A multivariate geostatistical approach for landscape classification from remotely sensed image data

Autores
Vallejos, Ronny; Mallea, Adriana; Herrera, Myriam; Ojeda, Silvia María
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Fil: Vallejos, Ronny. Universidad Técnica Federico Santa María. Departamento de Matemática; Chile.
Fil: Mallea, Adriana. Universidad Nacional de San Juan. Departamento de Matemática; Argentina.
Fil: Herrera, Myriam. Universidad Nacional de San Juan. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina.
Fil: Ojeda, Silvia María. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.
This paper proposes a methodology to address the classification of images that have been acquired from remote sensors. One common problem in classification is the high dimensionality of multivariate characteristics. The methodology we propose consists of reducing the dimensionality of the spectral bands associated with a multispectral satellite image. Such dimensionality reduction is accomplished by the use of the divergence of a modified Mahalanobis distance. Instead of using the covariance matrix of a multivariate spatial process, the codispersion matrix is considered which have some desirable asymptotic properties under very precise conditions. The consistency and asymptotic normality hold for a general class of processes that are a natural extension of the one-dimensional spatial processes for which the asymptotic properties were first established. The results allow the selection of a set of spectral bands to produce the highest value of divergence. Then, a supervised maximum likelihood method using the selected spectral bands is employed for landscape classification. An application with a real LANDSAT image is introduced to explore and visualize how our method works in practice.
publishedVersion
Fil: Vallejos, Ronny. Universidad Técnica Federico Santa María. Departamento de Matemática; Chile.
Fil: Mallea, Adriana. Universidad Nacional de San Juan. Departamento de Matemática; Argentina.
Fil: Herrera, Myriam. Universidad Nacional de San Juan. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina.
Fil: Ojeda, Silvia María. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.
Estadística y Probabilidad
Fuente
ISSN: 1436-3240
Materia
Multivariate spatial process
Spatial association
Codispersion matrix
Dimensionality reduction
Image classification
Nivel de accesibilidad
acceso abierto
Condiciones de uso
Repositorio
Repositorio Digital Universitario (UNC)
Institución
Universidad Nacional de Córdoba
OAI Identificador
oai:rdu.unc.edu.ar:11086/27180
Acceder
id RDUUNC_a884b606102f794428e65bff7bed23e7
oai_identifier_str oai:rdu.unc.edu.ar:11086/27180
network_acronym_str RDUUNC
repository_id_str 2572
network_name_str Repositorio Digital Universitario (UNC)
spelling A multivariate geostatistical approach for landscape classification from remotely sensed image dataVallejos, RonnyMallea, AdrianaHerrera, MyriamOjeda, Silvia MaríaMultivariate spatial processSpatial associationCodispersion matrixDimensionality reductionImage classificationFil: Vallejos, Ronny. Universidad Técnica Federico Santa María. Departamento de Matemática; Chile.Fil: Mallea, Adriana. Universidad Nacional de San Juan. Departamento de Matemática; Argentina.Fil: Herrera, Myriam. Universidad Nacional de San Juan. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina.Fil: Ojeda, Silvia María. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.This paper proposes a methodology to address the classification of images that have been acquired from remote sensors. One common problem in classification is the high dimensionality of multivariate characteristics. The methodology we propose consists of reducing the dimensionality of the spectral bands associated with a multispectral satellite image. Such dimensionality reduction is accomplished by the use of the divergence of a modified Mahalanobis distance. Instead of using the covariance matrix of a multivariate spatial process, the codispersion matrix is considered which have some desirable asymptotic properties under very precise conditions. The consistency and asymptotic normality hold for a general class of processes that are a natural extension of the one-dimensional spatial processes for which the asymptotic properties were first established. The results allow the selection of a set of spectral bands to produce the highest value of divergence. Then, a supervised maximum likelihood method using the selected spectral bands is employed for landscape classification. An application with a real LANDSAT image is introduced to explore and visualize how our method works in practice.publishedVersionFil: Vallejos, Ronny. Universidad Técnica Federico Santa María. Departamento de Matemática; Chile.Fil: Mallea, Adriana. Universidad Nacional de San Juan. Departamento de Matemática; Argentina.Fil: Herrera, Myriam. Universidad Nacional de San Juan. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina.Fil: Ojeda, Silvia María. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.Estadística y Probabilidad2015info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/11086/27180https://doi.org/10.1007/s00477-014-0884-5https://doi.org/10.1007/s00477-014-0884-5ISSN: 1436-3240reponame:Repositorio Digital Universitario (UNC)instname:Universidad Nacional de Córdobainstacron:UNCenginfo:eu-repo/semantics/openAccess2025-09-29T13:42:56Zoai:rdu.unc.edu.ar:11086/27180Institucionalhttps://rdu.unc.edu.ar/Universidad públicaNo correspondehttp://rdu.unc.edu.ar/oai/snrdoca.unc@gmail.comArgentinaNo correspondeNo correspondeNo correspondeopendoar:25722025-09-29 13:42:56.514Repositorio Digital Universitario (UNC) - Universidad Nacional de Córdobafalse
