Dual-band passive infrared imagery for automatic clutter rejection
In a typical automatic target recognition (ATR) system, the target detection module often produces too many false alarms, which may severely inhibit the effectiveness of the subsequent target classifier module. An effective clutter rejector is therefore needed between these two modules to reduce these false alarms. We explore the potential benefits of using dual-band infrared imagery to improve the performance of an eigenneural-based clutter rejector. Individual or combined bands of images are first compressed through eigenspace transformations, such as principal component analysis. The transformed data are then fed to a neural network that decides whether the input is a target or clutter. A huge and realistic set of dual-band passive infrared images was used in a series of experiments.