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Proceedings Paper

Hierarchical multi-scale approach to validation and uncertainty quantification of hyper-spectral image modeling
Author(s): Dave W. Engel; Thomas A. Reichardt; Thomas J. Kulp; David L. Graff; Sandra E. Thompson
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Paper Abstract

Validating predictive models and quantifying uncertainties inherent in the modeling process is a critical component of the HARD Solids Venture program [1]. Our current research focuses on validating physics-based models predicting the optical properties of solid materials for arbitrary surface morphologies and characterizing the uncertainties in these models. We employ a systematic and hierarchical approach by designing physical experiments and comparing the experimental results with the outputs of computational predictive models. We illustrate this approach through an example comparing a micro-scale forward model to an idealized solid-material system and then propagating the results through a system model to the sensor level. Our efforts should enhance detection reliability of the hyper-spectral imaging technique and the confidence in model utilization and model outputs by users and stakeholders.

Paper Details

Date Published: 17 May 2016
PDF: 9 pages
Proc. SPIE 9840, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII, 98400N (17 May 2016); doi: 10.1117/12.2224262
Show Author Affiliations
Dave W. Engel, Pacific Northwest National Lab. (United States)
Thomas A. Reichardt, Sandia National Labs. (United States)
Thomas J. Kulp, Sandia National Labs. (United States)
David L. Graff, Los Alamos National Lab. (United States)
Sandra E. Thompson, Pacific Northwest National Lab. (United States)

Published in SPIE Proceedings Vol. 9840:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII
Miguel Velez-Reyes; David W. Messinger, Editor(s)

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