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Hyperspectral image analysis for CARS, SRS, and Raman data

Masia, Francesco, Karuna, Arnica, Borri, Paola and Langbein, Wolfgang Werner 2015. Hyperspectral image analysis for CARS, SRS, and Raman data. Journal of Raman Spectroscopy 46 (8) , pp. 727-734. 10.1002/jrs.4729

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Abstract

In this work, we have significantly enhanced the capabilities of the hyperspectral image analysis (HIA) first developed by Masia et al. [1] 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] 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.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Physics and Astronomy
Subjects: Q Science > QC Physics
Uncontrolled Keywords: coherent Raman micro-spectroscopy; hyperspectral image analysis; sparse sampling
Publisher: Wiley-Blackwell
ISSN: 0377-0486
Funders: EPSRC, BBSRC
Date of First Compliant Deposit: 30 March 2016
Date of Acceptance: 5 May 2015
Last Modified: 20 May 2019 21:02
URI: http://orca.cf.ac.uk/id/eprint/74543

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