A New Approach to Segmentation of Multispectral Remote Sensing Images Based on MRF

Autores
Baumgartner, Josef Sylvester; Gimenez Romero, Javier Alejandro; Scavuzzo, Marcelo; Pucheta, Julián Antonio
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Segmentation of multispectral remote sensing images is a key competence for a great variety of applications. Many of the applied segmentation algorithms are generative models based on Markov random fields. These approaches are generally limited to multivariate probability densities like the normal distribution. Besides that, it is usually impossible to adjust the contextual parameters separately for each frequency band. In this work, we present a new segmentation algorithm that avoids the mentioned problems and allows the use of any univariate density function as emission probability in each band. The approach consists of three steps: First, calculate feature vectors for each frequency band. Second, estimate contextual parameters for each band and apply local smoothing. Third, merge the feature vectors of the frequency bands to obtain a final segmentation. This procedure can be iterated but experiments show, that after the first iteration most of the pixels are already in their final state. We call our approach Successive Band Merging (SBM). To evaluate the performance of SBM, we segment a Landsat 8 and an AVIRIS image. In both cases, the κ coefficients show, that SBM outperforms the benchmark algorithms.
Fil: Baumgartner, Josef Sylvester. Universidad Nacional de Cordoba. Facultad de Cs.exactas Fisicas y Naturales. Departamento de Electronica; Argentina
Fil: Gimenez Romero, Javier Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico San Juan. Instituto de Automática; Argentina
Fil: Scavuzzo, Marcelo. Comision Nacional de Actividades Espaciales; Argentina
Fil: Pucheta, Julián Antonio. Universidad Nacional de Cordoba. Facultad de Cs.exactas Fisicas y Naturales. Departamento de Electronica; Argentina
Materia
Image Segmentation
Markov Random Fields
Multispectral Imaging
Probability Density Function
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/7511

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network_name_str CONICET Digital (CONICET)
spelling A New Approach to Segmentation of Multispectral Remote Sensing Images Based on MRFBaumgartner, Josef SylvesterGimenez Romero, Javier AlejandroScavuzzo, MarceloPucheta, Julián AntonioImage SegmentationMarkov Random FieldsMultispectral ImagingProbability Density Functionhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2Segmentation of multispectral remote sensing images is a key competence for a great variety of applications. Many of the applied segmentation algorithms are generative models based on Markov random fields. These approaches are generally limited to multivariate probability densities like the normal distribution. Besides that, it is usually impossible to adjust the contextual parameters separately for each frequency band. In this work, we present a new segmentation algorithm that avoids the mentioned problems and allows the use of any univariate density function as emission probability in each band. The approach consists of three steps: First, calculate feature vectors for each frequency band. Second, estimate contextual parameters for each band and apply local smoothing. Third, merge the feature vectors of the frequency bands to obtain a final segmentation. This procedure can be iterated but experiments show, that after the first iteration most of the pixels are already in their final state. We call our approach Successive Band Merging (SBM). To evaluate the performance of SBM, we segment a Landsat 8 and an AVIRIS image. In both cases, the κ coefficients show, that SBM outperforms the benchmark algorithms.Fil: Baumgartner, Josef Sylvester. Universidad Nacional de Cordoba. Facultad de Cs.exactas Fisicas y Naturales. Departamento de Electronica; ArgentinaFil: Gimenez Romero, Javier Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico San Juan. Instituto de Automática; ArgentinaFil: Scavuzzo, Marcelo. Comision Nacional de Actividades Espaciales; ArgentinaFil: Pucheta, Julián Antonio. Universidad Nacional de Cordoba. Facultad de Cs.exactas Fisicas y Naturales. Departamento de Electronica; ArgentinaInstitute Of Electrical And Electronics Engineers2015-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/7511Baumgartner, Josef Sylvester; Gimenez Romero, Javier Alejandro; Scavuzzo, Marcelo; Pucheta, Julián Antonio; A New Approach to Segmentation of Multispectral Remote Sensing Images Based on MRF; Institute Of Electrical And Electronics Engineers; Ieee Geoscience And Remote Sensing Letters; 12; 8; 6-2015; 1720-17241545-598Xenginfo:eu-repo/semantics/altIdentifier/url/http://ieeexplore.ieee.org/document/7098347/?arnumber=7098347info:eu-repo/semantics/altIdentifier/doi/10.1109/LGRS.2015.2421736info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:58:13Zoai:ri.conicet.gov.ar:11336/7511instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:58:14.236CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A New Approach to Segmentation of Multispectral Remote Sensing Images Based on MRF
title A New Approach to Segmentation of Multispectral Remote Sensing Images Based on MRF
spellingShingle A New Approach to Segmentation of Multispectral Remote Sensing Images Based on MRF
Baumgartner, Josef Sylvester
Image Segmentation
Markov Random Fields
