Background Suppression and Feature-Based Spectroscopy Methods for Subpixel Material Identification

Feature-based imaging spectroscopy methods are effective for identifying materials that exhibit specific well-defined spectral absorption features. As long as a pixel contains a sufficient amount of material so that the absorption retains its predominant shape, a feature-based method can work well. However, there are occasions when a background material can mix with a material of interest, and significantly distort and maybe even remove the absorption. In such cases, the material identification capabilities of these methods are likely to be degraded. This effort proposes an approach to accommodate these conditions. The parameter values to determine fit of an absorption feature are selected to be more tolerant of distortions and the signal contributions of any detected sub-pixel backgrounds are removed by making use of a physically-constrained linear mixing model. This mixing model is used to remove any detected background spectra from the image spectra within the bounding locations of the spectral features. However, an expected consequence of loosening the parameter values and performing sub-pixel subtraction is an increase in false alarms. A statistically-based spectral matched filter is proposed as to reduce these false alarms. We test the individual and combined approaches for identifying full-pixel and sub-pixel Tyvek panels in an experiment using a HyMAP hyperspectral scene with ground truth collected over Waimanalo Bay, Oahu, Hawaii.