Automatic Target Recognition for Hyperspectral Imagery

Automatic target detection and recognition in hyperspectral imagery offer passive means to detect and identify anomalies based on their material composition. In many combat identification approaches through pattern recognition, a minimum level of confidence is expected with costs associated with labeling anomalies as targets, non-targets or out-of-library. This research approaches the problem by developing a baseline, autonomous four step automatic target recognition (ATR) process: 1) anomaly detection, 2) spectral matching, 3) out-of-library decision, and 4) non-declaration decision. Atmospheric compensation techniques are employed in the initial steps to compare truth library signatures and sensor processed signatures. ATR performance is assessed and additionally contrasted to two modified ATRs to study the effects of including steps three and four. Also explored is the impact on the ATR with two different anomaly detection methods

Ballistic Flash Characterization: Penetration and Back-face Flash

The Air Force is extremely concerned with the safety of its people, especially those who are flying aircraft. Aircrew members flying combat missions are concerned with the chance that a fragment from an exploding threat device may penetrate into the airframe to possibly ignite a fire onboard the aircraft. One concern for vulnerability revolves around a flash that may occur when a projectile strikes and penetrates an aircraft\u27s fuselage. When certain fired rounds strike the airframe, they break into fragments called spall. Spall and other fragmentation from an impact often gain enough thermal energy to oxidize the materials involved. This oxidation causes a flash. To help negate these incidents, analysts must be able to predict the flash that can occur when a projectile strikes an aircraft. This research directly continues AFIT work for the 46th Test Group, Survivability Analysis Flight, by examining models to predict the likelihood of penetration of a fragment fired at a target. Empirical live-fire fragment test data are used to create an empirical model of a flash event. The resulting model provides an initial back-face flash modeling capability that can be implemented in joint survivability analysis models