Potential of Texture Analysis for Charcoal Classification
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Floresta e Ambiente |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2179-80872019000300121 |
Resumo: | Abstract Charcoal produced from reforested wood can be distinguished from the charcoal derived from the wood of native species. This identification is very important for the trade, control and monitoring of charcoal production in Brazil. This study investigated the potential of texture analysis for classifying the charcoal based on origin (eucalyptus or native) and species. A total of 17 wood species were studied, five of which belonged to genus Eucalyptus and 12 were native to the Zona da Mata Mineira. Texture features based on the gray level co-occurrence matrix were extracted from digital images. The linear discriminant analysis was used to classify the images with these features. Employing 10 features, 96.2% accuracy was achieved for the classification by origin and 90.4% for the categorization by species. Texture analysis was shown to be a favorable and effective method that could facilitate the establishment of semiautomated techniques to classify the charcoal based on origin or species. |
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Floresta e Ambiente |
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Potential of Texture Analysis for Charcoal Classificationdiscriminant analysisgray level co-occurrence matriximage analysisAbstract Charcoal produced from reforested wood can be distinguished from the charcoal derived from the wood of native species. This identification is very important for the trade, control and monitoring of charcoal production in Brazil. This study investigated the potential of texture analysis for classifying the charcoal based on origin (eucalyptus or native) and species. A total of 17 wood species were studied, five of which belonged to genus Eucalyptus and 12 were native to the Zona da Mata Mineira. Texture features based on the gray level co-occurrence matrix were extracted from digital images. The linear discriminant analysis was used to classify the images with these features. Employing 10 features, 96.2% accuracy was achieved for the classification by origin and 90.4% for the categorization by species. Texture analysis was shown to be a favorable and effective method that could facilitate the establishment of semiautomated techniques to classify the charcoal based on origin or species.Instituto de Florestas da Universidade Federal Rural do Rio de Janeiro2019-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S2179-80872019000300121Floresta e Ambiente v.26 n.3 2019reponame:Floresta e Ambienteinstname:Universidade Federal do Rio de Janeiro (UFRJ)instacron:UFRJ10.1590/2179-8087.124117info:eu-repo/semantics/openAccessAndrade,Bruno Geike deVital,Benedito RochaCarneiro,Angélica de Cássia OliveiraBasso,Vanessa MariaPinto,Francisco de Assis de Carvalhoeng2019-06-18T00:00:00Zoai:scielo:S2179-80872019000300121Revistahttps://www.floram.org/PUBhttps://old.scielo.br/oai/scielo-oai.phpfloramjournal@gmail.com||floram@ufrrj.br||2179-80871415-0980opendoar:2019-06-18T00:00Floresta e Ambiente - Universidade Federal do Rio de Janeiro (UFRJ)false |
dc.title.none.fl_str_mv |
Potential of Texture Analysis for Charcoal Classification |
title |
Potential of Texture Analysis for Charcoal Classification |
spellingShingle |
Potential of Texture Analysis for Charcoal Classification Andrade,Bruno Geike de discriminant analysis gray level co-occurrence matrix image analysis |
title_short |
Potential of Texture Analysis for Charcoal Classification |
title_full |
Potential of Texture Analysis for Charcoal Classification |
title_fullStr |
Potential of Texture Analysis for Charcoal Classification |
title_full_unstemmed |
Potential of Texture Analysis for Charcoal Classification |
title_sort |
Potential of Texture Analysis for Charcoal Classification |
author |
Andrade,Bruno Geike de |
author_facet |
Andrade,Bruno Geike de Vital,Benedito Rocha Carneiro,Angélica de Cássia Oliveira Basso,Vanessa Maria Pinto,Francisco de Assis de Carvalho |
author_role |
author |
author2 |
Vital,Benedito Rocha Carneiro,Angélica de Cássia Oliveira Basso,Vanessa Maria Pinto,Francisco de Assis de Carvalho |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Andrade,Bruno Geike de Vital,Benedito Rocha Carneiro,Angélica de Cássia Oliveira Basso,Vanessa Maria Pinto,Francisco de Assis de Carvalho |
dc.subject.por.fl_str_mv |
discriminant analysis gray level co-occurrence matrix image analysis |
topic |
discriminant analysis gray level co-occurrence matrix image analysis |
description |
Abstract Charcoal produced from reforested wood can be distinguished from the charcoal derived from the wood of native species. This identification is very important for the trade, control and monitoring of charcoal production in Brazil. This study investigated the potential of texture analysis for classifying the charcoal based on origin (eucalyptus or native) and species. A total of 17 wood species were studied, five of which belonged to genus Eucalyptus and 12 were native to the Zona da Mata Mineira. Texture features based on the gray level co-occurrence matrix were extracted from digital images. The linear discriminant analysis was used to classify the images with these features. Employing 10 features, 96.2% accuracy was achieved for the classification by origin and 90.4% for the categorization by species. Texture analysis was shown to be a favorable and effective method that could facilitate the establishment of semiautomated techniques to classify the charcoal based on origin or species. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2179-80872019000300121 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2179-80872019000300121 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/2179-8087.124117 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Instituto de Florestas da Universidade Federal Rural do Rio de Janeiro |
publisher.none.fl_str_mv |
Instituto de Florestas da Universidade Federal Rural do Rio de Janeiro |
dc.source.none.fl_str_mv |
Floresta e Ambiente v.26 n.3 2019 reponame:Floresta e Ambiente instname:Universidade Federal do Rio de Janeiro (UFRJ) instacron:UFRJ |
instname_str |
Universidade Federal do Rio de Janeiro (UFRJ) |
instacron_str |
UFRJ |
institution |
UFRJ |
reponame_str |
Floresta e Ambiente |
collection |
Floresta e Ambiente |
repository.name.fl_str_mv |
Floresta e Ambiente - Universidade Federal do Rio de Janeiro (UFRJ) |
repository.mail.fl_str_mv |
floramjournal@gmail.com||floram@ufrrj.br|| |
_version_ |
1750128142833418240 |