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Proceedings Paper

CovAmCoh-analysis: a method to improve the interpretation of high resolution repeat pass SAR images of urban areas
Author(s): Karsten Schulz; Markus Boldt; Antje Thiele
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Paper Abstract

The main advantages of SAR (Synthetic Aperture Radar) are the availability of data under nearly all weather conditions and its independence from natural illumination. Data can be gathered on demand and exploited to extract the needed information. However, due to the side looking imaging geometry, SAR images are difficult to interpret and there is a need for support of human interpreters by image analysis algorithms. In this paper a method is described to improve and to simplify the interpretation of high resolution repeat pass SAR images. Modern spaceborne SAR sensors provide imagery with high spatial resolution and the same imaging geometry in an equidistant time interval. These repeat pass orbits are e. g. used for interferometric evaluation. The information contained in a repeat pass image pair is visualized by the introduced method so that some basic features can be directly extracted from a color representation of three deduced features. The CoV (Coefficient of Variation), the amplitude and the coherence are calculated and jointly evaluated. The combined evaluation of these features can be used to identify regions dominated by volume scatterers (e. g. leafed vegetation), rough surfaces (e. g. grass, gravel) and smooth surfaces (e. g. streets, parking lots). Additionally the coherence between the two images includes information about changes between the acquisitions. The potential of the CovAmCoh- Analysis is demonstrated and discussed by the evaluation of a TerraSAR-X image pair of the Frankfurt airport. The method shows a simple way to improve the intuitive interpretation by the human interpreter and it is used to improve the classification of some basic urban features.

Paper Details

Date Published: 7 October 2009
PDF: 9 pages
Proc. SPIE 7478, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology IX, 747805 (7 October 2009); doi: 10.1117/12.830441
Show Author Affiliations
Karsten Schulz, FGAN-Research Institute for Optronics and Pattern Recognition (Germany)
Markus Boldt, FGAN-Research Institute for Optronics and Pattern Recognition (Germany)
Antje Thiele, FGAN-Research Institute for Optronics and Pattern Recognition (Germany)


Published in SPIE Proceedings Vol. 7478:
Remote Sensing for Environmental Monitoring, GIS Applications, and Geology IX
Ulrich Michel; Daniel L. Civco, Editor(s)

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