Improving ATR performance through distance metric learning

High resolution synthetic aperture radar images usually contain much redundant, noisy and irrelevant information. Eliminating these information or extracting only useful information can enhance ATR performance, reduce processing time and increase the robustness of the ATR systems. Most existing feature extraction methods are either computationally expensive or can only provide ad hoc solutions and have no guarantee of optimality. In this paper, we describe a new distance metric learning algorithm. The algorithm is based on the local learning strategy and is formulated as a convex optimization problem. The algorithm not only is capable of learning the feature significance and feature correlations in a high dimensional space but also is very easy to implement with guaranteed global optimality. Experimental results based on the MSTAR database are presented to demonstrate the effectiveness of the new algorithm.