A sparse Gaussian sigmoid basis function approximation of hyperspectral data for detection of solids. (30th July 2019)

• Record Type: Journal Article
• Authors: Lanker, Cory; Smith, Milton O.
• Other Names: Lawrence Earl guestEditor.
• Is Part Of: Statistical analysis and data mining. Volume 12:Number 6(2019)
• Page Start: 489
• Page End: 495
• Publication Date: 2019-07-30
• Subjects: basis function decomposition -- classification -- curve fitting -- feature extraction -- LASSO regression -- minerals -- spectral characterization -- target detection; Data mining -- Statistical methods -- Periodicals; 006.312
• DOI: 10.1002/sam.11433
• Languages: English
• ISSNs: 1932-1864

Abstract

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.