ANÁLISE ORIENTADA A OBJETO GEOGRÁFICO NA CARACTERIZAÇÃO DO USO E OCUPAÇÃO DA TERRA EM SEGMENTOS DO RIO PITANGUI, PARANÁ: AVALIAÇÕES PRELIMINARES

• Ano de defesa: 2015
• Autor(a) principal: Antunes, Dinameres Aparecida
• Orientador(a): Ribeiro, Selma Regina Aranha
• Banca de defesa: Luz, Naissa Batista da , Calegari, Marcia Regina
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: UNIVERSIDADE ESTADUAL DE PONTA GROSSA
• Programa de Pós-Graduação: Programa de Pós Graduação Mestrado em Gestão do Território
• Departamento: Gestão do Território : Sociedade e Natureza
• País: BR
• Palavras-chave em Português: GEOBIA; classificação supervisionada; análise multivariada
• Palavras-chave em Inglês: GEOBIA; supervised classification; multivariate analysis
• Área do conhecimento CNPq: CNPQ::CIENCIAS HUMANAS::GEOGRAFIA
• Link de acesso: http://tede2.uepg.br/jspui/handle/prefix/580

Our major goal is through GEOBIA (Geographic Object Based Image Analysis) obtain the characterization of use and land occupation in two different zones nearby Pitangui’s river, situated among the cities of Castro, Carambeí and Ponta Grossa, Paraná State. Nowadays that is a growing demand of remote sensing images with high spatial resolution. As a result, there is a need to create new methodologies for digital image processing. Here we can mention GEOBIA, which has some advantages, such as a polygon generation for each geographical object created, relational data base with several descriptors, as well as, the possibility to use spectral, spatial and texture information. Yet we have been used the Principal Components Analysis (PCA) and the Cluster Analysis (CA) in order to reduce the size of our training samples from the relational data base, which are trained by means of supervised classification. With this, we aim to select descriptors that could bring the best results for our classification. We have obtained different geographic object oriented supervised classifications, whereas the descriptors were selected from PCA, also from PCA together with CA, as well as, all descriptors have been selected from GEOBIA. We have concluded that the best results for the two work zones were obtain from the descriptors selected from PCA together with CA. In work zone 1 we have obtained 95,68% of general precision in the confusion matrix and 0,95 in the kappa index. While, in work zone 2 we have obtained 93,85% in the confusion matrix and 0,93 in the kappa index. These numbers are consider outstanding in the literature. With this, we show that descriptors selection for geographic object oriented classification is an important approach, since there are descriptors with redundant information that could mislead the classification result.