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We present a fast, scalable method to simultaneously register and classify vehicles in circular synthetic aperture radar imagery. The method is robust to occlusions and partial matches. Images are represented as a set of attributed scattering centers that are mapped to local sets, which are invariant to rigid transformations. Similarity between local sets is measured using a method called pyramid...
Including polarization to circular synthetic aperture radar imagery increases the diversity of information as compared to non-polarized collections. The additional information improves classification performance in a civilian vehicle identification application. Effects of azimuth extent are also investigated. Radar imagery is represented as sets of attributed scattering centers, and vehicles are identified...
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