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Proceedings Paper

Detection and correction of bad pixels in hyperspectral sensors
Author(s): Hugh H. Kieffer
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Paper Abstract

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.

Paper Details

Date Published: 6 November 1996
PDF: 16 pages
Proc. SPIE 2821, Hyperspectral Remote Sensing and Applications, (6 November 1996); doi: 10.1117/12.257162
Show Author Affiliations
Hugh H. Kieffer, U.S. Geological Survey (United States)

Published in SPIE Proceedings Vol. 2821:
Hyperspectral Remote Sensing and Applications
Sylvia S. Shen, Editor(s)

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