dc.contributorMascarenhas, Nelson Delfino d'Ávila
dc.contributorhttp://lattes.cnpq.br/0557976975338451
dc.contributorhttp://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=H311648
dc.creatorLevada, Alexandre Luís Magalhães
dc.date.accessioned2006-07-26
dc.date.accessioned2016-06-02T19:05:18Z
dc.date.available2006-07-26
dc.date.available2016-06-02T19:05:18Z
dc.date.created2006-07-26
dc.date.created2016-06-02T19:05:18Z
dc.date.issued2006-02-22
dc.identifierLEVADA, Alexandre Luís Magalhães. Extração de atributos em imagens de sensoriamento remoto utilizando Independent Component Analysis e combinação de métodos lineares.. 2006. 104 f. Dissertação (Mestrado em Ciências Exatas e da Terra) - Universidade Federal de São Carlos, São Carlos, 2006.
dc.identifierhttps://repositorio.ufscar.br/handle/ufscar/318
dc.description.abstractMethods for feature extraction represent an important stage in statistical pattern recognition applications. In this work we present how to improve classification performance creating a feature fusion framework to combine second and higher order statistical methods, avoiding existing limitations of the individual approaches and problems as ill-conditioned behavior, which may cause unstable results during the estimation of the independent components (whitening process) and eventual noise amplifications. The resulting scheme is used to combine features obtained from a variety of methods into a unique feature vector defining two approaches: Concatenated and Hierarquical Feature Fusion. The methods are tested on both multispectral and hyperspectral remote sensing images, which are classified using the maxver (maximum likelihood) approach. Results indicate that the technique outperforms the usual methods in some cases, providing a valid useful tool for multivariate data analysis and classification.
dc.publisherUniversidade Federal de São Carlos
dc.publisherBR
dc.publisherUFSCar
dc.publisherPrograma de Pós-Graduação em Ciência da Computação - PPGCC
dc.rightsAcesso Aberto
dc.subjectReconhecimento de padrões
dc.subjectSensoriamento remoto
dc.subjectSeleção de atributos
dc.titleExtração de atributos em imagens de sensoriamento remoto utilizando Independent Component Analysis e combinação de métodos lineares
dc.typeTesis


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