Improving aTemplate-Based Classifier in a SARAutomatic Target Recognition System by Using 3-D Target Information

• In this article we propose an improved version of a conventional templatematching classifier that is currently used in an operational automatic targt;t recognition system for synthetic-aperture radar (SAR) imagery. This classifier was originally designed to maintain, for each target type of interest, a library of 2-D reference images (or templates) formed at a variety of radar viewing directions. The classifier accepts an input image of a target of unknown type, correlates this image with a reference template selected (by matching radar viewing direction) from each target library, and then classifies this image to the target category with the highest correlation score. Although this algorithm seems reasonable, it produces surprisingly poor classification results for some target types because of differences in SAR geometry between the input image and the best-matching reference image. Each reference library is indexed solely by radar viewing direction, and is thus unable to account for radar motion direction, which is an equally important parameter in specifying SAR imaging geometry. We correct this deficiency by incorporating a model-based reference generation procedure into the original classifier. The modification is implemented by (1) replacing each library of 2-D templates with a library of 3-D templates representing complete 3-D radar-reflectivity models for the target at each radar viewing direction, and (2) including a mathematical model of the SAR imaging process so that any 3-D template can be transformed into a 2-D image corresponding to the appropriate radar motion direction before the correlation operation is performed. We demonstrate experimentally that the proposed classifier is a promising alternative to the conventional classifier.