PHOTOGRAMMETRIC POINT CLOUD CLASSIFICATION BASED ON GEOMETRIC AND RADIOMETRIC DATA INTEGRATION
Data
2019-01-01
Autores
Pessoa, Guilherme Gomes [UNESP]
Amorim, Amilton [UNESP]
Galo, Mauricio [UNESP]
Bueno Trindade Galo, Maria de Lourdes [UNESP]
Título da Revista
ISSN da Revista
Título de Volume
Editor
Univ Federal Parana, Centro Politecnico
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.
Descrição
Palavras-chave
Classification, Photogrammetric Point Cloud, RPAS
Como citar
Boletim De Ciencias Geodesicas. Curitiba Pr: Univ Federal Parana, Centro Politecnico, v. 25, 17 p., 2019.