Pigment Unmixing of Hyperspectral Images of Paintings Using Deep Neural Networks

Neda Rohani; Emeline Pouyet; Marc Walton; Oliver Cossairt; Aggelos K. Katsaggelos

ICASSP 2019accepted0 citations

Pigment Unmixing of Hyperspectral Images of Paintings Using Deep Neural Networks

Neda Rohani, Emeline Pouyet, Marc Walton, Oliver Cossairt, Aggelos K. Katsaggelos

Abstract

In this paper, the problem of automatic nonlinear unmixing of hyperspectral reflectance data using works of art as test cases is described. We use a deep neural network to decompose a given spectrum quantitatively to the abundance values of pure pigments. We show that adding another step to identify the constituent pigments of a given spectrum leads to more accurate unmixing results. Towards this, we use another deep neural network to identify pigments first and integrate this information to different layers of the network used for pigment unmixing. As a test set, the hyperspectral images of a set of mock-up paintings consisting of a broad palette of pigment mixtures, and pure pigment exemplars, were measured. The results of the algorithm on the mock-up test set are reported and analyzed.

BibTeX

@inproceedings{icassp2019_pigmentunmixingo,
  title = {Pigment Unmixing of Hyperspectral Images of Paintings Using Deep Neural Networks},
  author = {Neda Rohani and Emeline Pouyet and Marc Walton and Oliver Cossairt and Aggelos K. Katsaggelos},
  booktitle = {ICASSP 2019},
  year = {2019}
}