Synthetic Infrared Data For Target Identification Training and Testing

The performance of infrared (IR) target identification classifiers, trained on randomly selected subsets of target chips taken from larger databases of either synthetic or measured data, is shown to improve rapidly with increasing subset size. This increase continues until the new data no longer provides additional information at which point classifier performance levels off. It will also be shown that subsets of data selected with advanced knowledge can significantly outperform randomly selected sets, suggesting that classifier training-sets must be carefully selected if optimal performance is desired. Performance will also be shown to be dependent on the quality of data used to train the classifier. Thus while increasing training set size generally improves classifier performance, the level of classifier performance improvement will be shown to depend on the similarity between the training data and testing data. In fact, if the training data to be added to a given set of training data is unlike the testing data, performance will often not improve but may possibly diminish. Having too much data can be as bad as having too little. Our results again [1] demonstrate that an IR target-identification classifier, trained on synthetic images of targets and tested on measured images, can perform as well as a classifier trained on measured images alone. We also demonstrate that the combination of the measured and the synthetic image databases can be used to train a classifier whose performance exceeds that of classifiers trained on either database alone. Results suggest that it may be possible to select data subsets from image databases that can optimize target classifiers performance for specific locations and operational scenarios.