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Title: Automatic matching of features in Synthetic Aperture Radar data to digital map data
Author: Caves, Ronald George
ISNI:       0000 0001 3525 4455
Awarding Body: University of Sheffield
Current Institution: University of Sheffield
Date of Award: 1993
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The large amounts of Synthetic Aperture Radar (SAR) data now being generated demand automatic tools for image interpretation. Where available, map data provides a valuable aid for visual interpretation and it should aid automatic interpretation. Automatic map based interpretation will be heavily dependent on methods for matching image and map features, both for defining the initial registration and for comparing image and map. This thesis investigates methods for carrying out this matching. Before beginning to develop image map matching methods, a full understanding of the nature of SAR data is first required. The general theory of SAR imaging, the effects of speckle and texture on image statistics, multi-look image statistics, and parameter estimation, are all discussed before addressing the main subject matter. Initially the feasibility of directly matching map features to SAR image features is investigated. Simulations based on a simple image model produce promising results. However, the results of matching features in real images are disappointing. This is due to the limitations of the image model on which matching is based. Possible extensions to include texture and correlation are considered to be computationally too expensive. Rather, it is concluded that pre-processing is needed to structure the image prior to matching. Structuring using edge detection and segmentation are investigated. Among operators for detecting edges in SAR an operator based on intensity ratios is identified as the most suitable. Its performance is fully analysed. Segmentation using an iterative edge detection/segment growing algorithm developed at the Royal Signals and Radar Establishment is investigated and various improvements are suggested. The output of segmentation is structured to a higher level than the output of edge detection. Thus the former is the more suitable candidate for map matching. Approaches to matching segmentations to map data are discussed.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Image interpretation