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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

PHOTOGRAMMETRIC POINT CLOUD CLASSIFICATION BASED ON GEOMETRIC AND RADIOMETRIC DATA INTEGRATION

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Autor(es):
Pessoa, Guilherme Gomes [1] ; Amorim, Amilton [2] ; Galo, Mauricio [2] ; Bueno Trindade Galo, Maria de Lourdes [2]
Número total de Autores: 4
Afiliação do(s) autor(es):
[1] FCT UNESP Univ Estadual Paulista Julio de Mesquit, Programa Posgrad Ciencias Cartograf, Presidente Prudente, SP - Brazil
[2] FCT UNESP Univ Estadual Paulista Julio de Mesquit, Dept Cartog, Presidente Prudente, SP - Brazil
Número total de Afiliações: 2
Tipo de documento: Artigo Científico
Fonte: Bol. Ciênc. Geod.; v. 25, n. SI 2019.
Citações Web of Science: 0
Resumo

The extraction of information from point cloud is usually done after the application of classification methods based on the geometric characteristics of the objects. However, the classification of photogrammetric point clouds can be carried out using radiometric information combined with geometric information to minimize possible classification issues. With this in mind, this work proposes an approach to the classification of photogrammetric point cloud, generated by correspondence of aerial images acquired by Remotely Piloted Aircraft System (RPAS). The proposed approach for classifying photogrammetric point clouds consists of a pixel-supervised classification method, based on a decision tree. To achieve this, three data sets were used, one to define which attributes allow discrimination between the classes and the definition of the thresholds. Initially, several attributes were extracted based on a training sample. The average and standard deviation values for the attributes of each class extracted were used to guide the decision tree definition. The defined decision tree was applied to the other two point clouds to validate the approach and for thematic accuracy assessment. The quantitative analyses of the classifications based on kappa coefficient of agreement, applied to both validation areas, reached values higher than 0.938. (AU)

Processo FAPESP: 14/01841-1 - Utilização de imagens obtidas por aeromodelo para o cadastro de imóveis na área de expansão urbana
Beneficiário:Amilton Amorim
Modalidade de apoio: Auxílio à Pesquisa - Regular