PHOTOGRAMMETRIC POINT CLOUD CLASSIFICATION BASED ON GEOMETRIC AND RADIOMETRIC DATA INTEGRATION
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Boletim de Ciências Geodésicas |
Texto Completo: | https://revistas.ufpr.br/bcg/article/view/69017 |
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 |
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Boletim de Ciências Geodésicas |
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PHOTOGRAMMETRIC POINT CLOUD CLASSIFICATION BASED ON GEOMETRIC AND RADIOMETRIC DATA INTEGRATIONThe 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 0.938Boletim de Ciências GeodésicasBulletin of Geodetic SciencesPessoa, Guilherme GomesAmorim, AmiltonGalo, MauricioGalo, Maria de Lourdes Bueno Trindade2019-09-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://revistas.ufpr.br/bcg/article/view/69017Boletim de Ciências Geodésicas; Vol 25, No 2 SPE (2019)Bulletin of Geodetic Sciences; Vol 25, No 2 SPE (2019)1982-21701413-4853reponame:Boletim de Ciências Geodésicasinstname:Universidade Federal do Paraná (UFPR)instacron:UFPRporhttps://revistas.ufpr.br/bcg/article/view/69017/39423Copyright (c) 2019 Guilherme Gomes Pessoa, Amilton Amorim, Mauricio Galo, Maria de Lourdes Bueno Trindade Galohttp://creativecommons.org/licenses/by-nc/4.0info:eu-repo/semantics/openAccess2021-09-14T14:34:42Zoai:revistas.ufpr.br:article/69017Revistahttps://revistas.ufpr.br/bcgPUBhttps://revistas.ufpr.br/bcg/oaiqdalmolin@ufpr.br|| danielsantos@ufpr.br||qdalmolin@ufpr.br|| danielsantos@ufpr.br1982-21701413-4853opendoar:2021-09-14T14:34:42Boletim de Ciências Geodésicas - Universidade Federal do Paraná (UFPR)false |
dc.title.none.fl_str_mv |
PHOTOGRAMMETRIC POINT CLOUD CLASSIFICATION BASED ON GEOMETRIC AND RADIOMETRIC DATA INTEGRATION |
title |
PHOTOGRAMMETRIC POINT CLOUD CLASSIFICATION BASED ON GEOMETRIC AND RADIOMETRIC DATA INTEGRATION |
spellingShingle |
PHOTOGRAMMETRIC POINT CLOUD CLASSIFICATION BASED ON GEOMETRIC AND RADIOMETRIC DATA INTEGRATION Pessoa, Guilherme Gomes |
title_short |
PHOTOGRAMMETRIC POINT CLOUD CLASSIFICATION BASED ON GEOMETRIC AND RADIOMETRIC DATA INTEGRATION |
title_full |
PHOTOGRAMMETRIC POINT CLOUD CLASSIFICATION BASED ON GEOMETRIC AND RADIOMETRIC DATA INTEGRATION |
title_fullStr |
PHOTOGRAMMETRIC POINT CLOUD CLASSIFICATION BASED ON GEOMETRIC AND RADIOMETRIC DATA INTEGRATION |
title_full_unstemmed |
PHOTOGRAMMETRIC POINT CLOUD CLASSIFICATION BASED ON GEOMETRIC AND RADIOMETRIC DATA INTEGRATION |
title_sort |
PHOTOGRAMMETRIC POINT CLOUD CLASSIFICATION BASED ON GEOMETRIC AND RADIOMETRIC DATA INTEGRATION |
author |
Pessoa, Guilherme Gomes |
author_facet |
Pessoa, Guilherme Gomes Amorim, Amilton Galo, Mauricio Galo, Maria de Lourdes Bueno Trindade |
author_role |
author |
author2 |
Amorim, Amilton Galo, Mauricio Galo, Maria de Lourdes Bueno Trindade |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
|
dc.contributor.author.fl_str_mv |
Pessoa, Guilherme Gomes Amorim, Amilton Galo, Mauricio Galo, Maria de Lourdes Bueno Trindade |
dc.subject.none.fl_str_mv |
|
description |
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 |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-09-04 |
dc.type.none.fl_str_mv |
|
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://revistas.ufpr.br/bcg/article/view/69017 |
url |
https://revistas.ufpr.br/bcg/article/view/69017 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://revistas.ufpr.br/bcg/article/view/69017/39423 |
dc.rights.driver.fl_str_mv |
http://creativecommons.org/licenses/by-nc/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
http://creativecommons.org/licenses/by-nc/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Boletim de Ciências Geodésicas Bulletin of Geodetic Sciences |
publisher.none.fl_str_mv |
Boletim de Ciências Geodésicas Bulletin of Geodetic Sciences |
dc.source.none.fl_str_mv |
Boletim de Ciências Geodésicas; Vol 25, No 2 SPE (2019) Bulletin of Geodetic Sciences; Vol 25, No 2 SPE (2019) 1982-2170 1413-4853 reponame:Boletim de Ciências Geodésicas instname:Universidade Federal do Paraná (UFPR) instacron:UFPR |
instname_str |
Universidade Federal do Paraná (UFPR) |
instacron_str |
UFPR |
institution |
UFPR |
reponame_str |
Boletim de Ciências Geodésicas |
collection |
Boletim de Ciências Geodésicas |
repository.name.fl_str_mv |
Boletim de Ciências Geodésicas - Universidade Federal do Paraná (UFPR) |
repository.mail.fl_str_mv |
qdalmolin@ufpr.br|| danielsantos@ufpr.br||qdalmolin@ufpr.br|| danielsantos@ufpr.br |
_version_ |
1799771719997587456 |