Attributing Scatterer Anisotropy for Model Based ATR

Scattering from man-made objects in SAR imagery often exhibit aspect and frequency dependences which are not well modeled by standard SAR imaging techniques. If ignored, these deviations may reduce recognition performance due to model mismatch, but when appropriately accounted for, these deviations can be exploited as attributes to better distinguish scatterers and their respective targets. Chiang and Moses developed an ATR system that allows the study of performance under various scatterer attributions. Kim et. al. examined a nonparametric approach for exploiting non-ideal scattering using a multi-resolution sub-aperture representation. Both of these works are extended here to examine the effect of anisotropic scattering attribution for model-based ATR. In particular, predicted and extracted peak scatterers are attributed with a discrete anisotropy feature. This feature can be obtained in a computationally efficient manner by performing a set of generalized log-likelihood ratio (GLLR) tests over a pyramidal sub-aperture representation. Furthermore, an approximate probabilistic characterization of the feature set allows for a natural inclusion into the approach of Chiang and Moses which will be used to evaluate the benefit of our attribution to the X-band MSTAR data and infer the phenomenology behind anisotropic scattering.