Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.410016
Title: Airborne SAR/IFSAR for mapping in urban areas
Author: Chayakula, Thongthit
ISNI:       0000 0001 3530 7481
Awarding Body: University of London
Current Institution: University College London (University of London)
Date of Award: 2004
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Abstract:
There are many problems in topographic mapping in an urban area. Traditional land survey is a very time consuming technique and can be very expensive. Photogrammetry is a popular choice but there are some problems such as clouds and limited operational time. Since Synthetic Aperture Radar, (SAR), is an active remote sensing system and its signal can penetrate through clouds, it can be operated at any time of day and is a independent of the weather. SAR could be a good solution for topographic mapping in an urban area. Combining SAR data and Interferometric radar technology can provide enough information for topographic mapping. Information can be extracted from SAR intensity Image. This thesis focuses on feature extraction and classification for topographic mapping in an urban area from airborne interferometric SAR data. A new algorithm is described which is simple and practical but yet very efficient for feature extraction and for object-based feature classification. An adapted Canny-Petrou-Kittler algorithm is applied for edge detection. Since the algorithm provides good detection, good localization and only one response to a single edge, it is an ideal edge detection for dense urban areas. Since the SAR image is noisy by its nature, small weak edges are expected. The modified non-maximal technique is also proposed to reduce unwanted edge. The technique of generation of bald earth DEM is proposed to obtain a normalised DEM for feature extraction. Region growing from edge detection is then applied to extract a more accurate shape of the feature and generate feature surface by using topographic parameters. The extracted feature is then classified by object-oriented classification technique, in which the classification is performed at object level not pixel level. And at the end of the process 3D city model can be produced.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.410016  DOI: Not available
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