Detecting liquid contamination on surfaces using hyperspectral imaging data

Over the past two years we have developed a new approach for detecting and identifying the presence of liquid chemical contamination on surfaces using hyperspectral imaging data. This work requires an algorithm for unmixing the data to separate the liquid contamination component of the data from all other possible spectral effects, such as the illumination and reflectance spectra of the pure background. The contamination components from S and P polarized reflectance data are then used to estimate the complex refractive index. We retain the index estimates within spectral windows chosen for each of a set of candidate contaminant materials based on their optical extinction. Spectral estimates within those windows are characteristic of the liquid material, and can be passed on to an algorithm for chemical detection and identification. The resulting algorithm is insensitive to the composition of the surface material, and requires no prior measurements of the uncontaminated surface. In a series of field tests, data from the Telops Hyper-Cam sensor were used to develop and validate our approach. We discuss our hyperspectral unmixing and index estimation approaches, and show results from tests conducted at the Telops facility in Quebec under a contract with the U.S. Army Edgewood Chemical Biological Center.