Signal processing algorithms for hyperspectral remote sensing of chemical plumes

Long-wave infrared (LWIR) hyperspectral imaging sensors are widely used for the detection and identification of released chemical agents in many civilian and military applications. Current hyperspectral system capabilities are limited by variation in the background clutter as opposed to the physics of photon detection. Hence, the development of statistical models for background clutter and optimum signal processing algorithms that exploit these models are essential for the design of practical systems that satisfy user´s requirements. This paper describes a signal processing system for the detection and identification of released chemical agents developed at MIT Lincoln Laboratory. We discuss the underlying signal models, key detection and identification algorithms, and some areas where the signal processing community could contribute.