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Proceedings Paper

Analyzing spectral sensors with highly overlapping bands
Author(s): Zhipeng Wang; Biliana Paskalova; M. M. Hayat; J. Scott Tyo
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Paper Abstract

Most traditional spectral sensors have spectrally adjacent bands with little overlap. This overlap is usually ignored in image processing because band-to-band correlation due to oversampling of the scene is almost always dominant. A new proposed class of adaptive spectral sensor based on bias-tunable quantum-dot infrared photodetectors (QDIPs) are different in that they have significant band-to-band overlaps. The influence of these overlaps to image processing results cannot be ignored for such sensors. To facilitate the analysis of such sensors, a generalized geometry-based model is provided here for spectral sensors with arbitrary spectral responses. It starts from the mathematical description of the interaction between sensor and the radiation from scene reaching it. In this model, the spectral responses of a sensor are used to define a sensor space. The spectral sensing process is shown to represent a projection of scene spectrum onto sensor space. The projected spectrum, which can be calculated through the output photocurrents and sensor's spectral responses, is the least-square error reconstruction of the scene spectrum. With this data interpretation, we can remove the influence of band overlap to the data. The band overlap also introduce correlation between noise of different bands, This correlation is also analyzed.

Paper Details

Date Published: 4 May 2006
PDF: 11 pages
Proc. SPIE 6233, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII, 62330T (4 May 2006); doi: 10.1117/12.665742
Show Author Affiliations
Zhipeng Wang, Univ. of New Mexico (United States)
Biliana Paskalova, Univ. of New Mexico (United States)
M. M. Hayat, Univ. of New Mexico (United States)
J. Scott Tyo, Univ. of New Mexico (United States)


Published in SPIE Proceedings Vol. 6233:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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