PhenoVis – A tool for visual phenological analysis of digital camera images using chronological percentage maps
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1016/j.ins.2016.08.052 http://hdl.handle.net/11449/178321 |
Resumo: | PhenoVis is framework for the visual phenological analysis of forest ecosystems. It contains the chronological percentage maps (CPM), a novel representation that is capable of discovering additional patterns by encoding percentage distributions of the data. Two types of masks are used in PhenoVis: a community mask , which considers all plant species in the image; and a species mask , associated with a given plant species. Among the several images taken at different times of the day, the image taken at noon is preferred for the analysis because it minimizes shadow effects. Therefore, only one image per day is used. The analysis considers the chromatic co- efficients associated with each pixel in the image. In PhenoVis we associate different colors with each bucket of the percentage histogram. The histogram granularity defines the size of a given bucket of the percentage distribution. The number of buckets is given by the number of colors available, and the range of the distribution is given by the IOI. The percentage map of a single input image consists of a normalized stacked bar chart. The chronological percentage map consists of a sequence of percentage maps stacked in chronological order, from top to bottom (portrait) or left to right (landscape). |
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Repositório Institucional da UNESP |
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spelling |
PhenoVis – A tool for visual phenological analysis of digital camera images using chronological percentage mapsPhenologyRemote sensingSimilarity rankingVegetation indexVisual analyticsPhenoVis is framework for the visual phenological analysis of forest ecosystems. It contains the chronological percentage maps (CPM), a novel representation that is capable of discovering additional patterns by encoding percentage distributions of the data. Two types of masks are used in PhenoVis: a community mask , which considers all plant species in the image; and a species mask , associated with a given plant species. Among the several images taken at different times of the day, the image taken at noon is preferred for the analysis because it minimizes shadow effects. Therefore, only one image per day is used. The analysis considers the chromatic co- efficients associated with each pixel in the image. In PhenoVis we associate different colors with each bucket of the percentage histogram. The histogram granularity defines the size of a given bucket of the percentage distribution. The number of buckets is given by the number of colors available, and the range of the distribution is given by the IOI. The percentage map of a single input image consists of a normalized stacked bar chart. The chronological percentage map consists of a sequence of percentage maps stacked in chronological order, from top to bottom (portrait) or left to right (landscape).Institute of Informatics Federal University of Rio Grande do Sul – UFRGSInstitute of Science and Technology Federal University of São Paulo – UNIFESPDept. of Botany São Paulo State University – UNESPInstitute of Computing University of Campinas – UNICAMPDept. of Botany São Paulo State University – UNESPFederal University of Rio Grande do Sul – UFRGSUniversidade de São Paulo (USP)Universidade Estadual Paulista (Unesp)Universidade Estadual de Campinas (UNICAMP)Leite, Roger A.Schnorr, Lucas MelloAlmeida, JurandyAlberton, Bruna [UNESP]Morellato, Leonor Patricia C. [UNESP]Torres, Ricardo da S.Comba, João L.D.2018-12-11T17:29:46Z2018-12-11T17:29:46Z2016-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article181-195application/pdfhttp://dx.doi.org/10.1016/j.ins.2016.08.052Information Sciences, v. 372, p. 181-195.0020-0255http://hdl.handle.net/11449/17832110.1016/j.ins.2016.08.0522-s2.0-849899648882-s2.0-84989964888.pdfScopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengInformation Sciences1,635info:eu-repo/semantics/openAccess2023-11-18T06:14:52Zoai:repositorio.unesp.br:11449/178321Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T18:04:40.302787Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
PhenoVis – A tool for visual phenological analysis of digital camera images using chronological percentage maps |
title |
PhenoVis – A tool for visual phenological analysis of digital camera images using chronological percentage maps |
spellingShingle |
PhenoVis – A tool for visual phenological analysis of digital camera images using chronological percentage maps Leite, Roger A. Phenology Remote sensing Similarity ranking Vegetation index Visual analytics |
title_short |
PhenoVis – A tool for visual phenological analysis of digital camera images using chronological percentage maps |
title_full |
PhenoVis – A tool for visual phenological analysis of digital camera images using chronological percentage maps |
title_fullStr |
PhenoVis – A tool for visual phenological analysis of digital camera images using chronological percentage maps |
title_full_unstemmed |
PhenoVis – A tool for visual phenological analysis of digital camera images using chronological percentage maps |
title_sort |
PhenoVis – A tool for visual phenological analysis of digital camera images using chronological percentage maps |
author |
Leite, Roger A. |
author_facet |
Leite, Roger A. Schnorr, Lucas Mello Almeida, Jurandy Alberton, Bruna [UNESP] Morellato, Leonor Patricia C. [UNESP] Torres, Ricardo da S. Comba, João L.D. |
author_role |
author |
author2 |
Schnorr, Lucas Mello Almeida, Jurandy Alberton, Bruna [UNESP] Morellato, Leonor Patricia C. [UNESP] Torres, Ricardo da S. Comba, João L.D. |
author2_role |
author author author author author author |
dc.contributor.none.fl_str_mv |
Federal University of Rio Grande do Sul – UFRGS Universidade de São Paulo (USP) Universidade Estadual Paulista (Unesp) Universidade Estadual de Campinas (UNICAMP) |
dc.contributor.author.fl_str_mv |
Leite, Roger A. Schnorr, Lucas Mello Almeida, Jurandy Alberton, Bruna [UNESP] Morellato, Leonor Patricia C. [UNESP] Torres, Ricardo da S. Comba, João L.D. |
dc.subject.por.fl_str_mv |
Phenology Remote sensing Similarity ranking Vegetation index Visual analytics |
topic |
Phenology Remote sensing Similarity ranking Vegetation index Visual analytics |
description |
PhenoVis is framework for the visual phenological analysis of forest ecosystems. It contains the chronological percentage maps (CPM), a novel representation that is capable of discovering additional patterns by encoding percentage distributions of the data. Two types of masks are used in PhenoVis: a community mask , which considers all plant species in the image; and a species mask , associated with a given plant species. Among the several images taken at different times of the day, the image taken at noon is preferred for the analysis because it minimizes shadow effects. Therefore, only one image per day is used. The analysis considers the chromatic co- efficients associated with each pixel in the image. In PhenoVis we associate different colors with each bucket of the percentage histogram. The histogram granularity defines the size of a given bucket of the percentage distribution. The number of buckets is given by the number of colors available, and the range of the distribution is given by the IOI. The percentage map of a single input image consists of a normalized stacked bar chart. The chronological percentage map consists of a sequence of percentage maps stacked in chronological order, from top to bottom (portrait) or left to right (landscape). |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-01-01 2018-12-11T17:29:46Z 2018-12-11T17:29:46Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1016/j.ins.2016.08.052 Information Sciences, v. 372, p. 181-195. 0020-0255 http://hdl.handle.net/11449/178321 10.1016/j.ins.2016.08.052 2-s2.0-84989964888 2-s2.0-84989964888.pdf |
url |
http://dx.doi.org/10.1016/j.ins.2016.08.052 http://hdl.handle.net/11449/178321 |
identifier_str_mv |
Information Sciences, v. 372, p. 181-195. 0020-0255 10.1016/j.ins.2016.08.052 2-s2.0-84989964888 2-s2.0-84989964888.pdf |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Information Sciences 1,635 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
181-195 application/pdf |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128890822983680 |