Pixelwise Time Series Retrieval in Phenological Studies
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/IGARSS.2019.8898112 http://hdl.handle.net/11449/232954 |
Resumo: | The support of time series similarity searches might be crucial in phenology studies, in which long-term time series analysis based on the identification of similar and different phenological patterns shared by individuals belonging to different species is a widely common task. In this paper, we introduce the use of well-established Information Retrieval (IR) technologies in the search of time series. The solution comprises four main steps: extraction of an image-based time series representation; image content description to encode time series properties and patterns; textual signature extraction based on image content descriptions; and textual signature indexing using off-the-shelf IR approaches. In this paper, we demonstrate both the effectiveness and the efficiency of the proposed solution in time series retrieval problems related to the management of phenological data associated with near-surface vegetation images. |
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Repositório Institucional da UNESP |
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Pixelwise Time Series Retrieval in Phenological Studiesinformation retrievalphenologyrecurrence plottime series retrievalThe support of time series similarity searches might be crucial in phenology studies, in which long-term time series analysis based on the identification of similar and different phenological patterns shared by individuals belonging to different species is a widely common task. In this paper, we introduce the use of well-established Information Retrieval (IR) technologies in the search of time series. The solution comprises four main steps: extraction of an image-based time series representation; image content description to encode time series properties and patterns; textual signature extraction based on image content descriptions; and textual signature indexing using off-the-shelf IR approaches. In this paper, we demonstrate both the effectiveness and the efficiency of the proposed solution in time series retrieval problems related to the management of phenological data associated with near-surface vegetation images.University of Campinas Institute of ComputingUniversidade Estadual PaulistaUniversidade Estadual PaulistaUniversidade Estadual de Campinas (UNICAMP)Universidade Estadual Paulista (UNESP)Santos, Elisangela SilvaAlberton, Bruna [UNESP]Morellato, Leonor Patricia [UNESP]Da Silva Torres, Ricardo2022-04-30T22:28:33Z2022-04-30T22:28:33Z2019-07-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject6586-6589http://dx.doi.org/10.1109/IGARSS.2019.8898112International Geoscience and Remote Sensing Symposium (IGARSS), p. 6586-6589.http://hdl.handle.net/11449/23295410.1109/IGARSS.2019.88981122-s2.0-85077688255Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengInternational Geoscience and Remote Sensing Symposium (IGARSS)info:eu-repo/semantics/openAccess2022-04-30T22:28:33Zoai:repositorio.unesp.br:11449/232954Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T23:37:57.048590Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Pixelwise Time Series Retrieval in Phenological Studies |
title |
Pixelwise Time Series Retrieval in Phenological Studies |
spellingShingle |
Pixelwise Time Series Retrieval in Phenological Studies Santos, Elisangela Silva information retrieval phenology recurrence plot time series retrieval |
title_short |
Pixelwise Time Series Retrieval in Phenological Studies |
title_full |
Pixelwise Time Series Retrieval in Phenological Studies |
title_fullStr |
Pixelwise Time Series Retrieval in Phenological Studies |
title_full_unstemmed |
Pixelwise Time Series Retrieval in Phenological Studies |
title_sort |
Pixelwise Time Series Retrieval in Phenological Studies |
author |
Santos, Elisangela Silva |
author_facet |
Santos, Elisangela Silva Alberton, Bruna [UNESP] Morellato, Leonor Patricia [UNESP] Da Silva Torres, Ricardo |
author_role |
author |
author2 |
Alberton, Bruna [UNESP] Morellato, Leonor Patricia [UNESP] Da Silva Torres, Ricardo |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual de Campinas (UNICAMP) Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Santos, Elisangela Silva Alberton, Bruna [UNESP] Morellato, Leonor Patricia [UNESP] Da Silva Torres, Ricardo |
dc.subject.por.fl_str_mv |
information retrieval phenology recurrence plot time series retrieval |
topic |
information retrieval phenology recurrence plot time series retrieval |
description |
The support of time series similarity searches might be crucial in phenology studies, in which long-term time series analysis based on the identification of similar and different phenological patterns shared by individuals belonging to different species is a widely common task. In this paper, we introduce the use of well-established Information Retrieval (IR) technologies in the search of time series. The solution comprises four main steps: extraction of an image-based time series representation; image content description to encode time series properties and patterns; textual signature extraction based on image content descriptions; and textual signature indexing using off-the-shelf IR approaches. In this paper, we demonstrate both the effectiveness and the efficiency of the proposed solution in time series retrieval problems related to the management of phenological data associated with near-surface vegetation images. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-07-01 2022-04-30T22:28:33Z 2022-04-30T22:28:33Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1109/IGARSS.2019.8898112 International Geoscience and Remote Sensing Symposium (IGARSS), p. 6586-6589. http://hdl.handle.net/11449/232954 10.1109/IGARSS.2019.8898112 2-s2.0-85077688255 |
url |
http://dx.doi.org/10.1109/IGARSS.2019.8898112 http://hdl.handle.net/11449/232954 |
identifier_str_mv |
International Geoscience and Remote Sensing Symposium (IGARSS), p. 6586-6589. 10.1109/IGARSS.2019.8898112 2-s2.0-85077688255 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
International Geoscience and Remote Sensing Symposium (IGARSS) |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
6586-6589 |
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_ |
1808129537707343872 |