Includes abstract. / Includes bibliographical references. / 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.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:uct/oai:localhost:11427/4993 |
| Date | January 2013 |
| Creators | Duncan, Patricia |
| Contributors | Smit, Julian |
| Publisher | University of Cape Town, Faculty of Engineering and the Built Environment, Division of Geomatics |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Master Thesis, Masters, MSc |
| Format | application/pdf |
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