Aspect dependent drivers for multi-perspective target classification

In this paper, a 2-D classifier using radial basis function neural networks (RBFNNs) has been implemented combining two imageries collected by different locations to prove the classification rates enhancement given by aspect diversification. Principal components analysis (PCA) is applied to features extracted from a masked version of the SAR image using the sole target's backscattering and shadow information. The classification performance, examined in terms of receiver operator characteristic (ROC) curves is presented using MSTAR data for a population formed by six classes plus two unknown and two independent targets. The resulting performance shows a reduction of the probability of false alarm, related to an improvement of probability of declaration and correct classification in comparison with the traditional single aspect case.