Gas plume species identification in airborne LWIR imagery using constrained stepwise regression analyses

Identification of constituent gases in effluent plumes is performed using linear least-squares regression techniques. Airborne thermal hyperspectral imagery is used for this study. Synthetic imagery is employed as the test-case for algorithm development. Synthetic images are generated by the Digital Imaging and Remote Sensing Image Generation (DIRSIG) Model. The use of synthetic data provides a direct measure of the success of the algorithm through the comparison with truth map outputs. In image test-cases, plumes emanating from factory stacks will have been identified using a separate detection algorithm. The gas identification algorithm being developed in this work is performed only on pixels having been determined to contain the plume. Constrained stepwise linear regression is used in this study. Results indicate that the ability of the algorithm to correctly identify plume gases is directly related to the concentration of the gas. Previous concerns that the algorithm is hindered by spectral overlap were eliminated through the use of constraints on the regression.