Contaminant mass estimation of powder contaminated surfaces

Timothy J. Gibbs; David W. Messinger

doi:10.1117/12.2262584

5 May 2017 Contaminant mass estimation of powder contaminated surfaces

Timothy J. Gibbs, David W. Messinger

Proceedings Volume 10198, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII; 101980M (2017) https://doi.org/10.1117/12.2262584
Event: SPIE Defense + Security, 2017, Anaheim, CA, United States

Abstract

How can we determine the physical characteristics of a mixture of multiple materials within a single pixel? Intimate mixing occurs when different materials within the region encompassed by a pixel interact with each other prior to reaching the sensor. For powder contaminated surfaces, nonlinear mixing is unavoidable. The Nonconventional Exploitation Factors Data System (NEFDS) Contamination Model can make longwave hyperspectral mixture signatures, but only for a small subset of their spectral library. In addition, the model uses percent coverage as its only physical property input despite it not being informative to the contaminants physical properties. Through a complex parameter inversion, the NEFDS contamination model can be used to derive various physical properties. These physical characteristics were estimated by using empirically measured data of varying contaminant amounts using a Designs and Prototypes Fourier transform infrared spectrometer. Once estimated parameters are found, the mixture spectra was recreated and compared to the measured data. The estimated areal coverage density is used to derive a total deposited mass on the surface based on the area of contaminated surface. This is compared to the known amount deposited that was measured during the experimental campaign. This paper presents some results of those measurements and model estimates.

Citation Download Citation

Timothy J. Gibbs and David W. Messinger "Contaminant mass estimation of powder contaminated surfaces", Proc. SPIE 10198, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII, 101980M (5 May 2017); https://doi.org/10.1117/12.2262584

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