Hyperspectral sensors may use a 2D array such that one direction across the array is spatial and the other direction is spectral. Any pixels therein having very poor signal-to-noise performance must have their values replaced. Because of the anisotropic nature of information at the array, common image processing techniques should not be used. A bad-pixel replacement algorithm has been developed which uses the information closest in both spectral and spatial sense to obtain a value which has both the spectral and reflectance properties of the adjacent terrain in the image. A simple and fast implementation that `repairs' individual bad pixels or clusters of bad pixels has three steps; the first two steps are done only once: (1) Pixels are flagged as `bad' if their noise level or responsivity fall outside acceptable limits for their spectral channel. (2) For each bad pixel, the minimum-sized surrounding rectangle is determined that has good pixels at all 4 corners and at the 4 edge-points where the row/column of the bad pixel intersect the rectangle boundary (five cases are possible due to bad pixels near an edge or corner of the detector array); the specifications of this rectangle are saved. (3) After a detector data frame has been radiometrically corrected (dark subtraction and gain corrections), the spectral shapes represented by the rectangle edges extending in the dispersion direction are averaged; this shape is then interpolated through the two pixels in the other edges of the rectangle. This algorithm has been implemented for HYDICE.
Hugh H. Kieffer,
"Detection and correction of bad pixels in hyperspectral sensors", Proc. SPIE 2821, Hyperspectral Remote Sensing and Applications, (6 November 1996); doi: 10.1117/12.257162; https://doi.org/10.1117/12.257162