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. 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.
- Research Organization:
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA), Office of Defense Nuclear Nonproliferation
- Grant/Contract Number:
- AC52-07NA27344
- OSTI ID:
- 1575862
- Alternate ID(s):
- OSTI ID: 1545646
- Report Number(s):
- LLNL-JRNL-753011; 939031
- Journal Information:
- Statistical Analysis and Data Mining, Vol. 12, Issue 6; ISSN 1932-1864
- Publisher:
- WileyCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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