DER: Dynamic Evidential Reasoning applied to hyperspectral images classification

Autores
Sanz, Cecilia Verónica; Jordán, Ramiro
Año de publicación
2002
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This paper describes a new classification method (DER) based on evidential reasoning to which a series of modifications are added [1]. DER allows including new evidence for the classification process and defines a different decision rule. The evidential reasoning algorithm provides a means to combine evidence from different data sources. It is a supervised classification technique that uses a training samples set. This novel method (DER) offers a learning stage to introduce new evidence in case the classifier requires so. Moreover, it uses the plausibility measure in order to define the decision rule as a way to incorporate data-associated uncertainty. The proposed method is applied in order to classify crops in hyperspectral images of the area of Nebraska (USA). Some results obtained are presented in order to assess DER precision.
Facultad de Informática
Materia
Ciencias Informáticas
Image processing software
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc/3.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/9430
Acceder
id SEDICI_6d65b44f87d4a731588d2e7be1094670
oai_identifier_str oai:sedici.unlp.edu.ar:10915/9430
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling DER: Dynamic Evidential Reasoning applied to hyperspectral images classificationSanz, Cecilia VerónicaJordán, RamiroCiencias InformáticasImage processing softwareThis paper describes a new classification method (DER) based on evidential reasoning to which a series of modifications are added [1]. DER allows including new evidence for the classification process and defines a different decision rule. The evidential reasoning algorithm provides a means to combine evidence from different data sources. It is a supervised classification technique that uses a training samples set. This novel method (DER) offers a learning stage to introduce new evidence in case the classifier requires so. Moreover, it uses the plausibility measure in order to define the decision rule as a way to incorporate data-associated uncertainty. The proposed method is applied in order to classify crops in hyperspectral images of the area of Nebraska (USA). Some results obtained are presented in order to assess DER precision.Facultad de Informática2002info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/9430enginfo:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/p21.pdfinfo:eu-repo/semantics/altIdentifier/issn/1666-6038info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc/3.0/Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-15T10:43:16Zoai:sedici.unlp.edu.ar:10915/9430Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-15 10:43:16.979SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv DER: Dynamic Evidential Reasoning applied to hyperspectral images classification
title DER: Dynamic Evidential Reasoning applied to hyperspectral images classification
spellingShingle DER: Dynamic Evidential Reasoning applied to hyperspectral images classification
Sanz, Cecilia Verónica
Ciencias Informáticas
Image processing software
title_short DER: Dynamic Evidential Reasoning applied to hyperspectral images classification
title_full DER: Dynamic Evidential Reasoning applied to hyperspectral images classification
title_fullStr DER: Dynamic Evidential Reasoning applied to hyperspectral images classification
title_full_unstemmed DER: Dynamic Evidential Reasoning applied to hyperspectral images classification
title_sort DER: Dynamic Evidential Reasoning applied to hyperspectral images classification
dc.creator.none.fl_str_mv Sanz, Cecilia Verónica
Jordán, Ramiro
author Sanz, Cecilia Verónica
author_facet Sanz, Cecilia Verónica
Jordán, Ramiro
author_role author
author2 Jordán, Ramiro
author2_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
Image processing software
topic Ciencias Informáticas
Image processing software
dc.description.none.fl_txt_mv This paper describes a new classification method (DER) based on evidential reasoning to which a series of modifications are added [1]. DER allows including new evidence for the classification process and defines a different decision rule. The evidential reasoning algorithm provides a means to combine evidence from different data sources. It is a supervised classification technique that uses a training samples set. This novel method (DER) offers a learning stage to introduce new evidence in case the classifier requires so. Moreover, it uses the plausibility measure in order to define the decision rule as a way to incorporate data-associated uncertainty. The proposed method is applied in order to classify crops in hyperspectral images of the area of Nebraska (USA). Some results obtained are presented in order to assess DER precision.
Facultad de Informática
description This paper describes a new classification method (DER) based on evidential reasoning to which a series of modifications are added [1]. DER allows including new evidence for the classification process and defines a different decision rule. The evidential reasoning algorithm provides a means to combine evidence from different data sources. It is a supervised classification technique that uses a training samples set. This novel method (DER) offers a learning stage to introduce new evidence in case the classifier requires so. Moreover, it uses the plausibility measure in order to define the decision rule as a way to incorporate data-associated uncertainty. The proposed method is applied in order to classify crops in hyperspectral images of the area of Nebraska (USA). Some results obtained are presented in order to assess DER precision.
publishDate 2002
dc.date.none.fl_str_mv 2002
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
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://sedici.unlp.edu.ar/handle/10915/9430
url http://sedici.unlp.edu.ar/handle/10915/9430
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/p21.pdf
info:eu-repo/semantics/altIdentifier/issn/1666-6038
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc/3.0/
Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc/3.0/
Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
_version_ 1846063847362789376
score 13.22299

Publicaciones similares