This paper demonstrates an image matching methodology for application
in automatic target recognition systems. This method is based on chunking of an image and can be applied to any image matching system that uses templates to match against a given input image. Using information theoretical measures, templates are divided into sub-parts, called chunks. These chunks are scored individually against corresopnding parts of an input image. Sub-part scoring adds the ability to distinguish poorly matching areas of the target from those that match well. If a very small set of chunks score significantly worse than the other chunks then the poor-scoring chunks maybe discarded. This increases the scores of an input image that is of the same class but there is little or no effect on the score of an input image that is of another class.
We investigate the complexity of template-based ATR algorithms using SAR imagery as an example. Performance measures (such as Pid) of such algorithms typically improve with increasing number of stored reference templates. This presumes, of course, that the training templates contain adequate statistical sampling of the range of observed or test templates. The tradeoff of improved performance is that computational complexity and the expense of algorithm development training template generation (synthetic and/or experimental) increases as well. Therefore, for practical implementations it is useful to characterize ATR problem complexity and to identify strategies to mitigate it. We adopt for this problem a complexity metric defined simply as the size of the minimal subset of stored templates drawn from an available training population that yields a specified Pid. Straightforward enumeration and testing of all possible template sets leads to a combinatorial explosion. Here we consider template selection strategies that are far more practical and apply these to a SAR- and template-based target identification problem. Our database of training templates consists of targets viewed at 3-degree increments in pose (azimuth). The template selection methods we investigate include uniform sampling, sequential forward search (also known as greedy selection), and adaptive floating search. The numerical results demonstrate that the complexity metric increases with intrinsic problem difficulty, and that template sets selected using the greedy method significantly outperform uniformly sampled template sets of the same size. The adaptive method, which is far more computationally expensive, selects template sets that outperform those selected by the greedy technique, but the small reduction in template set size was not significant for the specific examples considered here.
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