Specific land cover class mapping by semi-supervised weighted support vector machines

Detalhes bibliográficos
Autor(a) principal: Silva, Joel
Data de Publicação: 2017
Outros Autores: Bação, Fernando, Caetano, Mario
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://doi.org/10.3390/rs9020181
Resumo: Silva, J., Bação, F., & Caetano, M. (2017). Specific land cover class mapping by semi-supervised weighted support vector machines. Remote Sensing, 9(2), [181]. https://doi.org/10.3390/rs9020181
id RCAP_a2b1a6df246e5560b5a361b57c726fd9
oai_identifier_str oai:run.unl.pt:10362/27384
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str 7160
spelling Specific land cover class mapping by semi-supervised weighted support vector machinesLand coverLandsatMangroveOne-class support vector machinesRandom training setSpecific class mappingWeighted support vector machineEarth and Planetary Sciences(all)Silva, J., Bação, F., & Caetano, M. (2017). Specific land cover class mapping by semi-supervised weighted support vector machines. Remote Sensing, 9(2), [181]. https://doi.org/10.3390/rs9020181In many remote sensing projects on land cover mapping, the interest is often in a sub-set of classes presented in the study area. Conventional multi-class classification may lead to a considerable training effort and to the underestimation of the classes of interest. On the other hand, one-class classifiers require much less training, but may overestimate the real extension of the class of interest. This paper illustrates the combined use of cost-sensitive and semi-supervised learning to overcome these difficulties. This method utilises a manually-collected set of pixels of the class of interest and a random sample of pixels, keeping the training effort low. Each data point is then weighted according to its distance to its near positive data point to inform the learning algorithm. The proposed approach was compared with a conventional multi-class classifier, a one-class classifier, and a semi-supervised classifier in the discrimination of high-mangrove in Saloum estuary, Senegal, from Landsat imagery. The derived classification accuracies were high: 93.90% for the multi-class supervised classifier, 90.75% for the semi-supervised classifier, 88.75% for the one-class classifier, and 93.75% for the proposed method. The results show that accuracy achieved with the proposed method is statistically non-inferior to that achieved with standard binary classification, requiring however much less training effort.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNSilva, JoelBação, FernandoCaetano, Mario2017-12-28T23:05:28Z20172017-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://doi.org/10.3390/rs9020181eng2072-4292PURE: 2336688http://www.scopus.com/inward/record.url?scp=85013625662&partnerID=8YFLogxKhttps://doi.org/10.3390/rs9020181info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:14:27Zoai:run.unl.pt:10362/27384Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:28:40.272910Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Specific land cover class mapping by semi-supervised weighted support vector machines
title Specific land cover class mapping by semi-supervised weighted support vector machines
spellingShingle Specific land cover class mapping by semi-supervised weighted support vector machines
Silva, Joel
Land cover
Landsat
Mangrove
One-class support vector machines
Random training set
Specific class mapping
Weighted support vector machine
Earth and Planetary Sciences(all)
title_short Specific land cover class mapping by semi-supervised weighted support vector machines
title_full Specific land cover class mapping by semi-supervised weighted support vector machines
title_fullStr Specific land cover class mapping by semi-supervised weighted support vector machines
title_full_unstemmed Specific land cover class mapping by semi-supervised weighted support vector machines
title_sort Specific land cover class mapping by semi-supervised weighted support vector machines
author Silva, Joel
author_facet Silva, Joel
Bação, Fernando
Caetano, Mario
author_role author
author2 Bação, Fernando
Caetano, Mario
author2_role author
author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Silva, Joel
Bação, Fernando
Caetano, Mario
dc.subject.por.fl_str_mv Land cover
Landsat
Mangrove
One-class support vector machines
Random training set
Specific class mapping
Weighted support vector machine
Earth and Planetary Sciences(all)
topic Land cover
Landsat
Mangrove
One-class support vector machines
Random training set
Specific class mapping
Weighted support vector machine
Earth and Planetary Sciences(all)
description Silva, J., Bação, F., & Caetano, M. (2017). Specific land cover class mapping by semi-supervised weighted support vector machines. Remote Sensing, 9(2), [181]. https://doi.org/10.3390/rs9020181
publishDate 2017
dc.date.none.fl_str_mv 2017-12-28T23:05:28Z
2017
2017-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://doi.org/10.3390/rs9020181
url https://doi.org/10.3390/rs9020181
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2072-4292
PURE: 2336688
http://www.scopus.com/inward/record.url?scp=85013625662&partnerID=8YFLogxK
https://doi.org/10.3390/rs9020181
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
_version_ 1799137912085807104

Registros relacionados