Multispectral Imaging
Probability Density Function
title_short A New Approach to Segmentation of Multispectral Remote Sensing Images Based on MRF
title_full A New Approach to Segmentation of Multispectral Remote Sensing Images Based on MRF
title_fullStr A New Approach to Segmentation of Multispectral Remote Sensing Images Based on MRF
title_full_unstemmed A New Approach to Segmentation of Multispectral Remote Sensing Images Based on MRF
title_sort A New Approach to Segmentation of Multispectral Remote Sensing Images Based on MRF
dc.creator.none.fl_str_mv Baumgartner, Josef Sylvester
Gimenez Romero, Javier Alejandro
Scavuzzo, Marcelo
Pucheta, Julián Antonio
author Baumgartner, Josef Sylvester
author_facet Baumgartner, Josef Sylvester
Gimenez Romero, Javier Alejandro
Scavuzzo, Marcelo
Pucheta, Julián Antonio
author_role author
author2 Gimenez Romero, Javier Alejandro
Scavuzzo, Marcelo
Pucheta, Julián Antonio
author2_role author
author
author
dc.subject.none.fl_str_mv Image Segmentation
Markov Random Fields
Multispectral Imaging
Probability Density Function
topic Image Segmentation
Markov Random Fields
Multispectral Imaging
Probability Density Function
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Segmentation of multispectral remote sensing images is a key competence for a great variety of applications. Many of the applied segmentation algorithms are generative models based on Markov random fields. These approaches are generally limited to multivariate probability densities like the normal distribution. Besides that, it is usually impossible to adjust the contextual parameters separately for each frequency band. In this work, we present a new segmentation algorithm that avoids the mentioned problems and allows the use of any univariate density function as emission probability in each band. The approach consists of three steps: First, calculate feature vectors for each frequency band. Second, estimate contextual parameters for each band and apply local smoothing. Third, merge the feature vectors of the frequency bands to obtain a final segmentation. This procedure can be iterated but experiments show, that after the first iteration most of the pixels are already in their final state. We call our approach Successive Band Merging (SBM). To evaluate the performance of SBM, we segment a Landsat 8 and an AVIRIS image. In both cases, the κ coefficients show, that SBM outperforms the benchmark algorithms.
Fil: Baumgartner, Josef Sylvester. Universidad Nacional de Cordoba. Facultad de Cs.exactas Fisicas y Naturales. Departamento de Electronica; Argentina
Fil: Gimenez Romero, Javier Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico San Juan. Instituto de Automática; Argentina
Fil: Scavuzzo, Marcelo. Comision Nacional de Actividades Espaciales; Argentina
Fil: Pucheta, Julián Antonio. Universidad Nacional de Cordoba. Facultad de Cs.exactas Fisicas y Naturales. Departamento de Electronica; Argentina
description Segmentation of multispectral remote sensing images is a key competence for a great variety of applications. Many of the applied segmentation algorithms are generative models based on Markov random fields. These approaches are generally limited to multivariate probability densities like the normal distribution. Besides that, it is usually impossible to adjust the contextual parameters separately for each frequency band. In this work, we present a new segmentation algorithm that avoids the mentioned problems and allows the use of any univariate density function as emission probability in each band. The approach consists of three steps: First, calculate feature vectors for each frequency band. Second, estimate contextual parameters for each band and apply local smoothing. Third, merge the feature vectors of the frequency bands to obtain a final segmentation. This procedure can be iterated but experiments show, that after the first iteration most of the pixels are already in their final state. We call our approach Successive Band Merging (SBM). To evaluate the performance of SBM, we segment a Landsat 8 and an AVIRIS image. In both cases, the κ coefficients show, that SBM outperforms the benchmark algorithms.
publishDate 2015
dc.date.none.fl_str_mv 2015-06
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/11336/7511
Baumgartner, Josef Sylvester; Gimenez Romero, Javier Alejandro; Scavuzzo, Marcelo; Pucheta, Julián Antonio; A New Approach to Segmentation of Multispectral Remote Sensing Images Based on MRF; Institute Of Electrical And Electronics Engineers; Ieee Geoscience And Remote Sensing Letters; 12; 8; 6-2015; 1720-1724
1545-598X
url http://hdl.handle.net/11336/7511
identifier_str_mv Baumgartner, Josef Sylvester; Gimenez Romero, Javier Alejandro; Scavuzzo, Marcelo; Pucheta, Julián Antonio; A New Approach to Segmentation of Multispectral Remote Sensing Images Based on MRF; Institute Of Electrical And Electronics Engineers; Ieee Geoscience And Remote Sensing Letters; 12; 8; 6-2015; 1720-1724
1545-598X
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://ieeexplore.ieee.org/document/7098347/?arnumber=7098347
info:eu-repo/semantics/altIdentifier/doi/10.1109/LGRS.2015.2421736
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Institute Of Electrical And Electronics Engineers
publisher.none.fl_str_mv Institute Of Electrical And Electronics Engineers
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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