UNSUPERVISED LAND-COVER CLASSIFICATION THROUGH HYPER-HEURISTIC-BASED HARMONY SEARCH

Detalhes bibliográficos
Autor(a) principal: Papa, J. [UNESP]
Data de Publicação: 2015
Outros Autores: Papa, L., Pisani, R., Pereira, D., IEEE
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://hdl.handle.net/11449/161289
Resumo: Unsupervised land-cover classification aims at learning intrinsic properties of spectral and spatial features for the task of area coverage in urban and rural areas. In this paper, we propose to model the problem of optimizing the well-known k means algorithm by combining different variations of the Harmony Search technique using Genetic Programming (GP). We have shown GP can improve the recognition rates when using one optimization technique only, but it still deserves a deeper study when we have a very good individual technique to be combined.
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spelling UNSUPERVISED LAND-COVER CLASSIFICATION THROUGH HYPER-HEURISTIC-BASED HARMONY SEARCHClusteringLand-cover classificationMachine LearningGenetic ProgrammingUnsupervised land-cover classification aims at learning intrinsic properties of spectral and spatial features for the task of area coverage in urban and rural areas. In this paper, we propose to model the problem of optimizing the well-known k means algorithm by combining different variations of the Harmony Search technique using Genetic Programming (GP). We have shown GP can improve the recognition rates when using one optimization technique only, but it still deserves a deeper study when we have a very good individual technique to be combined.Sao Paulo State Univ, Dept Comp, Bauru, SP, BrazilSao Paulo State Southwest Coll, Dept Hlth, Avare, SP, BrazilUniv Western Sao Paulo, Dept Comp, Presidente Prudente, SP, BrazilSao Paulo State Univ, Dept Comp, Bauru, SP, BrazilIeeeUniversidade Estadual Paulista (Unesp)Sao Paulo State Southwest CollUniv Western Sao PauloPapa, J. [UNESP]Papa, L.Pisani, R.Pereira, D.IEEE2018-11-26T16:27:55Z2018-11-26T16:27:55Z2015-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject69-722015 Ieee International Geoscience And Remote Sensing Symposium (igarss). New York: Ieee, p. 69-72, 2015.2153-6996http://hdl.handle.net/11449/161289WOS:000371696700018Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2015 Ieee International Geoscience And Remote Sensing Symposium (igarss)info:eu-repo/semantics/openAccess2024-04-23T16:11:26Zoai:repositorio.unesp.br:11449/161289Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-04-23T16:11:26Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv UNSUPERVISED LAND-COVER CLASSIFICATION THROUGH HYPER-HEURISTIC-BASED HARMONY SEARCH
title UNSUPERVISED LAND-COVER CLASSIFICATION THROUGH HYPER-HEURISTIC-BASED HARMONY SEARCH
spellingShingle UNSUPERVISED LAND-COVER CLASSIFICATION THROUGH HYPER-HEURISTIC-BASED HARMONY SEARCH
Papa, J. [UNESP]
Clustering
Land-cover classification
Machine Learning
Genetic Programming
title_short UNSUPERVISED LAND-COVER CLASSIFICATION THROUGH HYPER-HEURISTIC-BASED HARMONY SEARCH
title_full UNSUPERVISED LAND-COVER CLASSIFICATION THROUGH HYPER-HEURISTIC-BASED HARMONY SEARCH
title_fullStr UNSUPERVISED LAND-COVER CLASSIFICATION THROUGH HYPER-HEURISTIC-BASED HARMONY SEARCH
title_full_unstemmed UNSUPERVISED LAND-COVER CLASSIFICATION THROUGH HYPER-HEURISTIC-BASED HARMONY SEARCH
title_sort UNSUPERVISED LAND-COVER CLASSIFICATION THROUGH HYPER-HEURISTIC-BASED HARMONY SEARCH
author Papa, J. [UNESP]
author_facet Papa, J. [UNESP]
Papa, L.
Pisani, R.
Pereira, D.
IEEE
author_role author
author2 Papa, L.
Pisani, R.
Pereira, D.
IEEE
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
Sao Paulo State Southwest Coll
Univ Western Sao Paulo
dc.contributor.author.fl_str_mv Papa, J. [UNESP]
Papa, L.
Pisani, R.
Pereira, D.
IEEE
dc.subject.por.fl_str_mv Clustering
Land-cover classification
Machine Learning
Genetic Programming
topic Clustering
Land-cover classification
Machine Learning
Genetic Programming
description Unsupervised land-cover classification aims at learning intrinsic properties of spectral and spatial features for the task of area coverage in urban and rural areas. In this paper, we propose to model the problem of optimizing the well-known k means algorithm by combining different variations of the Harmony Search technique using Genetic Programming (GP). We have shown GP can improve the recognition rates when using one optimization technique only, but it still deserves a deeper study when we have a very good individual technique to be combined.
publishDate 2015
dc.date.none.fl_str_mv 2015-01-01
2018-11-26T16:27:55Z
2018-11-26T16:27:55Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
status_str publishedVersion
dc.identifier.uri.fl_str_mv 2015 Ieee International Geoscience And Remote Sensing Symposium (igarss). New York: Ieee, p. 69-72, 2015.
2153-6996
http://hdl.handle.net/11449/161289
WOS:000371696700018
identifier_str_mv 2015 Ieee International Geoscience And Remote Sensing Symposium (igarss). New York: Ieee, p. 69-72, 2015.
2153-6996
WOS:000371696700018
url http://hdl.handle.net/11449/161289
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2015 Ieee International Geoscience And Remote Sensing Symposium (igarss)
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 69-72
dc.publisher.none.fl_str_mv Ieee
publisher.none.fl_str_mv Ieee
dc.source.none.fl_str_mv Web of Science
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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