Publicação
Slum mapping : a comparison of single class learning and expert system object-oriented classification for mapping slum settlements in Addis Ababa city, Ethiopia
| Resumo: | Updated spatial information on the dynamics of slums can be helpful to measure and evaluate the progress of urban upgrading projects and policies. Earlier studies have shown that remote sensing techniques, with the help of very-high resolution imagery, can play a significant role in detecting slums, and providing timely spatial information. The main objective of this thesis is to develop a reliable object-oriented slum identification technique that enables the provision of timely spatial information about slum settlements in Addis Ababa city. It compares the one-class support vector machines algorithm with the expert defined classification rule set in the discrimination of slums, using GeoEye-1 imagery. Two different approaches, called manual and automatic fine-tuning, were deployed to determine the best value of parameters in one-class support vector machines algorithm. The manual fine-tuning of the parameters is done using extensive manual trial. The automatic tuning is done using cross-validation grid search with the overall accuracy as the performance metric. Two regions of study were defined with different landscape compositions, providing different classification scenarios to compare the classification approaches. After image segmentation, twenty predictive variables were computed to characterize the objects in both study areas. An image analyst collected one hundred sample objects of a slum to be used as training for the single-class learner. In parallel, an image analyst has defined a hierarchical rule set to discriminate the class of interest. Results in both study areas indicate that the one-class support vector machine with manual tuning yields higher overall accuracy (97.7% in subset 1, and 92% in subset 2) and requiring much less application effort and computing time than the expert system. |
|---|---|
| Autores principais: | Tesfay, Mulu Weldegebreal |
| Assunto: | Remote sensing Image segmentation Image classification Object-oriented image analysis Single class learning One class support vector machine Expert system classification |
| Ano: | 2018 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| _version_ | 1868983326849630208 |
|---|---|
| author | Tesfay, Mulu Weldegebreal |
| author_facet | Tesfay, Mulu Weldegebreal |
| author_role | author |
| contributor_name_str_mv | Silva, Joel Dinis Baptista Ferreira da Caetano, Mário Sílvio Rochinha de Andrade Guerrero, Ignacio RUN |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Tesfay, Mulu Weldegebreal\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Silva, Joel Dinis Baptista Ferreira da Caetano, Mário Sílvio Rochinha de Andrade Guerrero, Ignacio RUN |
| datacite.creators.creator.creatorName.fl_str_mv | Tesfay, Mulu Weldegebreal |
| datacite.date.Accepted.fl_str_mv | 2018-02-27T00:00:00Z |
| datacite.date.available.fl_str_mv | 2018-04-03T14:50:33Z |
| datacite.date.embargoed.fl_str_mv | 2018-04-03T14:50:33Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Remote sensing Image segmentation Image classification Object-oriented image analysis Single class learning One class support vector machine Expert system classification |
| datacite.titles.title.fl_str_mv | Slum mapping : a comparison of single class learning and expert system object-oriented classification for mapping slum settlements in Addis Ababa city, Ethiopia |
| dc.contributor.none.fl_str_mv | Silva, Joel Dinis Baptista Ferreira da Caetano, Mário Sílvio Rochinha de Andrade Guerrero, Ignacio RUN |
| dc.creator.none.fl_str_mv | Tesfay, Mulu Weldegebreal |
| dc.date.Accepted.fl_str_mv | 2018-02-27T00:00:00Z |
| dc.date.available.fl_str_mv | 2018-04-03T14:50:33Z |
| dc.date.embargoed.fl_str_mv | 2018-04-03T14:50:33Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10362/33711 |
| dc.language.none.fl_str_mv | eng |
| dc.rights.cclincense.fl_str_mv | http://creativecommons.org/licenses/by/4.0/ |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.subject.none.fl_str_mv | Remote sensing Image segmentation Image classification Object-oriented image analysis Single class learning One class support vector machine Expert system classification |
| dc.title.fl_str_mv | Slum mapping : a comparison of single class learning and expert system object-oriented classification for mapping slum settlements in Addis Ababa city, Ethiopia |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_bdcc |
| description | Updated spatial information on the dynamics of slums can be helpful to measure and evaluate the progress of urban upgrading projects and policies. Earlier studies have shown that remote sensing techniques, with the help of very-high resolution imagery, can play a significant role in detecting slums, and providing timely spatial information. The main objective of this thesis is to develop a reliable object-oriented slum identification technique that enables the provision of timely spatial information about slum settlements in Addis Ababa city. It compares the one-class support vector machines algorithm with the expert defined classification rule set in the discrimination of slums, using GeoEye-1 imagery. Two different approaches, called manual and automatic fine-tuning, were deployed to determine the best value of parameters in one-class support vector machines algorithm. The manual fine-tuning of the parameters is done using extensive manual trial. The automatic tuning is done using cross-validation grid search with the overall accuracy as the performance metric. Two regions of study were defined with different landscape compositions, providing different classification scenarios to compare the classification approaches. After image segmentation, twenty predictive variables were computed to characterize the objects in both study areas. An image analyst collected one hundred sample objects of a slum to be used as training for the single-class learner. In parallel, an image analyst has defined a hierarchical rule set to discriminate the class of interest. Results in both study areas indicate that the one-class support vector machine with manual tuning yields higher overall accuracy (97.7% in subset 1, and 92% in subset 2) and requiring much less application effort and computing time than the expert system. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | masterThesis |
