Share Email Print

Proceedings Paper

NEFDS contamination model parameter estimation of powder contaminated surfaces
Author(s): Timothy J. Gibbs; David W. Messinger
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Hyperspectral signatures of powdered contaminated surfaces are challenging to characterize due to intimate mixing between materials. Most radiometric models have difficulties in recreating these signatures due to non-linear interactions between particles with different physical properties. The Nonconventional Exploitation Factors Data System (NEFDS) Contamination Model is capable of recreating longwave hyperspectral signatures at any contamination mixture amount, but only for a limited selection of materials currently in the database. A method has been developed to invert the NEFDS model and perform parameter estimation on emissivity measurements from a variety of powdered materials on substrates. This model was chosen for its potential to accurately determine contamination coverage density as a parameter in the inverted model. Emissivity data were measured using a Designs and Prototypes fourier transform infrared spectrometer model 102 for different levels of contamination. Temperature emissivity separation was performed to convert data from measure radiance to estimated surface emissivity. Emissivity curves were then input into the inverted model and parameters were estimated for each spectral curve. A comparison of measured data with extrapolated model emissivity curves using estimated parameter values assessed performance of the inverted NEFDS contamination model. This paper will present the initial results of the experimental campaign and the estimated surface coverage parameters.

Paper Details

Date Published: 17 May 2016
PDF: 13 pages
Proc. SPIE 9840, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII, 98400L (17 May 2016); doi: 10.1117/12.2222302
Show Author Affiliations
Timothy J. Gibbs, Rochester Institute of Technology (United States)
David W. Messinger, Rochester Institute of Technology (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)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?