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Modern spectral estimation methods applied to FOPEN SAR imagery

The automatic target recognition (ATR) of targets obscured by forest canopy in FOliage PENetration (FOPEN) synthetic aperture radar (SAR) imagery is difficult due to poor resolution and the electromagnetic distortion introduced by the forest canopy. In this thesis we have investigated the application of modern spectral estimation methods, which reduce mainlobe width and lower sidelobe amplitude, to FOPEN SAR imagery in the hope that improved resolution will lead to improved ATR performance. We applied the modern spectral estimation methods to images polarimetrically processed by methods such as the SPAN filter, the polarimetric matched filter (PMF), and the polarimetric whitening filter (PWF). The discrimination performance of each modern spectral estimation method was tested on Gaussian classifiers that discriminate using geometric features and on Gaussian classifiers that discriminate using polarimetric ratio features. Initial results indicate that the modern spectral estimation methods investigated in this thesis do not provide significantly improved discrimination performance using geometric features to discriminate targets from clutter. Discrimination performance was improved in some cases using polarimetric ratio features. Thesis Supervisor: Leslie M. Novak Title: Senior Staff, MIT Lincoln Laboratory Thesis Supervisor: Jeffrey H. Shapiro Title: Julius A. Stratton Professor of Electrical Engineering