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

Emeline Pouyet, Neda Rohani, Aggelos K Katsaggelos, Oliver Strides Cossairt, Marc Sebastian Walton*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

25 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)493-506
Number of pages14
JournalPure and Applied Chemistry
Issue number3
StatePublished - Feb 23 2018


  • ChemCultHerit
  • Data reduction and visualization
  • Illuminated manuscript
  • Multivariate analysis
  • T-distributed stochastic neighbor embedding
  • Visible hyperspectral imaging

ASJC Scopus subject areas

  • Chemistry(all)
  • Chemical Engineering(all)


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