TARGETING AND INTELLIGENCE ELECTROOPTICAL RECOGNITION MODELING - A JUXTAPOSITION OF THE PROBABILITIES OF DISCRIMINATION AND THE GENERAL IMAGE QUALITY EQUATION

The recognition of objects using target acquisition systems is modeled by a sensor's minimum resolvable temperature (MRT), the Johnson crite ria, atmospherics, and object specifics. Collectively, these three cha racteristics provide an acquisition model for estimating the probabili ty of object recognition (and detection, identification) as a function of sensor-to-object range. This technique is called the probabilities of discrimination. When quantifying the performance of intelligence-s urveillance-reconnaissance (ISR) systems, object recognition is assess ed using the National Imagery Interpretability Scale (NIIRS). Each NII RS level corresponds to a different capacity for object recognition an d is defined by a set of recognition criteria. The general image quali ty equation (GIQE) is the ISR sensor model that determines the expecte d NIIRS level of a sensor for a given set of sensor parameters. It is important that electro-optical sensor engineers understand both of the se recognition models. The segregation between the target acquisition and ISR sensor communities is becoming less sharp as ISR sensors are b eginning to be used for target acquisition purposes and visa versa. Ne twork and wireless communication advances are providing the means for dual exploitation of these systems. Descriptions of these two recognit ion models, probabilities of discrimination, and the GIQE are provided . The two models are applied to example systems. Finally, the two mode ls are compared and contrasted. (C) 1998 Society of Photo-Optical Inst rumentation Engineers.[S0091-3286(98)00503-0].