Automatic target detection using dualband infrared imagery

An automatic target detector often produces too many false alarms that could bog down the performance of subsequent target classifier Therefore, we need a good clutter rejector to remove as many clutters as possible, before feeding the most likely target detections to the classifier. We investigate the benefits of using dual-band foward-looking infrared images to improve the performance of an eigen-neural based clutter rejector. With individual or combined bands as input, we use either principal component analysis or the eigenspace separation transform to perform feature extraction and dimensionality reduction. The transformed data is then fed to a properly trained MLP that predicts the identity of the input, which is either a target or clutter. Experimental results are presented on a dataset of real dual-hand images.