A clutter rejection technique for FLIR imagery using region-based principal component analysis

The preprocessing stage of an automatic target recognition system extracts areas containing potential targets from a battlefield scene. These potential target images are then sent to the classification stage to identify the targets. It is highly desirable at the preprocessing stage to minimize the incorrect rejection rate. This, however, results in a high false alarm rate. The high false alarm rate, in turn, makes subsequent target classification decisions unreliable. We present a new technique to reject false alarms (clutter images) produced by the preprocessing stage. Our technique, which we call region-based principal component analysis (PCA), uses topological features of the targets to reject false alarms. In this technique a potential target is divided into several regions and a PCA is performed on each region to extract regional feature vectors. We propose to use regional feature vectors of arbitrary shapes and dimensions that are optimized for the topology of a target in a particular region. These regional feature vectors are then used by a two-class classifier based on the learning vector quantization to decide whether a potential target is a false alarm or a real target