DETECTION OF MILITARY VEHICLES USING INFRARED SPECTRAL RADIOMETRIC SIGNATURES

Automatic detection of time-critical mobile targets using nonimaging, spectral infrared radiometric target signatures is explored. A novel s et of classification features is developed for the spectral data and u tilized in a Bayesian classifier. The processing results are presented , and sensitivity of the class separability to target set, target conf iguration, diurnal variations, mean contrast, and ambient temperature estimation errors is explored. This work introduces the concept of atm ospheric normalization of classification features, in which feature va lues are normalized using an estimate of the ambient temperature in th e vicinity of the target. The probability of detection, false alarm ra te, and total error rate associated with this detection process is pre sented. Testing on an array of U.S. and foreign military assets reveal s a total error rate near 5% with a 95% probability of detection and a concurrent false alarm rate of 4% when a single classification featur e is employed. Sensitivity analysis indicates that the probability of detection is reduced to 70 to 75% in the hours preceding daylight, and that for the total error rate to be less than 10%, the target-to-back ground mean contrast must be greater than 0.025. Analysis of the atmos pheric normalization technique reveals that to keep the total error ra te less than 10%, the ambient temperature must be estimated with less than 3 K absolute accuracy. (C) 1996 Society of Photo Optical Instrume ntation Engineers.