Detection and Identification of Effluent Gases Using Invariant Hyperspectral Algorithms

The ability to detect and identify effluent gases is a problem that has been pursued with limited success. An algorithm to do this would not only aid in the regulation of pollutants but also in treaty enforcement. Considering these applications, finding a way to remotely investigate a gaseous emission is highly desirable. This research utilizes hyperspectral imagery in the infrared region of the electromagnetic spectrum to evaluate invariant methods of detecting and identifying gases within a scene. The image is evaluated on a pixel-by-pixel basis and is also studied at the subpixel level. A library of target gas spectra is generated using a simple radiance model. This results in a more robust representation of the gas spectra which are representative of real-world observations. This library is the subspace utilized by the detection and identification algorithm. An evaluation was carried out to determine the subset of basis vectors that best span the subspace. Two basis vector selection methods are used to determine the subset of basis vectors; Singular Value Decomposition (SVD) and the Maximum Distance Method (MaxD). The Generalized Likelihood Ratio Test (GLRT) was used to determine whether the pixel is more like the target or the background. The target can be either a single species or a combination of gases, however, this study only looks for iv