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Proceedings Paper

Feature extraction of bridges for change detection in high resolution SAR data
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Paper Abstract

SAR is a remote sensing technique capable to deliver actual data at any time and under bad weather conditions. Before launch of TerraSAR-X, RADARSAT-2, or COSMO-SkyMed, the rather coarse resolution of operational SAR satellite systems allowed an analysis of spaceborne SAR data in case of disaster management only for medium scale products. The new generation of spaceborne SAR satellites permits a more detailed analysis at the object level even for urban areas, which was before restricted to airborne SAR sensors. Change detection in SAR images is an important field of research. In general, the appearance of objects in SAR images strongly depends on the viewing angle and look direction. This makes a comparison of images on a pixel level difficult. The changeover from pixel- to object level leads to the possibility, to look for object-features that are more stable concerning different imaging constellations. Bridges are keyelements of man made infrastructure. In this paper the appearance of bridges in SAR data is analyzed and features are derived that are exploitable for change detection. Here the focus is on analysis at the object level to derive features that are either stable concerning the imaging constellations or that can be predicted based on a given imaging constellation. Thereby, the usage of different sensors will be possible to achieve the goal of real time information. The investigations are supported by simulations, which allow the creation of SAR images for different imaging constellations, bridge materials, and even for situations with destroyed bridges.

Paper Details

Date Published: 10 October 2008
PDF: 10 pages
Proc. SPIE 7110, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VIII, 71100I (10 October 2008); doi: 10.1117/12.800162
Show Author Affiliations
Erich Cadario, Research Institute for Optronics and Pattern Recognition (Germany)
Hermann Gross, Research Institute for Optronics and Pattern Recognition (Germany)
Horst Hammer, Research Institute for Optronics and Pattern Recognition (Germany)
Antje Thiele, Research Institute for Optronics and Pattern Recognition (Germany)
Ulrich Thoennessen, Research Institute for Optronics and Pattern Recognition (Germany)
Karsten Schulz, Research Institute for Optronics and Pattern Recognition (Germany)
Uwe Soergel, Leibniz Univ. Hannover (Germany)
Dan J. Weydahl, Norwegian Defense Research Establishment (Norway)


Published in SPIE Proceedings Vol. 7110:
Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VIII
Ulrich Michel; Daniel L. Civco; Manfred Ehlers; Hermann J. Kaufmann, Editor(s)

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