Prediction of observed image spectra using synthetic image generation models

John R. Schott; Shiao Didi Kuo; Scott D. Brown; Rolando V. Raqueno

doi:10.1117/12.283821

31 October 1997 Prediction of observed image spectra using synthetic image generation models

John R. Schott, Shiao Didi Kuo, Scott D. Brown, Rolando V. Raqueno

Proceedings Volume 3118, Imaging Spectrometry III; (1997) https://doi.org/10.1117/12.283821
Event: Optical Science, Engineering and Instrumentation '97, 1997, San Diego, CA, United States

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.

Citation Download Citation

John R. Schott, Shiao Didi Kuo, Scott D. Brown, and Rolando V. Raqueno "Prediction of observed image spectra using synthetic image generation models", Proc. SPIE 3118, Imaging Spectrometry III, (31 October 1997); https://doi.org/10.1117/12.283821

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