Assessment of Residual Nonuniformity on Hyperspectral Target Detection Performance
AFRL/RYMT Wright-Patterson AFB United States
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Hyperspectral imaging sensors suffer from pixel-to-pixel response non-uniformity that manifests as fixed pattern noise FPN in collected data. FPN is typically removed by application of flat-field calibration procedures and non-uniformity correction algorithms. Despite application of these techniques, some amount of residual fixed pattern noise RFPN may persist in the data, negatively impacting target detection performance. In this work we examine the conditions under which RFPN can impact detection performance using data collected in the SWIR across a range of target materials. We designed and conducted a unique tower-based experiment where we carefully selected target materials that have varying degrees of separability from natural grass backgrounds. Furthermore, we designed specially-shaped targets for this experiment that introduce controlled levels of mixing between the target and background materials to support generation of high fidelity receiver operating characteristic ROC curves in our detection analysis. We perform several studies using this collected data. First, we assess the detection performance after a conventional nonuniformity correction. We then apply several scene-based non-uniformity correction SB- NUC algorithms from the literature and assess their abilities to improve target detection performance as a function of material separability. Then, we introduced controlled RFPN and study its adverse affects on target detection performance as well as the SBNUC techniques ability to remove it. We demonstrate how residual fixed pattern noise affects the detectability of each target class differently based upon its inherent separability from the background. A moderate inherently separable material from the background is affected the most by the inclusion of SBNUC algorithms.
- Atomic and Molecular Physics and Spectroscopy