Simulation of sub-pixel thermal target detection

Spectral unmixing is commonly used in the solar-reflective spectral regime to find materials down to abundances (`pixel fill factors´ (PFFs) or `target fractions´) of (typically) ~10%. However, little is known about whether such spectral unmixing applies in the long-wavelength-infrared (LWIR) spectral regime (designated herein as the “thermal” region). The first part of this paper discusses a numerical method to un-mix thermal pixels for target fraction, temperature and emissivity (ε). In the second part, a simulation is performed to detect particular material spectra at the sub-pixel level. The data is assumed to have been reduced to a temperature map and an emissivity data cube (a three-dimensional construct consisting of emissivity versus pixel position in the image plane corresponding to a given spectral interval; referred to herein as a `cube´ consisting of ε, x, y). Thus, a hyperspectral emissivity cube is generated and sub-pixel targets are added. The scene is then blurred. Various pre-processing algorithms are then evaluated with respect to the detectability of sub-pixel targets. The result of sharpening a hyperspectral cube increases the number of correctly identified sub-pixel targets compared to attempts at target detection with no pre-processing. In particular, the simple sharpened masking filter generates excellent results. We believe that many sub-pixel target detection algorithms could potentially benefit from sharpening of the spectral data cube.