A taxonomy of algorithms for chemical vapor detection with hyperspectral imaging spectroscopy

Remote detection of chemical vapors in the atmosphere has a wide range of civilian and military applications. In the past few years there has been significant interest in the detection of effluent plumes using hyperspectral imaging spectroscopy in the 8-12- m atmospheric window. A major obstacle in the full exploitation of this technology is the fact that everything in the infrared is a source of radiation. As a result, the emission from the gases of interest is always mixed with emission by the more abundant atmospheric constituents and by other objects in the sensor field of view. The radiance fluctuations in this background emission constitute an additional source of interference which is much stronger than the detector noise. The purpose of this paper is threefold. First, we review the thin plume approximation, the resulting additive signal model, and the key differences between reflective and emissive radiance signal models. Second, based on the additive signal model we derive two families of detection algorithms using the generalized likelihood ratio test. The first family models the background using, whereas the second family models the background using a linear subspace. Finally, we present a taxonomy of the available algorithms and show that some other ad-hoc approaches, like orthogonal background suppression, are simplified special cases of optimally derived detectors.