A co-occurrence region based Bayesian network stepwise remote sensing image retrieval algorithm

Although scholars have conducted numerous researches on content-based image retrieval and obtained great achievements, they make little progress in studying remote sensing image retrieval. Both theoretical and application systems are immature. Since remote sensing images are characterized by large d...

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Autores:
Zeng, Rui
Wang, Yingyan
Wang, Wanliang
Tipo de recurso:
Article of journal
Fecha de publicación:
2018
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/63561
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/63561
http://bdigital.unal.edu.co/64007/
Palabra clave:
55 Ciencias de la tierra / Earth sciences and geology
Bayesian network
Co-occurrence region
Remote sensing image retrieval
red bayesiana
región de coocurrencia
recuperación de imágenes por teledetección.
Rights
openAccess
License
Atribución-NoComercial 4.0 Internacional
id UNACIONAL2_b98a298d67309ce9c98c328dc5e1cc99
oai_identifier_str oai:repositorio.unal.edu.co:unal/63561
network_acronym_str UNACIONAL2
network_name_str Universidad Nacional de Colombia
repository_id_str
dc.title.spa.fl_str_mv A co-occurrence region based Bayesian network stepwise remote sensing image retrieval algorithm
title A co-occurrence region based Bayesian network stepwise remote sensing image retrieval algorithm
spellingShingle A co-occurrence region based Bayesian network stepwise remote sensing image retrieval algorithm
55 Ciencias de la tierra / Earth sciences and geology
Bayesian network
Co-occurrence region
Remote sensing image retrieval
red bayesiana
región de coocurrencia
recuperación de imágenes por teledetección.
title_short A co-occurrence region based Bayesian network stepwise remote sensing image retrieval algorithm
title_full A co-occurrence region based Bayesian network stepwise remote sensing image retrieval algorithm
title_fullStr A co-occurrence region based Bayesian network stepwise remote sensing image retrieval algorithm
title_full_unstemmed A co-occurrence region based Bayesian network stepwise remote sensing image retrieval algorithm
title_sort A co-occurrence region based Bayesian network stepwise remote sensing image retrieval algorithm
dc.creator.fl_str_mv Zeng, Rui
Wang, Yingyan
Wang, Wanliang
dc.contributor.author.spa.fl_str_mv Zeng, Rui
Wang, Yingyan
Wang, Wanliang
dc.subject.ddc.spa.fl_str_mv 55 Ciencias de la tierra / Earth sciences and geology
topic 55 Ciencias de la tierra / Earth sciences and geology
Bayesian network
Co-occurrence region
Remote sensing image retrieval
red bayesiana
región de coocurrencia
recuperación de imágenes por teledetección.
dc.subject.proposal.spa.fl_str_mv Bayesian network
Co-occurrence region
Remote sensing image retrieval
red bayesiana
región de coocurrencia
recuperación de imágenes por teledetección.
description Although scholars have conducted numerous researches on content-based image retrieval and obtained great achievements, they make little progress in studying remote sensing image retrieval. Both theoretical and application systems are immature. Since remote sensing images are characterized by large data volume, broad coverage, vague themes and rich semantics, the research results on natural images and medical images cannot be directly used in remote sensing image retrieval. Even perfect content-based remote sensing image retrieval systems have many difficulties with data organization, storage and management, feature description and extraction, similarity measurement, relevance feedback, network service mode, and system structure design and implementation. This paper proposes a remote sensing image retrieval algorithm that combines co-occurrence region based Bayesian network image retrieval with average high-frequency signal strength. By Bayesian networks, it establishes correspondence relationships between images and semantics, thereby realizing semantic-based retrieval of remote sensing images. In the meantime, integrated region matching is introduced for iterative retrieval, which effectively improves the precision of semantic retrieval.
publishDate 2018
dc.date.issued.spa.fl_str_mv 2018-01-01
dc.date.accessioned.spa.fl_str_mv 2019-07-02T21:54:06Z
dc.date.available.spa.fl_str_mv 2019-07-02T21:54:06Z
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.issn.spa.fl_str_mv ISSN: 2339-3459
dc.identifier.uri.none.fl_str_mv https://repositorio.unal.edu.co/handle/unal/63561
dc.identifier.eprints.spa.fl_str_mv http://bdigital.unal.edu.co/64007/
identifier_str_mv ISSN: 2339-3459
url https://repositorio.unal.edu.co/handle/unal/63561
http://bdigital.unal.edu.co/64007/
dc.language.iso.spa.fl_str_mv spa
language spa
dc.relation.spa.fl_str_mv https://revistas.unal.edu.co/index.php/esrj/article/view/66107
dc.relation.ispartof.spa.fl_str_mv Universidad Nacional de Colombia Revistas electrónicas UN Earth Sciences Research Journal
Earth Sciences Research Journal
dc.relation.references.spa.fl_str_mv Zeng, Rui and Wang, Yingyan and Wang, Wanliang (2018) A co-occurrence region based Bayesian network stepwise remote sensing image retrieval algorithm. Earth Sciences Research Journal, 22 (1). pp. 29-35. ISSN 2339-3459
dc.rights.spa.fl_str_mv Derechos reservados - Universidad Nacional de Colombia
