Automatic road network extraction from high resolution satellite imagery using spectral classification methods

Road networks play an important role in a number of geospatial applications, such as cartographic, infrastructure planning and traffic routing software. Automatic and semi-automatic road network extraction techniques have significantly increased the extraction rate of road networks. Automated processes still yield some erroneous and incomplete results and costly human intervention is still required to evaluate results and correct errors. With the aim of improving the accuracy of road extraction systems, three objectives are defined in this thesis: Firstly, the study seeks to develop a flexible semi-automated road extraction system, capable of extracting roads from QuickBird satellite imagery. The second objective is to integrate a variety of algorithms within the road network extraction system. The benefits of using each of these algorithms within the proposed road extraction system, is illustrated. Finally, a fully automated system is proposed by incorporating a number of the algorithms investigated throughout the thesis. Copyright

Grantor dc:publisher

University of Pretoria

Year dc:date.issued

2010

Advisor dc:contributor.advisor

• Engelbrecht, Andries P.

• Spectral classification
• Edge detection
• Remote sensing analysis
• Feature extraction
• Segmentation
• Automated road network extraction
• UCTD

Statement dc:rights

• © 2010, University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria.

Dc Identifier Other

C10/513/gm

OAI identifier oai:identifier

oai:repository.up.ac.za:2263/26866