Specific land cover class mapping by semi-supervised weighted support vector machines
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , |
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 |
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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 |
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reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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