Performance modeling of feature-based classification in SAR imagery

We present a novel method for modeling the performance of a vote-based approach for target classification in SAR imagery. In this approach, the geometric locations of the scattering centers are used to represent 2-D model views of a 3-D target for a specific sensor under a given viewing condition (azimuth, depression and squint angles). Performance of such an approach is modeled in the presence of data uncertainty, occlusion, and clutter. The proposed method captures the structural similarity between model views, which plays an important role in determining the classification performance. In particular, performance would improve if the model views are dissimilar and vice versa. The method consists of the following steps. In the first step, given a bound on data uncertainty, model similarity is determined by finding feature correspondence in the space of relative translations between each pair of model views. In the second step, statistical analysis is carried out in the vote, occlusion and clutter space, in order to determine the probability of misclassifying each model view. In the third step, the misclassification probability is averaged for all model views to estimate the probability-of-correct-identification (PCI) plot as a function of occlusion and clutter rates. Validity of the method is demonstrated by comparing predicted PCI plots with ones that are obtained experimentally. Results are presented using both XPATCH and MSTAR SAR data.