Improved feature classification by means of a polarimetric IR imaging sensor

Automatic detection and recognition of targets by means of passive IR sensors suffer from limitations due to lack of sufficient contrast between the targets and their background,and among the facets of a target. In this paper the results of a suite of polarization-sensitive automatic target detection and recognition algorithms on sets of simulated and real polarimetric IR imagery are presented. A custom designed Polarimetric IR (PIR) imaging sensor is used for collecting real polarimetric target data under a variety of conditions. Then a set of novel algorithms are designed and tested that uses the target and background Stokes parameters for detection, segmentation and classification of targets. The empirical performance results in terms of the probabilities of detection, false alarm rate, segmentation accuracy, and recognition probabilities as functions of number of pixels on target, aspect and depression angles and several background conditions (clutter densities) of applying this ATR algorithms on the polarimetric data and its comparison with a typical IR only ATR are demonstrated that shows that a noticeable improvement can be achieved