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Hyperspectral image analysis for CARS, SRS, and Raman data
Author(s) -
Masia Francesco,
Karuna Arnica,
Borri Paola,
Langbein Wolfgang
Publication year - 2015
Publication title -
journal of raman spectroscopy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.748
H-Index - 110
eISSN - 1097-4555
pISSN - 0377-0486
DOI - 10.1002/jrs.4729
Subject(s) - hyperspectral imaging , raman spectroscopy , principal component analysis , raman scattering , computer science , artificial intelligence , pattern recognition (psychology) , filter (signal processing) , outlier , singular value decomposition , weighting , optics , remote sensing , physics , computer vision , geology , acoustics
In this work, we have significantly enhanced the capabilities of the hyperspectral image analysis (HIA) first developed by Masia et al . [1][F. Masia, 2013] The HIA introduced a method to factorize the hyperspectral data into the product of component concentrations and spectra for quantitative analysis of the chemical composition of the sample. The enhancements shown here comprise (1) a spatial weighting to reduce the spatial variation of the spectral error, which improves the retrieval of the chemical components with significant local but small global concentrations; (2) a new selection criterion for the spectra used when applying sparse sampling[2][F. Masia, 2014] to speed up sequential hyperspectral imaging; and (3) a filter for outliers in the data using singular value decomposition, suited e.g. to suppress motion artifacts. We demonstrate the enhancements on coherent anti‐Stokes Raman scattering, stimulated Raman scattering, and spontaneous Raman data. We provide the HIA software as executable for public use. © 2015 The Authors. Journal of Raman Spectroscopy published by John Wiley & Sons, Ltd.