HIL range performance of notional hyperspectral imaging sensors
2016
In the use of conventional broadband imaging systems, whether reflective or emissive, scene image contrasts are often so
low that target discrimination is difficult or uncertain, and it is contrast that drives human-in-the-loop (HIL) sensor range
performance. This situation can occur even when the spectral shapes of the target and background signatures (radiances)
across the sensor waveband differ significantly from each other. The fundamental components of broadband image
contrast are the spectral integrals of the target and background signatures, and this spectral integration can average away
the spectral differences between scene objects. In many low broadband image contrast situations, hyperspectral imaging
(HSI) can preserve a greater degree of the intrinsic scene spectral contrast for the display, and more display contrast
means greater range performance by a trained observer. This paper documents a study using spectral radiometric
signature modeling and the U.S. Army’s Night Vision Integrated Performance Model (NV-IPM) to show how waveband
selection by a notional HSI sensor using spectral contrast optimization can significantly increase HIL sensor range
performance over conventional broadband sensors.
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