| fulltext.url.fl_str_mv | https://run.unl.pt/bitstreams/ea0d590b-c751-4cc7-addf-70b89612a692/download |
| id | run_3dc054e6cd86f2fec5b7aaf5a9474cbc |
| identifier.url.fl_str_mv | http://hdl.handle.net/10362/33711 |
| inst_facet_str | urn:organizationAcronym:unl{{{_:::_}}}Universidade Nova de Lisboa |
| instacron_str | unl |
| institution | Universidade Nova de Lisboa |
| instname_str | Universidade Nova de Lisboa |
| language | eng |
| network_acronym_str | run |
| network_name_str | Repositório Institucional da UNL |
| oai_identifier_str | oai:run.unl.pt:10362/33711 |
| organization_str_mv | urn:organizationAcronym:unl |
| person_str_mv | Tesfay, Mulu Weldegebreal |
| publishDate | 2018 |
| repo_facet_str | urn:repositoryAcronym:run{{{_:::_}}}Repositório Institucional da UNL |
| reponame_str | Repositório Institucional da UNL |
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| spelling | engpt_PTUpdated spatial information on the dynamics of slums can be helpful to measure and evaluate the progress of urban upgrading projects and policies. Earlier studies have shown that remote sensing techniques, with the help of very-high resolution imagery, can play a significant role in detecting slums, and providing timely spatial information. The main objective of this thesis is to develop a reliable object-oriented slum identification technique that enables the provision of timely spatial information about slum settlements in Addis Ababa city. It compares the one-class support vector machines algorithm with the expert defined classification rule set in the discrimination of slums, using GeoEye-1 imagery. Two different approaches, called manual and automatic fine-tuning, were deployed to determine the best value of parameters in one-class support vector machines algorithm. The manual fine-tuning of the parameters is done using extensive manual trial. The automatic tuning is done using cross-validation grid search with the overall accuracy as the performance metric. Two regions of study were defined with different landscape compositions, providing different classification scenarios to compare the classification approaches. After image segmentation, twenty predictive variables were computed to characterize the objects in both study areas. An image analyst collected one hundred sample objects of a slum to be used as training for the single-class learner. In parallel, an image analyst has defined a hierarchical rule set to discriminate the class of interest. Results in both study areas indicate that the one-class support vector machine with manual tuning yields higher overall accuracy (97.7% in subset 1, and 92% in subset 2) and requiring much less application effort and computing time than the expert system.application/pdfpt_PTSlum mapping : a comparison of single class learning and expert system object-oriented classification for mapping slum settlements in Addis Ababa city, EthiopiaTesfay, Mulu WeldegebrealSilva, Joel Dinis Baptista Ferreira daCaetano, Mário Sílvio Rochinha de AndradeGuerrero, IgnacioHostingInstitutionOrganizationalRUNe-mailmailto:run@unl.ptrun@unl.ptURNurn:tid:2018925022018-04-03T14:50:33Z2018-02-272018-02-27T00:00:00ZHandlehttp://hdl.handle.net/10362/33711http://purl.org/coar/access_right/c_abf2open accessRemote sensingImage segmentationImage classificationObject-oriented image analysisSingle class learningOne class support vector machineExpert system classification3048403 bytesliteraturehttp://purl.org/coar/resource_type/c_bdccmaster thesis2018-02-27http://creativecommons.org/licenses/by/4.0/http://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://run.unl.pt/bitstreams/ea0d590b-c751-4cc7-addf-70b89612a692/download |
| spellingShingle | Slum mapping : a comparison of single class learning and expert system object-oriented classification for mapping slum settlements in Addis Ababa city, Ethiopia Tesfay, Mulu Weldegebreal Remote sensing Image segmentation Image classification Object-oriented image analysis Single class learning One class support vector machine Expert system classification |
| status | SINGLETON |
| subject.fl_str_mv | Remote sensing Image segmentation Image classification Object-oriented image analysis Single class learning One class support vector machine Expert system classification |
| title | Slum mapping : a comparison of single class learning and expert system object-oriented classification for mapping slum settlements in Addis Ababa city, Ethiopia |
| title_full | Slum mapping : a comparison of single class learning and expert system object-oriented classification for mapping slum settlements in Addis Ababa city, Ethiopia |
| title_fullStr | Slum mapping : a comparison of single class learning and expert system object-oriented classification for mapping slum settlements in Addis Ababa city, Ethiopia |
| title_full_unstemmed | Slum mapping : a comparison of single class learning and expert system object-oriented classification for mapping slum settlements in Addis Ababa city, Ethiopia |
| title_short | Slum mapping : a comparison of single class learning and expert system object-oriented classification for mapping slum settlements in Addis Ababa city, Ethiopia |
| title_sort | Slum mapping : a comparison of single class learning and expert system object-oriented classification for mapping slum settlements in Addis Ababa city, Ethiopia |
| topic | Remote sensing Image segmentation Image classification Object-oriented image analysis Single class learning One class support vector machine Expert system classification |
| topic_facet | Remote sensing Image segmentation Image classification Object-oriented image analysis Single class learning One class support vector machine Expert system classification |
| url | http://hdl.handle.net/10362/33711 |
| visible | 1 |