dc.title.none.fl_str_mv A multivariate geostatistical approach for landscape classification from remotely sensed image data
title A multivariate geostatistical approach for landscape classification from remotely sensed image data
spellingShingle A multivariate geostatistical approach for landscape classification from remotely sensed image data
Vallejos, Ronny
Multivariate spatial process
Spatial association
Codispersion matrix
Dimensionality reduction
Image classification
title_short A multivariate geostatistical approach for landscape classification from remotely sensed image data
title_full A multivariate geostatistical approach for landscape classification from remotely sensed image data
title_fullStr A multivariate geostatistical approach for landscape classification from remotely sensed image data
title_full_unstemmed A multivariate geostatistical approach for landscape classification from remotely sensed image data
title_sort A multivariate geostatistical approach for landscape classification from remotely sensed image data
dc.creator.none.fl_str_mv Vallejos, Ronny
Mallea, Adriana
Herrera, Myriam
Ojeda, Silvia María
author Vallejos, Ronny
author_facet Vallejos, Ronny
Mallea, Adriana
Herrera, Myriam
Ojeda, Silvia María
author_role author
author2 Mallea, Adriana
Herrera, Myriam
Ojeda, Silvia María
author2_role author
author
author
dc.subject.none.fl_str_mv Multivariate spatial process
Spatial association
Codispersion matrix
Dimensionality reduction
Image classification
topic Multivariate spatial process
Spatial association
Codispersion matrix
Dimensionality reduction
Image classification
dc.description.none.fl_txt_mv Fil: Vallejos, Ronny. Universidad Técnica Federico Santa María. Departamento de Matemática; Chile.
Fil: Mallea, Adriana. Universidad Nacional de San Juan. Departamento de Matemática; Argentina.
Fil: Herrera, Myriam. Universidad Nacional de San Juan. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina.
Fil: Ojeda, Silvia María. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.
This paper proposes a methodology to address the classification of images that have been acquired from remote sensors. One common problem in classification is the high dimensionality of multivariate characteristics. The methodology we propose consists of reducing the dimensionality of the spectral bands associated with a multispectral satellite image. Such dimensionality reduction is accomplished by the use of the divergence of a modified Mahalanobis distance. Instead of using the covariance matrix of a multivariate spatial process, the codispersion matrix is considered which have some desirable asymptotic properties under very precise conditions. The consistency and asymptotic normality hold for a general class of processes that are a natural extension of the one-dimensional spatial processes for which the asymptotic properties were first established. The results allow the selection of a set of spectral bands to produce the highest value of divergence. Then, a supervised maximum likelihood method using the selected spectral bands is employed for landscape classification. An application with a real LANDSAT image is introduced to explore and visualize how our method works in practice.
publishedVersion
Fil: Vallejos, Ronny. Universidad Técnica Federico Santa María. Departamento de Matemática; Chile.
Fil: Mallea, Adriana. Universidad Nacional de San Juan. Departamento de Matemática; Argentina.
Fil: Herrera, Myriam. Universidad Nacional de San Juan. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina.
Fil: Ojeda, Silvia María. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.
Estadística y Probabilidad
description Fil: Vallejos, Ronny. Universidad Técnica Federico Santa María. Departamento de Matemática; Chile.
publishDate 2015
dc.date.none.fl_str_mv 2015
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11086/27180
https://doi.org/10.1007/s00477-014-0884-5
https://doi.org/10.1007/s00477-014-0884-5
url http://hdl.handle.net/11086/27180
https://doi.org/10.1007/s00477-014-0884-5
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv ISSN: 1436-3240
reponame:Repositorio Digital Universitario (UNC)
instname:Universidad Nacional de Córdoba
instacron:UNC
reponame_str Repositorio Digital Universitario (UNC)
collection Repositorio Digital Universitario (UNC)
instname_str Universidad Nacional de Córdoba
instacron_str UNC
institution UNC
repository.name.fl_str_mv Repositorio Digital Universitario (UNC) - Universidad Nacional de Córdoba
repository.mail.fl_str_mv oca.unc@gmail.com
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score 13.070432

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