Pigment Unmixing of Hyperspectral Images of Paintings Using Deep Neural Networks. Rohani, N., Pouyet, E., Walton, M., Cossairt, O., & Katsaggelos, A. K. In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), volume 2019-May, pages 3217–3221, may, 2019. IEEE. ![Pigment Unmixing of Hyperspectral Images of Paintings Using Deep Neural Networks [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@inproceedings{Neda2019,
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.},
author = {Rohani, Neda and Pouyet, Emeline and Walton, Marc and Cossairt, Oliver and Katsaggelos, Aggelos K.},
booktitle = {ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
doi = {10.1109/ICASSP.2019.8682838},
isbn = {978-1-4799-8131-1},
issn = {15206149},
keywords = {Hyperspectral imaging,deep neural network,fusion,nonlinear unmixing,pigment identification},
month = {may},
pages = {3217--3221},
publisher = {IEEE},
title = {{Pigment Unmixing of Hyperspectral Images of Paintings Using Deep Neural Networks}},
url = {https://ieeexplore.ieee.org/document/8682838/},
volume = {2019-May},
year = {2019}
}
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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. 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