24 August 2000 Modeling artificial synthetic aperture radar clutter scenes using spatial autocorrelation and group statistics
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Synthetic Aperture Radar (SAR) image scene modeling tools are of high interest to Automatic Target Recognition (ATR) algorithm evaluation because they allow the testing of ATR's over a wider range of extended operating conditions (EOCs). Typical EOCs include target aspect, target configuration, target obscuration, and background terrain variations. A baseline terrain image synthesis technique empirically derived probability density functions (pdfs) for various terrain types from measured data to allow the simulation of user defined scenes. Initial full scene simulation experiments that applied this technique to the MSTAR data showed that using measured images as a data source for creating distribution functions in artificial scenes can introduce error, unless the proper spatial autocorrelation is also modeled. Measured SAR scene pixels have non-zero autocorrelation that blurs edges between different terrain types and creates a texture in the clutter regions of the image. Unfortunately, applying simple blurring techniques, such as a moving weighted window, to model autocorrelation mutes the second and higher moments of the pixel amplitude statistics. We propose a technique that models spatial autocorrelation while preserving the desired the amplitude statistics within each defined terrain class group.
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Kelce S. Wilson, Kelce S. Wilson, Patricia A. Ryan, Patricia A. Ryan, Chahira M. Hopper, Chahira M. Hopper, } "Modeling artificial synthetic aperture radar clutter scenes using spatial autocorrelation and group statistics", Proc. SPIE 4053, Algorithms for Synthetic Aperture Radar Imagery VII, (24 August 2000); doi: 10.1117/12.396371; https://doi.org/10.1117/12.396371


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