Avalia？？o de desempenho de atributos estatísticos e texturais em uma classifica？？o de cobertura da terra baseada em objeto

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 km2, 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.