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

Fil: Vallejos, Ronny. Universidad Técnica Federico Santa María. Departamento de Matemática; Chile.

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Autores principales: Vallejos, Ronny, Mallea, Adriana, Herrera, Myriam, Ojeda, Silvia María
Formato: publishedVersion article
Lenguaje:Inglés
Publicado: 2022
Materias:
Multivariate spatial process
Spatial association
Codispersion matrix
Dimensionality reduction
Image classification
Acceso en línea:http://hdl.handle.net/11086/27180
https://doi.org/10.1007/s00477-014-0884-5
https://doi.org/10.1007/s00477-014-0884-5
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Repositorio Digital Universitario (UNC) de Universidad Nacional de Córdoba
id I10-R141-11086-27180
record_format dspace
spelling I10-R141-11086-271802023-08-31T13:16:42Z A multivariate geostatistical approach for landscape classification from remotely sensed image data Vallejos, Ronny Mallea, Adriana Herrera, Myriam Ojeda, Silvia María Multivariate spatial process Spatial association Codispersion matrix Dimensionality reduction Image classification 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. 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 2022-07-12T14:08:33Z 2015 article http://hdl.handle.net/11086/27180 https://doi.org/10.1007/s00477-014-0884-5 https://doi.org/10.1007/s00477-014-0884-5 eng Attribution-NonCommercial-NoDerivatives 4.0 International restrictedAccess http://creativecommons.org/licenses/by-nc-nd/4.0/ Impreso; Electrónico y/o Digital ISSN: 1436-3240
institution Universidad Nacional de Córdoba
institution_str I-10
repository_str R-141
collection Repositorio Digital Universitario (UNC)
language Inglés
topic Multivariate spatial process
Spatial association
Codispersion matrix
Dimensionality reduction
Image classification
spellingShingle Multivariate spatial process
Spatial association
Codispersion matrix
Dimensionality reduction
Image classification
Vallejos, Ronny
Mallea, Adriana
Herrera, Myriam
Ojeda, Silvia María
A multivariate geostatistical approach for landscape classification from remotely sensed image data
topic_facet Multivariate spatial process
Spatial association
Codispersion matrix
Dimensionality reduction
Image classification
description Fil: Vallejos, Ronny. Universidad Técnica Federico Santa María. Departamento de Matemática; Chile.
format publishedVersion
article
author Vallejos, Ronny
Mallea, Adriana
Herrera, Myriam
Ojeda, Silvia María
author_facet Vallejos, Ronny
Mallea, Adriana
Herrera, Myriam
Ojeda, Silvia María
author_sort Vallejos, Ronny
title A multivariate geostatistical approach for landscape classification from remotely sensed image data
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 multivariate geostatistical approach for landscape classification from remotely sensed image data
publishDate 2022
url http://hdl.handle.net/11086/27180
https://doi.org/10.1007/s00477-014-0884-5
https://doi.org/10.1007/s00477-014-0884-5
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