Texture analysis for the segmentation of sugar cane multispectral images

Autores
Solano, Agustin; Schneider, Gerardo; Kemerer, Alejandra; Hadad, Alejandro Javier
Año de publicación
2014
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
In this paper is presented an analysis of the impact of texture features for segmentation of multispectral aerial images of sugar cane. Currently there are no precise techniques to estimate objectively areas of fallen cane and this causes significant losses in crop productivity and industrialization. For the real-ization of this work was made an image dataset. To build this dataset was im-plemented a software from which were obtained labeled regions in the images related to this agronomic phenomenon and then were extracted some texture features and a typical agronomic index (NDVI). The features related to segmen-tation task were analyzed with classical techniques such as Principal Compo-nent Analysis and Decision Trees. The results obtained show good performance to distinguish normal sugar cane versus fallen sugar cane but not between dif-ferent fallen sugar cane classes. However this approach was satisfactory to es-timate the normal and fallen sugar cane areas and this increase the information quality available to support agronomic decisions.
Sociedad Argentina de Informática e Investigación Operativa (SADIO)
Materia
Ciencias Informáticas
Ciencias Agrarias
sugar cane
multiespectral images
texture features
principal components analysis
Image databases
decision trees
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by/3.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/41995
Acceder
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network_name_str SEDICI (UNLP)
spelling Texture analysis for the segmentation of sugar cane multispectral imagesSolano, AgustinSchneider, GerardoKemerer, AlejandraHadad, Alejandro JavierCiencias InformáticasCiencias Agrariassugar canemultiespectral imagestexture featuresprincipal components analysisImage databasesdecision treesIn this paper is presented an analysis of the impact of texture features for segmentation of multispectral aerial images of sugar cane. Currently there are no precise techniques to estimate objectively areas of fallen cane and this causes significant losses in crop productivity and industrialization. For the real-ization of this work was made an image dataset. To build this dataset was im-plemented a software from which were obtained labeled regions in the images related to this agronomic phenomenon and then were extracted some texture features and a typical agronomic index (NDVI). The features related to segmen-tation task were analyzed with classical techniques such as Principal Compo-nent Analysis and Decision Trees. The results obtained show good performance to distinguish normal sugar cane versus fallen sugar cane but not between dif-ferent fallen sugar cane classes. However this approach was satisfactory to es-timate the normal and fallen sugar cane areas and this increase the information quality available to support agronomic decisions.Sociedad Argentina de Informática e Investigación Operativa (SADIO)2014-09info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf87-95http://sedici.unlp.edu.ar/handle/10915/41995enginfo:eu-repo/semantics/altIdentifier/url/http://43jaiio.sadio.org.ar/proceedings/CAI/8.pdfinfo:eu-repo/semantics/altIdentifier/issn/1851-2526info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/3.0/Creative Commons Attribution 3.0 Unported (CC BY 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-15T10:53:46Zoai:sedici.unlp.edu.ar:10915/41995Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-15 10:53:46.999SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Texture analysis for the segmentation of sugar cane multispectral images
title Texture analysis for the segmentation of sugar cane multispectral images
spellingShingle Texture analysis for the segmentation of sugar cane multispectral images
Solano, Agustin
Ciencias Informáticas
Ciencias Agrarias
sugar cane
multiespectral images
texture features
principal components analysis
Image databases
decision trees
title_short Texture analysis for the segmentation of sugar cane multispectral images
title_full Texture analysis for the segmentation of sugar cane multispectral images
title_fullStr Texture analysis for the segmentation of sugar cane multispectral images
title_full_unstemmed Texture analysis for the segmentation of sugar cane multispectral images
title_sort Texture analysis for the segmentation of sugar cane multispectral images
dc.creator.none.fl_str_mv Solano, Agustin
Schneider, Gerardo
Kemerer, Alejandra
Hadad, Alejandro Javier
author Solano, Agustin
author_facet Solano, Agustin
Schneider, Gerardo
Kemerer, Alejandra
Hadad, Alejandro Javier
author_role author
author2 Schneider, Gerardo
Kemerer, Alejandra
Hadad, Alejandro Javier
author2_role author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Ciencias Agrarias
sugar cane
multiespectral images
texture features
principal components analysis
Image databases
decision trees
topic Ciencias Informáticas
Ciencias Agrarias
sugar cane
multiespectral images
texture features
principal components analysis
Image databases
decision trees
dc.description.none.fl_txt_mv In this paper is presented an analysis of the impact of texture features for segmentation of multispectral aerial images of sugar cane. Currently there are no precise techniques to estimate objectively areas of fallen cane and this causes significant losses in crop productivity and industrialization. For the real-ization of this work was made an image dataset. To build this dataset was im-plemented a software from which were obtained labeled regions in the images related to this agronomic phenomenon and then were extracted some texture features and a typical agronomic index (NDVI). The features related to segmen-tation task were analyzed with classical techniques such as Principal Compo-nent Analysis and Decision Trees. The results obtained show good performance to distinguish normal sugar cane versus fallen sugar cane but not between dif-ferent fallen sugar cane classes. However this approach was satisfactory to es-timate the normal and fallen sugar cane areas and this increase the information quality available to support agronomic decisions.
Sociedad Argentina de Informática e Investigación Operativa (SADIO)
description In this paper is presented an analysis of the impact of texture features for segmentation of multispectral aerial images of sugar cane. Currently there are no precise techniques to estimate objectively areas of fallen cane and this causes significant losses in crop productivity and industrialization. For the real-ization of this work was made an image dataset. To build this dataset was im-plemented a software from which were obtained labeled regions in the images related to this agronomic phenomenon and then were extracted some texture features and a typical agronomic index (NDVI). The features related to segmen-tation task were analyzed with classical techniques such as Principal Compo-nent Analysis and Decision Trees. The results obtained show good performance to distinguish normal sugar cane versus fallen sugar cane but not between dif-ferent fallen sugar cane classes. However this approach was satisfactory to es-timate the normal and fallen sugar cane areas and this increase the information quality available to support agronomic decisions.
publishDate 2014
dc.date.none.fl_str_mv 2014-09
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/41995
url http://sedici.unlp.edu.ar/handle/10915/41995
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://43jaiio.sadio.org.ar/proceedings/CAI/8.pdf
info:eu-repo/semantics/altIdentifier/issn/1851-2526
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/3.0/
Creative Commons Attribution 3.0 Unported (CC BY 3.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/3.0/
Creative Commons Attribution 3.0 Unported (CC BY 3.0)
dc.format.none.fl_str_mv application/pdf
87-95
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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