17 May 2016 Hierarchical multi-scale approach to validation and uncertainty quantification of hyper-spectral image modeling
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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.
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Dave W. Engel, Thomas A. Reichardt, Thomas J. Kulp, David L. Graff, Sandra E. Thompson, "Hierarchical multi-scale approach to validation and uncertainty quantification of hyper-spectral image modeling", Proc. SPIE 9840, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII, 98400N (17 May 2016); doi: 10.1117/12.2224262; https://doi.org/10.1117/12.2224262
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