SAR target detection by fusion of CFAR, variance, and fractal statistics

Lance M. Kaplan; Romain Murenzi; Kameswara Rao Namuduri

doi:10.1117/12.327094

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17 July 1998 SAR target detection by fusion of CFAR, variance, and fractal statistics

Lance M. Kaplan; Romain Murenzi; Kameswara Rao Namuduri

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Proceedings Volume 3374, Signal Processing, Sensor Fusion, and Target Recognition VII; (1998) https://doi.org/10.1117/12.327094
Event: Aerospace/Defense Sensing and Controls, 1998, Orlando, FL, United States

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.

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Lance M. Kaplan, Romain Murenzi, and Kameswara Rao Namuduri "SAR target detection by fusion of CFAR, variance, and fractal statistics", Proc. SPIE 3374, Signal Processing, Sensor Fusion, and Target Recognition VII, (17 July 1998); https://doi.org/10.1117/12.327094

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