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Proceedings Paper

Characterization and reduction of stochastic and periodic anomalies in a hyperspectral imaging sensor system
Author(s): Bruce V. Shetler; Hugh H. Kieffer
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Paper Abstract

HYDICE, the HYperspectral Digital Imagery Collection Experiment, is an airborne hyperspectral imaging sensor operating in a pushbroom mode. HYDICE collects data simultaneously in 210 wavelength bands from 0.4 to 2.5 micrometers using a prism as the dispersing element. While the overall quality of HYDICE data is excellent, certain data stream anomalies have been identified, among which are a periodic offset in DN level related to the operation of the system cryocooler and a quasi-random variation in the spectral alignment between the dispersed image and the focal plane. In this paper we report on an investigation into the above two effects and the development of algorithms and software to correct or minimize their impact in a production data processing system. We find the periodic variation to have unexpected time and band-dependent characteristics which argues against the possibility of correction in post- processing, but to be relatively insensitive to signal and consequently of low impact on the operation of the system. We investigate spectral jitter through an algorithm which performs a least squares fit to several atmospheric spectral features to characterize both the time-dependent jitter motion and systematic spectral mis-registration. This method is also implemented to correct the anomalies in the production data stream. A comprehensive set of hyperspectral sensor calibration and correction algorithm is also presented.

Paper Details

Date Published: 6 November 1996
PDF: 18 pages
Proc. SPIE 2821, Hyperspectral Remote Sensing and Applications, (6 November 1996); doi: 10.1117/12.257163
Show Author Affiliations
Bruce V. Shetler, Science Applications International Corp. (United States)
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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