Improving a Template-Based Classifier in a SAR Automatic Target RecognitionSystem by Using 3-D Target Information. (Reannouncement with New Availability Information)

摘要

In this article we propose an improved version of a conventional template-matching classifier that is currently used in an operational automatic target 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.