In conventional SAR image formation, idealizations are made about the underlying scattering phenomena in the target field. In particular, the reflected signal is modeled as a pure delay and scaling of the transmitted signal where the delay is determined by the distance to the scatterer. Inherent in this assumption is that the scatterers are isotropic, i.e. their reflectivity appears the same from all orientations, and frequency independent, i.e. the magnitude and phase of the reflectivity are constant with respect to the frequency of the transmitted signal. Frequently, these assumptions are relatively poor resulting in an image which is highly variable with respect to imaging aspect. This variability often poses a difficulty for subsequent processing such as ATR. However, this need not be the case if the nonideal scattering is taken into account. In fact, we believe that if utilized properly, these nonideal characteristics may actually be used to aid in the processing as they convey distinguishing information about the content of the scene under investigation. In this paper, we describe a feature set which is specifically motivated by scattering aspect dependencies present in SAR. These dependencies are learned with a nonparametric density estimator allowing the full richness of the data to reveal itself. These densities are then used to determine the classification of the image content.