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.
Revised: February 15, 2017 |
Published: September 17, 2016
Citation
Engel D.W., T.A. Reichardt, T.J. Kulp, D. Graff, and S.E. Thompson. 2016.Hierarchical Multi-Scale Approach To Validation and Uncertainty Quantification of Hyper-Spectral Image Modeling. In Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII, April 17, Baltimore, Maryland. Proceedings of the SPIE, edited by M Velez-Reyes and DM Messinger, 9840, Paper No. 98400N. Bellingham, Washington:SPIE.PNNL-SA-117473.doi:10.1117/12.2224262