Building Extraction From High Resolution Satellite Images Using Hough Transform

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dc.contributor.authorKoç Şan, Dilek
dc.contributor.authorTürker, M.
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 was developed for the automatic extraction of the rectangular and circular shaped buildings from high resolution satellite imagery using Hough transform. First, the candidate building patches are detected from the imagery using the binary Support Vector Machines (SVM) classification technique. In addition to original image bands, the bands NDVI (Normalized Difference Vegetation Index), and nDSM (normalized Digital Surface Model) are also used in the classification. After detecting the building patches, their edges are detected using the Canny edge detection algorithm. The edge image is then converted into vector form using the Hough transform, which is a widely used technique for extracting the lines or curves of the objects. The vector lines and curves that represent the building edges are grouped based on perceptual groupings, and the building boundaries are constructed. The proposed approach was implemented using a program written in MATLAB (R) v. 7.1 programming environment. The experimental tests were carried out in the residential and industrial urban blocks selected in the Batikent district of Ankara, the capital city of Turkey using the pan-sharpened and panchromatic IKONOS images. The results obtained indicate that the proposed building extraction procedure based on SVM and Hough transform can be effectively used to extract the boundaries of the rectangular and circular shaped buildings. For the industrial buildings, we obtained quite satisfactory results with the average Building Detection Percentage (BDP) and the Quality Percentage (QP) values of 93.45% and 79.51%, respectively. For the residential rectangular buildings, the average BDP and QP values were computed to be 95.34% and 79.05%, respectively. For the residential circular buildings, the average BDP and QP values were found to be 78.74% and 66.81%, respectively.en_US
dc.description.sponsorshipISPRS Tech Commissen_US
dc.description.sponsorshipState Planning Organization (DPT)Turkiye Cumhuriyeti Kalkinma Bakanligi [BAP-08-11-DPT2002K120510]; Selcuk University, Scientific Research Projects Department [10701360]en_US
dc.description.sponsorshipThis research was supported by the State Planning Organization (DPT) Grants: BAP-08-11-DPT2002K120510 and by Selcuk University, Scientific Research Projects Department with project no: 10701360.en_US
dc.identifier.citationKoç Şan, D., Türker, M., (2010). Building Extraction From High Resolution Satellite Images Using Hough Transform. Networking the World with Remote Sensing, (38), 1063-1068.
dc.identifier.endpage1068en_US
dc.identifier.issn2194-9034en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage1063en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12395/24669
dc.identifier.volume38en_US
dc.identifier.wosWOS:000341930000230en_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 extractionen_US
dc.subjectSvmen_US
dc.subjectEdge detectionen_US
dc.subjectHough transformen_US
dc.subjectIkonosen_US
dc.titleBuilding Extraction From High Resolution Satellite Images Using Hough Transformen_US
dc.typeConference Objecten_US

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