Subspace-Based Target Detection in LWIR Hyperspectral Imaging

This letter presents a new method to detect materials with known spectral emissivity in data acquired by longwave infrared hyperspectral sensors. The proposed approach differs from existing methods because it takes into account the uncertainty of the downwelling radiance. Such uncertainty is addressed assuming that the downwelling radiance spans a low-rank subspace whose basis matrix is learned, regardless of the analyzed image, from MODTRAN simulated spectra. The analysis, carried out over data simulated by considering different atmospheric conditions, surface temperatures, and emissivity spectra, shows the effectiveness of the proposed algorithm.