Nonlinear Unmixing of Hyperspectral Datasets for the Study of Painted Works of Art

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


Nonlinear unmixing of hyperspectral reflectance data is one of the key problems in quantitative imaging of painted works of art. The approach presented is to interrogate a hyperspectral image cube by first decomposing it into a set of reflectance curves representing pure basis pigments and second to estimate the scattering and absorption coefficients of each pigment in a given pixel to produce estimates of the component fractions. This two-step algorithm uses a deep neural network to qualitatively identify the constituent pigments in any unknown spectrum and, based on the pigment(s) present and Kubelka–Munk theory to estimate the pigment concentration on a per-pixel basis. Using hyperspectral data acquired on a set of mock-up paintings and a well-characterized illuminated folio from the 15th century, the performance of the proposed algorithm is demonstrated for pigment recognition and quantitative estimation of concentration.

Original languageEnglish (US)
Pages (from-to)10910-10914
Number of pages5
JournalAngewandte Chemie - International Edition
Issue number34
StatePublished - Aug 20 2018


  • deep neural network classification
  • heritage science
  • nonlinear unmixing Kubelka–Munk theory
  • visible hyperspectral imaging

ASJC Scopus subject areas

  • Catalysis
  • Chemistry(all)


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