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On the information provided by uncertainty measures in the classification of remote sensing images

2009-07Conference paper Open access
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datacite.subject.fosCiências Naturais::Ciências da Computação e da Informação
datacite.subject.fosCiências Sociais::Economia e Gestão
datacite.subject.fosCiências Naturais::Matemáticas
datacite.subject.sdg07:Energias Renováveis e Acessíveis
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
datacite.subject.sdg11:Cidades e Comunidades Sustentáveis
dc.contributor.authorGonçalves, Luisa
dc.contributor.authorFonte, Cidália C.
dc.contributor.authorJúlio, Eduardo N.B.S.
dc.contributor.authorCaetano, Mario
dc.date.accessioned2025-06-06T14:50:45Z
dc.date.available2025-06-06T14:50:45Z
dc.date.issued2009-07
dc.descriptionJoint 2009 International Fuzzy Systems Association World Congress, IFSA 2009 and 2009 European Society of Fuzzy Logic and Technology Conference, EUSFLAT 2009, 20 July 2009 through 24 July 2009 - Code 94760. https://scholar.google.com/scholar?q=On%20the%20information%20provided%20by%20uncertainty%20measures%20in%20the%20classification%20of%20remote%20sensing%20images
dc.description.abstractThis paper investigates the potential information provided to the user by the uncertainty measures applied to the possibility distributions associated with the spatial units of an IKONOS satellite image, generated by two fuzzy classifiers, based, respectively, on the Nearest Neighbour Classifier and the Minimum Distance to Means Classifier. The deviation of the geographic unit characteristics from the prototype of the class to which the geographic unit is assigned is evaluated with the Un non-specificity uncertainty measures proposed by [1] and the exaggeration uncertainty measure proposed by [2]. The classifications were evaluated using accuracy and uncertainty indexes to determine their compatibility. Both classifications generated medium to high levels of uncertainty for almost all classes, and the global accuracy indexes computed were 70% for the Nearest Neighbour Classifier and 53% for the Minimum Distance to Means Classifier. The results show that similar conclusions can be obtained with accuracy and uncertainty indexes and the latter, along with the analysis of the possibility distributions, may be used as indicators of the classification performance and may therefore be very useful tools. Since the uncertainty indexes may be computed to all spatial units, the spatial distribution of the uncertainty was also analysed. It's visualization shows that regions where less reliability is expected present a great amount of detail that may be potentially useful to the user.eng
dc.identifier.citationGonçalves, L. M., Fonte, C. C., Júlio, E. N., & Caetano, M. (2009). On the Information Provided by Uncertainty Measures in the Classification of Remote Sensing Images. In IFSA/EUSFLAT Conf. (pp. 1051-1056).
dc.identifier.isbn978-989950796-8
dc.identifier.urihttp://hdl.handle.net/10400.8/13169
dc.language.isoeng
dc.peerreviewedyes
dc.publisherEUROPEAN SOC FUZZY LOGIC & TECHNOLOGY
dc.relation.hasversionhttps://www.webofscience.com/wos/woscc/full-record/WOS:000279170600183
dc.rights.uriN/A
dc.subjectAccuracy assessment
dc.subjectMinimum Distance to Mean Classifier
dc.subjectNearest Neighbour Classifier
dc.subjectNon-specificity measures
dc.subjectRemote Sensing Images
dc.subjectUncertainty
dc.titleOn the information provided by uncertainty measures in the classification of remote sensing imageseng
dc.typeconference paper
dspace.entity.typePublication
oaire.citation.conferenceDate2009-07
oaire.citation.conferencePlaceLisbon, Portugal
oaire.citation.endPage1056
oaire.citation.startPage1051
oaire.citation.titleInternational Fuzzy Systems Association World Congress
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameGonçalves
person.givenNameLuisa
person.identifier.ciencia-id9116-82A0-3060
person.identifier.orcid0000-0002-6265-8903
person.identifier.ridU-1298-2017
person.identifier.scopus-author-id35145815700
relation.isAuthorOfPublication1ba44699-bdda-4e01-97ec-c02fe603afc5
relation.isAuthorOfPublication.latestForDiscovery1ba44699-bdda-4e01-97ec-c02fe603afc5

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This paper investigates the potential information provided to the user by the uncertainty measures applied to the possibility distributions associated with the spatial units of an IKONOS satellite image, generated by two fuzzy classifiers, based, respectively, on the Nearest Neighbour Classifier and the Minimum Distance to Means Classifier. The deviation of the geographic unit characteristics from the prototype of the class to which the geographic unit is assigned is evaluated with the Un non-specificity uncertainty measures proposed by [1] and the exaggeration uncertainty measure proposed by [2]. The classifications were evaluated using accuracy and uncertainty indexes to determine their compatibility. Both classifications generated medium to high levels of uncertainty for almost all classes, and the global accuracy indexes computed were 70% for the Nearest Neighbour Classifier and 53% for the Minimum Distance to Means Classifier. The results show that similar conclusions can be obtained with accuracy and uncertainty indexes and the latter, along with the analysis of the possibility distributions, may be used as indicators of the classification performance and may therefore be very useful tools. Since the uncertainty indexes may be computed to all spatial units, the spatial distribution of the uncertainty was also analysed. It's visualization shows that regions where less reliability is expected present a great amount of detail that may be potentially useful to the user.
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