The development of a method for semi-automatic classification of built-up areas from aerial imagery

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dc.contributor.advisorSmit, Julianen_ZA
dc.contributor.authorDuncan, Patriciaen_ZA
dc.date.accessioned2014-07-31T10:24:32Z
dc.date.available2014-07-31T10:24:32Z
dc.date.issued2013en_ZA
dc.descriptionIncludes abstract.
dc.descriptionIncludes bibliographical references.
dc.description.abstractIt is essential for geospatial and mapping organisations that changes to the landscapeare regularly detected and captured, so that map databases can be updated. The Chief Directorate of National Geospatial Information (CD: NGI), South Africa’s national mapping agency, currently relies on manual methods for digitizing features and detecting changes. These methods are time consuming and labour intensive, and rely on the skills and interpretation of the operator. It is therefore necessary to move towards more automated methods in the production process at CD: NGI. The objective of this research is to develop a process for semi-automatic classification of built-up areas from aerial imagery in South Africa. Built-up areas are important as they can grow and change rapidly. Since the South African landscape is varied and climatological conditions differ from one area to another, a general and robust method that can be applied across the country is needed. This project aims to find the best approach for classifying urban built-up areas from high-resolution aerial imagery by comparing various image classification methods, so that a method that is transferable and applicable in diverse South African scenes may be developed. Image classification methods were compared and it was found that pixel-based classifiers were unsatisfactory in classifying built-up areas, whereas object-based classifiers had better results. Image segmentation, the first step in an object-based classification, can considerably influence the results of the classification task. It is therefore essential that suitable image segments be generated before the segments are classified. The proposed The proposed methodology involves the use of cadastral data in the image segmentation process and texture measures in the classification of built-up areas within an object-based process. The method can be applied to diverse scenes across South Africa to find built-up areas. This is a generalised approach and can assist the CD: NGI in the process of updating their topographic database by reducing the time that operators spend on identifying and manually digitizing built-up areas.en_ZA
dc.identifier.apacitationDuncan, P. (2013). <i>The development of a method for semi-automatic classification of built-up areas from aerial imagery</i>. (Thesis). University of Cape Town ,Faculty of Engineering & the Built Environment ,Division of Geomatics. Retrieved from http://hdl.handle.net/11427/4993en_ZA
dc.identifier.chicagocitationDuncan, Patricia. <i>"The development of a method for semi-automatic classification of built-up areas from aerial imagery."</i> Thesis., University of Cape Town ,Faculty of Engineering & the Built Environment ,Division of Geomatics, 2013. http://hdl.handle.net/11427/4993en_ZA
dc.identifier.citationDuncan, P. 2013. The development of a method for semi-automatic classification of built-up areas from aerial imagery. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Duncan, Patricia AB - It is essential for geospatial and mapping organisations that changes to the landscapeare regularly detected and captured, so that map databases can be updated. The Chief Directorate of National Geospatial Information (CD: NGI), South Africa’s national mapping agency, currently relies on manual methods for digitizing features and detecting changes. These methods are time consuming and labour intensive, and rely on the skills and interpretation of the operator. It is therefore necessary to move towards more automated methods in the production process at CD: NGI. The objective of this research is to develop a process for semi-automatic classification of built-up areas from aerial imagery in South Africa. Built-up areas are important as they can grow and change rapidly. Since the South African landscape is varied and climatological conditions differ from one area to another, a general and robust method that can be applied across the country is needed. This project aims to find the best approach for classifying urban built-up areas from high-resolution aerial imagery by comparing various image classification methods, so that a method that is transferable and applicable in diverse South African scenes may be developed. Image classification methods were compared and it was found that pixel-based classifiers were unsatisfactory in classifying built-up areas, whereas object-based classifiers had better results. Image segmentation, the first step in an object-based classification, can considerably influence the results of the classification task. It is therefore essential that suitable image segments be generated before the segments are classified. The proposed The proposed methodology involves the use of cadastral data in the image segmentation process and texture measures in the classification of built-up areas within an object-based process. The method can be applied to diverse scenes across South Africa to find built-up areas. This is a generalised approach and can assist the CD: NGI in the process of updating their topographic database by reducing the time that operators spend on identifying and manually digitizing built-up areas. DA - 2013 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2013 T1 - The development of a method for semi-automatic classification of built-up areas from aerial imagery TI - The development of a method for semi-automatic classification of built-up areas from aerial imagery UR - http://hdl.handle.net/11427/4993 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/4993
dc.identifier.vancouvercitationDuncan P. The development of a method for semi-automatic classification of built-up areas from aerial imagery. [Thesis]. University of Cape Town ,Faculty of Engineering & the Built Environment ,Division of Geomatics, 2013 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/4993en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDivision of Geomaticsen_ZA
dc.publisher.facultyFaculty of Engineering and the Built Environment
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherArchitecture, Planning and Geomaticsen_ZA
dc.titleThe development of a method for semi-automatic classification of built-up areas from aerial imageryen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMScen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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