almeida, Claudia Maria",
          affiliation = "Univ Fed Fluminense, Inst Geociencias, Dept Anal Geoambiental, 
                         Campus Praia Vermelha, BR-24210310 Niteroi, RJ, Brazil. and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Evaluating the performance of statistical and textural attributes 
                         for an object-based land cover classification / 
                         Avalia{\c{c}}{\~a}o de desempenho de atributos 
                         estat{\'{\i}}sticos e texturais em uma classifica{\c{c}}{\~a}o 
                         de cobertura da terra baseada em objeto",
              journal = "Boletim de Ci{\^e}ncias Geod{\'e}sicas",
                 year = "2012",
               volume = "18",
               number = "2",
                pages = "302 326",
                month = "Apr.-Jun.",
             keywords = "Semantic Networks, Images Classification, Data Mining, ALOS SAR 
                         IMAGES.",
             abstract = "This paper aim at evaluating the performance of two semantic 
                         networks generated by data mining for classifying land cover using 
                         GEographic Object-Based Image Analysis (GEOBIA). The first one 
                         used statistical and texture attributes, and the second network 
                         employed only statistical attributes. The attributes were 
                         extracted from ALOS/AVNIR images pan-sharpened with ALOS/PRISM. 
                         Relief information provided by the TOPODATA geomorphometric 
                         database was also used as input data. The studied area is Nova 
                         Friburgo County, with an extension of 933 km(2), located in the 
                         mountainous region of Rio de Janeiro State. The Kappa index 
                         obtained by the classification based on statistical and texture 
                         attributes was 0.81, while the result for the classification 
                         derived only from statistical attributes achieved 0.84. These 
                         values corroborate the excellent accuracy of both results. The 
                         statistical hypothesis test between the two indices at 95% 
                         confidence interval demonstrated that there is no difference 
                         between the two classification accuracies. RESUMO Este artigo tem 
                         como objetivo avaliar o desempenho de duas redes sem{\^a}nticas 
                         geradas por minera{\c{c}}{\~a}o de dados para a 
                         classifica{\c{c}}{\~a}o de cobertura da terra por meio de 
                         an{\'a}lise de imagens baseada em objetos geogr{\'a}ficos 
                         (GEographic Object-Based Image Analysis GEOBIA). Para isto, uma 
                         rede utilizou-se de descritores estat{\'{\i}}sticos e texturais, 
                         e a outra, apenas de descritores estat{\'{\i}}sticos. A base de 
                         dados foi constitu{\'{\i}}da de imagens ALOS/AVNIR fusionadas 
                         com imagens ALOS/PRISM e dados de relevo provenientes do banco de 
                         dados TOPODATA. A {\'a}rea de estudo corresponde ao 
                         munic{\'{\i}}pio de Nova Friburgo, com 933 kmē, localizado na 
                         regi{\~a}o serrana do estado do Rio de Janeiro. O {\'{\i}}ndice 
                         Kappa alcan{\c{c}}ado pela classifica{\c{c}}{\~a}o baseada em 
                         {\'a}rvore de decis{\~a}o composta por descritores 
                         estat{\'{\i}}sticos e texturais foi de 0,81, enquanto que este 
                         valor para a classifica{\c{c}}{\~a}o derivada apenas de 
                         descritores estat{\'{\i}}sticos foi de 0,84. Considerando os 
                         {\'{\i}}ndices alcan{\c{c}}ados, conclui-se que ambos os 
                         resultados apresentam excelente qualidade quanto {\`a} 
                         acur{\'a}cia da classifica{\c{c}}{\~a}o. O teste de 
                         hip{\'o}tese entre os dois {\'{\i}}ndices mostra, com 
                         n{\'{\i}}vel de signific{\^a}ncia de 5%, que n{\~a}o h{\'a} 
                         diferen{\c{c}}as entre as duas classifica{\c{c}}{\~o}es quanto 
                         {\`a} acur{\'a}cia. Palavras-Chave: Redes Sem{\^a}nticas; 
                         Classifica{\c{c}}{\~a}o de Imagens; Minera{\c{c}}{\~a}o de 
                         Dados; ALOS. ABSTRACT This paper aim at evaluating the performance 
                         of two semantic networks generated by data mining for classifying 
                         land cover using GEographic Object-Based Image Analysis (GEOBIA). 
                         The first one used statistical and texture attributes, and the 
                         second network employed only statistical attributes. The 
                         attributes were extracted from ALOS/AVNIR images pan-sharpened 
                         with ALOS/PRISM. Relief information provided by the TOPODATA 
                         geomorphometric database was also used as input data. The studied 
                         area is Nova Friburgo County, with an extension of 933 kmē, 
                         located in the mountainous region of Rio de Janeiro State. The 
                         Kappa index obtained by the classification based on statistical 
                         and texture attributes was 0.81, while the result for the 
                         classification derived only from statistical attributes achieved 
                         0.84. These values corroborate the excellent accuracy of both 
                         results. The statistical hypothesis test between the two indices 
                         at 95% confidence interval demonstrated that there is no 
                         difference between the two classification accuracies.",
                  doi = "10.1590/S1982-21702012000200008",
                  url = "http://dx.doi.org/10.1590/S1982-21702012000200008",
                 issn = "1413-4853",
             language = "en",
           targetfile = "08-1.pdf",
        urlaccessdate = "17 maio 2024"
}