Bipolar eigenspace separation transformation for automatic clutter rejection

A major problem for a detection algorithm is the vast amount of false alarms normally generated. This amount of false alarms has to be substantially reduced so that a typical target classifier in the subsequent stage may work reasonably. We use the bipolar eigenspace separation transformation (BEST) and neural network techniques to improve the clutter rejection performance of an automatic target detector. Experiments have been conducted on huge and realistic datasets of forward looking infrared (FLIR) imagery. Compared to the performance of the unipolar EST and principal component analysis (PCA) with the same datasets, significant improvement in clutter rejection rates has been achieved with BEST.

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