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A sparse Gaussian sigmoid basis function approximation of hyperspectral data for detection of solids
Author(s) -
Lanker Cory,
Smith Milton O.
Publication year - 2019
Publication title -
statistical analysis and data mining: the asa data science journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.381
H-Index - 33
eISSN - 1932-1872
pISSN - 1932-1864
DOI - 10.1002/sam.11433
Subject(s) - hyperspectral imaging , sigmoid function , gaussian , gaussian process , pattern recognition (psychology) , artificial intelligence , gaussian function , computer science , mathematics , remote sensing , algorithm , physics , geology , artificial neural network , quantum mechanics
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.