The detection of long range air targets in a Naval scenario using passive Imaging Infra-Red sensors is a task of primary importance for current and next generation Naval equipment. The authors have investigated Dynamic Programming based target detection systems utilising the output ofan image filter as the input to a likelihood classifier based on intensity alone. Variations ofthis technique have been proven to offer high sensitivity to dim targets though environmental characteristics in the Naval scenario can give rise to clutter induced false alarms. The work presented herein investigates augmentation of the intensity classifier with textural analysis techniques on IR imagery in the 3-5 micron waveband to assist in false alarm discrimination. It is shown that augmentation with a textural classifier can improve rejection of false alarms due to clutter. This work is part ofan ongoing programme ofIRST and Surveillance Sensor processing development.

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