Hierarchical multi-scale approach to validation and uncertainty quantification of hyper-spectral image modeling

Dave W. Engel; Thomas A. Reichardt; Thomas J. Kulp; David L. Graff; Sandra E. Thompson

doi:10.1117/12.2224262

17 May 2016 Hierarchical multi-scale approach to validation and uncertainty quantification of hyper-spectral image modeling

Dave W. Engel, Thomas A. Reichardt, Thomas J. Kulp, David L. Graff, Sandra E. Thompson

Author Affiliations +

Proceedings Volume 9840, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII; 98400N (2016) https://doi.org/10.1117/12.2224262
Event: SPIE Defense + Security, 2016, Baltimore, MD, United States

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.

Citation Download Citation

Dave W. Engel, Thomas A. Reichardt, Thomas J. Kulp, David L. Graff, and 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); https://doi.org/10.1117/12.2224262

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