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.license.spa.fl_str_mv Atribución-NoComercial 4.0 Internacional
dc.rights.uri.spa.fl_str_mv http://creativecommons.org/licenses/by-nc/4.0/
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv Atribución-NoComercial 4.0 Internacional
Derechos reservados - Universidad Nacional de Colombia
http://creativecommons.org/licenses/by-nc/4.0/
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv Universidad Nacional de Colombia - Sede Bogotá - Facultad de Ciencias - Departamento de Geociencia
institution Universidad Nacional de Colombia
bitstream.url.fl_str_mv https://repositorio.unal.edu.co/bitstream/unal/63561/1/66107-380283-1-PB.pdf
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spelling Atribución-NoComercial 4.0 InternacionalDerechos reservados - Universidad Nacional de Colombiahttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Zeng, Ruibd64f46e-40f4-47cd-a3f1-6dca1388a6cd300Wang, Yingyan26b52969-285c-457c-9e4f-bcea3690e436300Wang, Wanliangff0ca174-effb-439b-ab44-56753c63cdfe3002019-07-02T21:54:06Z2019-07-02T21:54:06Z2018-01-01ISSN: 2339-3459https://repositorio.unal.edu.co/handle/unal/63561http://bdigital.unal.edu.co/64007/Although scholars have conducted numerous researches on content-based image retrieval and obtained great achievements, they make little progress in studying remote sensing image retrieval. Both theoretical and application systems are immature. Since remote sensing images are characterized by large data volume, broad coverage, vague themes and rich semantics, the research results on natural images and medical images cannot be directly used in remote sensing image retrieval. Even perfect content-based remote sensing image retrieval systems have many difficulties with data organization, storage and management, feature description and extraction, similarity measurement, relevance feedback, network service mode, and system structure design and implementation. This paper proposes a remote sensing image retrieval algorithm that combines co-occurrence region based Bayesian network image retrieval with average high-frequency signal strength. By Bayesian networks, it establishes correspondence relationships between images and semantics, thereby realizing semantic-based retrieval of remote sensing images. In the meantime, integrated region matching is introduced for iterative retrieval, which effectively improves the precision of semantic retrieval.A pesar de que muchos investigadores han realizado numerosos trabajos sobre la consulta de imágenes mediante ejemplo y han obtenido grandes logros, poco se ha avanzado en la recuperación de imágenes por teledetección. Tanto la teoría como la aplicación de los sistemas son inmaduros. Ya que las imágenes por teledetección se caracterizan por un gran volumen de información, amplia cobertura, temas difusos y semántica abundante, los resultados de las investigaciones en imágenes naturales e imágenes médicas estos no pueden ser usados directamente en la recuperación de imágenes por teledetección. Incluso en una consulta perfecta de imágenes mediante ejemplo, los sistemas tienen muchas dificultades con la organización de información, almacenamiento y manejo, descripción de características y extracción, medición de similitudes, retroalimentación relevante, modo de servicio de red y diseño e implementación del sistema estructural. Este artículo propone un algoritmo de recuperación de imágenes por teledetección que combina la coocurrencia local de una red bayesiana de recuperación de imagénes con el promedio de potencia de la señal de alta frecuencia. Por las redes bayesianas, se establecen las relaciones de correspondencia entre imágenes y semántica, además de permitir la recuperación de imágenes de teledetección a través de la semántica. Mientras tanto, se desarrolló el módulo de región integrada para la recuperación repetitiva, lo que mejora efectivamente la precisión de la recuperación semántica.application/pdfspaUniversidad Nacional de Colombia - Sede Bogotá - Facultad de Ciencias - Departamento de Geocienciahttps://revistas.unal.edu.co/index.php/esrj/article/view/66107Universidad Nacional de Colombia Revistas electrónicas UN Earth Sciences Research JournalEarth Sciences Research JournalZeng, Rui and Wang, Yingyan and Wang, Wanliang (2018) A co-occurrence region based Bayesian network stepwise remote sensing image retrieval algorithm. Earth Sciences Research Journal, 22 (1). pp. 29-35. ISSN 2339-345955 Ciencias de la tierra / Earth sciences and geologyBayesian networkCo-occurrence regionRemote sensing image retrievalred bayesianaregión de coocurrenciarecuperación de imágenes por teledetección.A co-occurrence region based Bayesian network stepwise remote sensing image retrieval algorithmArtículo de revistainfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Texthttp://purl.org/redcol/resource_type/ARTORIGINAL66107-380283-1-PB.pdfapplication/pdf1626613https://repositorio.unal.edu.co/bitstream/unal/63561/1/66107-380283-1-PB.pdfb5d439a652b04ccaf6797aab6cc40933MD51THUMBNAIL66107-380283-1-PB.pdf.jpg66107-380283-1-PB.pdf.jpgGenerated Thumbnailimage/jpeg7309https://repositorio.unal.edu.co/bitstream/unal/63561/2/66107-380283-1-PB.pdf.jpg7d5ba5f2f3ff90ff92b8a498ba5a6b20MD52unal/63561oai:repositorio.unal.edu.co:unal/635612023-04-22 23:05:12.988Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.co

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