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Proceedings Paper

SAR target detection by fusion of CFAR, variance, and fractal statistics
Author(s): Lance M. Kaplan; Romain Murenzi; Kameswara Rao Namuduri
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Paper Abstract

Two texture-based and one amplitude-based features are evaluated as detection statistics for synthetic aperture radar (SAR) imagery. The statistics include a local variance, an extended fractal, and a two-parameter CFAR feature. The paper compares the effectiveness of focus of attention (FOA) algorithms that consist of any number of combinations of the three statistics. The public MSTAR database is used to derive receiver-operator-characteristic (ROC) curves for the different detectors at various signal-to-clutter rations (SCR). The database contains one foot resolution X-band SAR imagery. The results in the paper indicate that the extended fractal statistic provides the best target/clutter discrimination, and the variance statistic is the most robust against SCR. In fact, the extended fractal statistic combines the intensity difference information used also by the CFAR feature with the spatial extent of the higher intensity pixels to generate an attractive detection statistics.

Paper Details

Date Published: 17 July 1998
PDF: 12 pages
Proc. SPIE 3374, Signal Processing, Sensor Fusion, and Target Recognition VII, (17 July 1998); doi: 10.1117/12.327094
Show Author Affiliations
Lance M. Kaplan, Clark Atlanta Univ. (United States)
Romain Murenzi, Clark Atlanta Univ. (United States)
Kameswara Rao Namuduri, Clark Atlanta Univ. (United States)


Published in SPIE Proceedings Vol. 3374:
Signal Processing, Sensor Fusion, and Target Recognition VII
Ivan Kadar, Editor(s)

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