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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/7511
Ver los metadatos del registro completo
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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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1842269508840980480 |
score |
13.13397 |