Building Detection From Pan-Sharpened Ikonos Imagery Through Support Vector Machines Classification

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dc.contributor.authorTürker, M.
dc.contributor.authorKoç Şan, Dilek
dc.date.accessioned2020-03-26T17:47:18Z
dc.date.available2020-03-26T17:47:18Z
dc.date.issued2010
dc.departmentSelçuk Üniversitesien_US
dc.description8th Symposium on Networking the World with Remote Sensing of ISPRS-Technical-Commission -- AUG 09-12, 2010 -- Kyoto, JAPANen_US
dc.description.abstractAn approach is presented for detecting the buildings from high resolution pan-sharpened IKONOS imagery through binary Support Vector Machines (SVM) classification. In addition to original spectral bands, the bands nDSM (normalized Digital Surface Model), NDVI (Normalized Difference Vegetation Index), PC1, PC2, PC3, and PC4 (First, Second, Third, and Fourth Principal Components), are also included in the classification. The proposed classification procedure was carried out in three study areas selected in the Batikent district of Ankara, Turkey. The study areas show different residential and industrial characteristics. The first study area covers mainly the residential parts that include buildings with different shapes, sizes, dwelling types, and colored roofs. The second study area also represents the residential characteristics but contains buildings with more regular shapes. The third study area contains the industrial buildings with the gray tone roofs and the sizes of the buildings are larger. Also tested in the present study is the effect of the training sample size in the accuracy of the SVM classification. The results reveal that the overall accuracies were computed to be between 90% and 99%, while the kappa coefficients were found to be between 0.80 and 0.98. The inclusion of additional bands in the SVM classification had a considerable effect in the accuracy of building detection. Increasing the training size increased the accuracy, however, the increase was not more than 3%.en_US
dc.description.sponsorshipISPRS Tech Commissen_US
dc.description.sponsorshipState Planning Organization (DPT)Turkiye Cumhuriyeti Kalkinma Bakanligi [BAP-08-11-DPT2002K120510]en_US
dc.description.sponsorshipThis research was supported by the State Planning Organization (DPT) Grants: BAP-08-11-DPT2002K120510.en_US
dc.identifier.citationTürker, M., Koç Şan, D., (2010). Building Detection From Pan-Sharpened Ikonos Imagery Through Support Vector Machines Classification. Networking the World with Remote Sensing, (38), 841-846.
dc.identifier.endpage846en_US
dc.identifier.issn2194-9034en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage841en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12395/24668
dc.identifier.volume38en_US
dc.identifier.wosWOS:000341930000179en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorKoç Şan, Dilek
dc.language.isoenen_US
dc.publisherCopernicus Gesellschaft Mbhen_US
dc.relation.ispartofNetworking the World with Remote Sensingen_US
dc.relation.ispartofseriesInternational Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectBuilding detectionen_US
dc.subjectClassificationen_US
dc.subjectSupport vector machinesen_US
dc.subjectIkonosen_US
dc.subjectNdsmen_US
dc.subjectNdvıen_US
dc.titleBuilding Detection From Pan-Sharpened Ikonos Imagery Through Support Vector Machines Classificationen_US
dc.typeConference Objecten_US

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