17 May 2016 Identification of solid materials using HSI spectral oscillators
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Abstract
Our research aims to characterize solid materials through LWIR reflectance spectra in order to improve com-positional exploitation in a hyperspectral imaging (HSI) sensor data cube. Specifically, we aim to reduce false alarm rates when identifying target materials without compromising sensitivity. We employ dispersive analysis to extract the material oscillator resonances from reflectance spectra with a stepwise fitting algorithm to estimate the Lorentz or Gaussian oscillators effectively present in the HSI spectral measurements. The proposed algorithm operates through nonlinear least squares minimization through a grid search over potential oscillator resonance frequencies and widths. Experimental validation of the algorithm is performed with published values of crys-talline and amorphous materials. Our aim is to use the derived oscillator parameters to characterize the materials that are present in an HSI pixel. We demonstrate that there are material-specific properties of oscillators that show subtle variability when considering changes in morphology or measurement conditions. The experimentally verified results include variability in material particle size, measurement angle, and atmospheric conditions for six mineral measurements. Once a target material’s oscillators are characterized, we apply statistical learning techniques to form a classifier based on the estimated spectral oscillators of the HSI pixels. We show that this approach has good initial identification results that are extendible across localized experimental conditions.
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Cory L. Lanker, Cory L. Lanker, Milton O. Smith, Milton O. Smith, } "Identification of solid materials using HSI spectral oscillators", Proc. SPIE 9840, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII, 98400U (17 May 2016); doi: 10.1117/12.2224337; https://doi.org/10.1117/12.2224337
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