A electro-optic (EO) and infrared (IR) automatic target recognition (ATR) system based on the minimum noise
and correlation energy (MINACE) distortion invariant filter (DIF) is presented. The system uses exceptionally high
resolution EO and IR data obtained from the Shared Reconnaissance Pod (SHARP). Excellent detection results are
obtained. Furthermore, the selection of a key parameter - the MINACE filter parameter c - is fully automated using a
training and validation set. We also present a set of correlation plane post processing methods to reduce false alarms and
improve detection accuracies. The system is evaluated using multi-sensor imagery acquired using the SHARP sensor
suite, the detection (PD) and false alarm (PFA) scores are presented for the problem of detecting aircrafts in the high
resolution imagery. The scale and orientation of the targets are not assumed to be known, thus making the problem more
realistic.
A system to detect vehicles (cars, trucks etc) in electro-optic (EO) and infrared (IR) imagery is presented. We present the
use of the minimum noise and correlation (MINACE) distortion invariant filter (DIF) for this problem. The selection of
the MINACE filter parameter c is automated using a training and validation set. A new set of correlation plane post
processing methods that improve detection accuracies and reduce false alarms are presented. The system is tested on real
life imagery of traffic in parking lots and roads obtained using a multi-sensor EO/IR platform.
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