Median spectral-spatial bad pixel identification and replacement for hyperspectral SWIR sensors

摘要

Hyperspectral focal plane arrays typically contain many pixels that are excessively noisy, dead, or exhibit poor signal to-noise performance in comparison to the average pixel. These bad pixels can significantly impair the performance of spectral target-detection algorithms. Even a single missed bad pixel can lead to false alarms. If the bad pixels are sparsely populated across the focal plane, the over-sampling in both spatial and spectral dimensions of the array can be capitalized upon to replace these pixels without significant loss of information. However, bad pixels are frequently localized in clusters, requiring a replacement strategy that rather than providing a good estimate of the missing data will instead minimize artifacts that may negatively affect the performance of spectral detection algorithms. In this paper, we evaluate a robust method to automatically identify bad pixels for short-wavelength infrared (SWIR) hyperspectral sensors. In addition, we introduce a novel procedure for the replacement of these pixels, which we demonstrate provides a better estimate of the original pixel value compared to interpolation methods for bad pixels found as both isolated individuals and in clusters. The advantages of our technique are discussed and demonstrated with data from several different airborne sensor systems.