Algorithms for chemical detection, identification and quantification for thermal hyperspectral imagers

Standoff detection, identification and quantification of chemicals in the gaseous state are fundamental needs in several fields of applications. Sensor requirements derived from these applications include high sensitivity, low false alarms and real-time operation, all in a compact and robust package suitable for field use. The thermal infrared portion of the electromagnetic spectrum has been utilized to implement such chemical sensors, either with spectrometers (with no or moderate imaging capability) or with imagers (with moderate spectral capability). Only with the recent emergence of high-speed, large format infrared imaging arrays has it been possible to design chemical sensors offering uncompromising performance in the spectral, spatial, as well as the temporal domain. It is clear from analytical studies that the combined spatial and spectral information holds enormous promises on improving the current performance of passive detection, identification and quantification of chemical agents. This paper presents detection, identification and quantification algorithms developed for hyperspectral imagers operating in the thermal infrared. The effectiveness of these algorithms is illustrated using gaseous releases datacubes acquired using the Telops FIRST imaging spectrometer in the field.