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Proceedings Paper

Prediction of observed image spectra using synthetic image generation models
Author(s): John R. Schott; Shiao Didi Kuo; Scott D. Brown; Rolando V. Raqueno
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Paper Abstract

Most spectrometric image analysis algorithms either require or can be augmented by estimates of target/background spectral signatures. The prediction of these spectra is complicated by the complex interplay of the target's spectra, background spectra, energy matter interaction effects, atmospheric effects and sensor response, an noise effects. Signatures can be further confused in the thermal IR by the temperature and temperature variation of targets and backgrounds. Finally, in nearly all cases, the image signature is the result of spatial mixing of target and background spectra. This paper addresses the potential for using synthetic image generation modeling tools to help in the prediction and understanding of hyperspectral signature. The DIRSIG model is discussed in terms of how it deals spectrally with target/background interactions, atmospheric propagation and sensor spectral, geometric MTF and noise effects. The DIRSIG model enables the estimation of mixed pixel 'image' spectra as they would be observed by an actual system imaging a complex 3D scene.

Paper Details

Date Published: 31 October 1997
PDF: 13 pages
Proc. SPIE 3118, Imaging Spectrometry III, (31 October 1997); doi: 10.1117/12.283821
Show Author Affiliations
John R. Schott, Rochester Institute of Technology (United States)
Shiao Didi Kuo, Rochester Institute of Technology (United States)
Scott D. Brown, Rochester Institute of Technology (United States)
Rolando V. Raqueno, Rochester Institute of Technology (United States)

Published in SPIE Proceedings Vol. 3118:
Imaging Spectrometry III
Michael R. Descour; Sylvia S. Shen, Editor(s)

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