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Title: A sparse Gaussian sigmoid basis function approximation of hyperspectral data for detection of solids

Abstract

We define a new characterization of emissivity and reflectance curves for compositional exploitation of hyperspectral data. Here, our method decomposes each spectrum into a sparse set of Gaussian sigmoid components using penalized regression. Detection is based on the combination of Gaussian sigmoid components unique to a target material. Focusing on the presence of spectral upslopes and downslopes rather than spectral correlations makes detection more robust to both target variation and spectral variability from atmosphere and background encountered during the collection process. We present simulation studies that demonstrate the potential to reduce false positive rates without compromising sensitivity. Characterization of long–wave infrared (LWIR) experimental data validates our method using minerals of different particle sizes, measurement angles, and collection conditions.

Authors:
ORCiD logo [1];  [1]
  1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA), Office of Defense Nuclear Nonproliferation (NA-20)
OSTI Identifier:
1575862
Alternate Identifier(s):
OSTI ID: 1545646
Report Number(s):
LLNL-JRNL-753011
Journal ID: ISSN 1932-1864; 939031
Grant/Contract Number:  
AC52-07NA27344; PL14‐FY14‐112‐PD3WA
Resource Type:
Accepted Manuscript
Journal Name:
Statistical Analysis and Data Mining
Additional Journal Information:
Journal Volume: 12; Journal Issue: 6; Journal ID: ISSN 1932-1864
Publisher:
Wiley
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; basis function decomposition; classification; curve fitting; feature extraction; LASSO regression; minerals; spectral characterization; target detection

Citation Formats

Lanker, Cory L., and Smith, Milton O. A sparse Gaussian sigmoid basis function approximation of hyperspectral data for detection of solids. United States: N. p., 2019. Web. doi:10.1002/sam.11433.
Lanker, Cory L., & Smith, Milton O. A sparse Gaussian sigmoid basis function approximation of hyperspectral data for detection of solids. United States. doi:10.1002/sam.11433.
Lanker, Cory L., and Smith, Milton O. Tue . "A sparse Gaussian sigmoid basis function approximation of hyperspectral data for detection of solids". United States. doi:10.1002/sam.11433.
@article{osti_1575862,
title = {A sparse Gaussian sigmoid basis function approximation of hyperspectral data for detection of solids},
author = {Lanker, Cory L. and Smith, Milton O.},
abstractNote = {We define a new characterization of emissivity and reflectance curves for compositional exploitation of hyperspectral data. Here, our method decomposes each spectrum into a sparse set of Gaussian sigmoid components using penalized regression. Detection is based on the combination of Gaussian sigmoid components unique to a target material. Focusing on the presence of spectral upslopes and downslopes rather than spectral correlations makes detection more robust to both target variation and spectral variability from atmosphere and background encountered during the collection process. We present simulation studies that demonstrate the potential to reduce false positive rates without compromising sensitivity. Characterization of long–wave infrared (LWIR) experimental data validates our method using minerals of different particle sizes, measurement angles, and collection conditions.},
doi = {10.1002/sam.11433},
journal = {Statistical Analysis and Data Mining},
number = 6,
volume = 12,
place = {United States},
year = {2019},
month = {7}
}

Journal Article:
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