文章基本信息

标题：ANÁLISE DA INCERTEZA NA REPRESENTAÇÃO DE CLASSES DE COBERTURA DO SOLO URBANO RESULTANTES DA APLICAÇÃO DE UMA REDE NEURAL ARTIFICIAL
本地全文：下载
作者：Letícia Sabo Boschi ; Maria de Lourdes Bueno Trindade Galo
期刊名称：Boletim de Ciências Geodésicas
印刷版ISSN：1982-2170
出版年度：2007
卷号：13
期号：1
语种：Portuguese
出版社：Universidade Federal do Paraná-UFPR
摘要：The great diversity of materials that characterizes the urban environment determines a structure of mixed classes in a classification of multiespectral images. In that sense, it is important to define an appropriate classification system using a non parametric classifier, that allows incorporating non spectral (such as texture) data to the process. They also allow analyzing the uncertainty associated to each class from the output values of the network calculated in relation to each class. Considering these properties, an experiment was carried out. This experiment consisted in the application of an Artificial Neural Network aiming at the classification of the urban land cover of Presidente Prudente and the analysis of the uncertainty in the representation of the mapped thematic classes. The results showed that it is possible to discriminate the variations in the urban land cover through the application of an Artificial Neural Network. It was also possible to visualize the spatial variation of the uncertainty in the attribution of classes of urban land cover from the generated representations. The class characterized by a defined pattern as intermediary related to the impermeability of the urban soil presented larger ambiguity degree and, therefore, larger mixture. Keywords: Classification of urban environment, Artificial Neural Networks, Uncertainty in the classification, Remote Sensing.
其他摘要：The great diversity of materials that characterizes the urban environment determinesa structure of mixed classes in a classification of multiespectral images. In thatsense, it is important to define an appropriate classification system using a nonparametric classifier, that allows incorporating non spectral (such as texture) data tothe process. They also allow analyzing the uncertainty associated to each class fromthe output values of the network calculated in relation to each class. Consideringthese properties, an experiment was carried out. This experiment consisted in theapplication of an Artificial Neural Network aiming at the classification of the urbanland cover of Presidente Prudente and the analysis of the uncertainty in therepresentation of the mapped thematic classes. The results showed that it is possibleto discriminate the variations in the urban land cover through the application of anArtificial Neural Network. It was also possible to visualize the spatial variation ofthe uncertainty in the attribution of classes of urban land cover from the generatedrepresentations. The class characterized by a defined pattern as intermediary relatedto the impermeability of the urban soil presented larger ambiguity degree and,therefore, larger mixture.Keywords: Classification of urban environment, Artificial Neural Networks,Uncertainty in the classification, Remote Sensing.
关键词：Classificação de ambientes urbanos, Redes Neurais Artificiais, Incerteza na classificação, Sensoriamento Remoto;Classification of urban environment, Artificial Neural Networks, Uncertainty in the classification, Remote Sensing