Scene-based correction of fixed pattern noise in hyperspectral image data using temporal reordering

Abstract

Hyperspectral image data suffer from pixel-to-pixel response nonuniformity that degrades the imagery in the form of columnated striping noise. This nonuniformity, or fixed pattern noise (FPN), is typically compensated for through flat-field calibration procedures. FPN is a particularly challenging problem because the detector responsivities drift relative to one another in time, requiring that the sensor be periodically recalibrated. Both the rate and severity of the drift depend on a host of factors that result in varying levels of residual calibration error being present within the data at all times. Scene-based nonuniformity correction (SBNUC) algorithms estimate and remove FPN by exploiting content within the scene data and are often necessary to acceptably remove sensor artifacts for subpixel target detection applications. We present results from two SBNUC techniques that reduce residual FPN and improve target signal-to-clutter ratio. We make the observation that temporally reordering the data in conjunction with the use of spatial ratios or differentials results in algorithms that require a low number of temporal data samples to reliably correct for FPN with minimal introduction of image artifacts. Additionally, application of the algorithms within the principal components domain can further improve their correction ability.