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Pure and Applied Chemistry

The Scientific Journal of IUPAC

Ed. by Burrows, Hugh / Stohner, Jürgen

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IMPACT FACTOR 2017: 5.294

CiteScore 2017: 3.42

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1365-3075
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Volume 90, Issue 3

Issues

Innovative data reduction and visualization strategy for hyperspectral imaging datasets using t-SNE approach

Emeline Pouyet
  • Center for Scientific Studies in the Art, Northwestern University, 2145 Sheridan Rd., Evanston, IL 60208, USA
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Neda Rohani
  • Electrical Engineering and Computer Science, Northwestern University, 2145 Sheridan Rd., Evanston, IL 60208, USA
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Aggelos K. Katsaggelos
  • Electrical Engineering and Computer Science, Northwestern University, 2145 Sheridan Rd., Evanston, IL 60208, USA
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Oliver Cossairt
  • Electrical Engineering and Computer Science, Northwestern University, 2145 Sheridan Rd., Evanston, IL 60208, USA
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Marc Walton
  • Corresponding author
  • Center for Scientific Studies in the Art, Northwestern University, 2145 Sheridan Rd., Evanston, IL 60208, USA
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2018-01-05 | DOI: https://doi.org/10.1515/pac-2017-0907

Abstract

Visible hyperspectral imaging (HSI) is a fast and non-invasive imaging method that has been adapted by the field of conservation science to study painted surfaces. By collecting reflectance spectra from a 2D surface, the resulting 3D hyperspectral data cube contains millions of recorded spectra. While processing such large amounts of spectra poses an analytical and computational challenge, it also opens new opportunities to apply powerful methods of multivariate analysis for data evaluation. With the intent of expanding current data treatment of hyperspectral datasets, an innovative approach for data reduction and visualization is presented in this article. It uses a statistical embedding method known as t-distributed stochastic neighbor embedding (t-SNE) to provide a non-linear representation of spectral features in a lower 2D space. The efficiency of the proposed method for painted surfaces from cultural heritage is established through the study of laboratory prepared paint mock-ups, and medieval French illuminated manuscript.

Keywords: ChemCultHerit; data reduction and visualization; illuminated manuscript; multivariate analysis; t-distributed stochastic neighbor embedding; visible hyperspectral imaging

Article note:

A special issue containing invited papers on Chemistry and Cultural Heritage (M.J. Melo, A. Nevin and P. Baglioni, editors).

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About the article

Published Online: 2018-01-05

Published in Print: 2018-02-23


Citation Information: Pure and Applied Chemistry, Volume 90, Issue 3, Pages 493–506, ISSN (Online) 1365-3075, ISSN (Print) 0033-4545, DOI: https://doi.org/10.1515/pac-2017-0907.

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Neda Rohani, Emeline Pouyet, Marc Walton, Oliver Cossairt, and Aggelos K. Katsaggelos
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[3]
Neda Rohani, Emeline Pouyet, Marc Walton, Oliver Cossairt, and Aggelos K. Katsaggelos
Angewandte Chemie International Edition, 2018

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