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Neural networks for hyperspectral imaging of historical paintings : a practical review

Lingxi Liu (UGent) , Tsveta Miteva, Giovanni Delnevo, Silvia Mirri, Philippe Walter, Laurence de Viguerie and Emeline Pouyet
(2023) SENSORS. 23(5).
Author
Organization
Abstract
Hyperspectral imaging (HSI) has become widely used in cultural heritage (CH). This very efficient method for artwork analysis is connected with the generation of large amounts of spectral data. The effective processing of such heavy spectral datasets remains an active research area. Along with the firmly established statistical and multivariate analysis methods, neural networks (NNs) represent a promising alternative in the field of CH. Over the last five years, the application of NNs for pigment identification and classification based on HSI datasets has drastically expanded due to the flexibility of the types of data they can process, and their superior ability to extract structures contained in the raw spectral data. This review provides an exhaustive analysis of the literature related to NNs applied for HSI data in the CH field. We outline the existing data processing workflows and propose a comprehensive comparison of the applications and limitations of the various input dataset preparation methods and NN architectures. By leveraging NN strategies in CH, the paper contributes to a wider and more systematic application of this novel data analysis method.
Keywords
hyperspectral imaging, neural network, deep learning, cultural heritage, SPECTRAL REFLECTANCE CURVES, SPECTROSCOPY, IMAGES, CLASSIFICATION, SEGMENTATION, PIGMENTS, FUSION

Citation

Please use this url to cite or link to this publication:

MLA
Liu, Lingxi, et al. “Neural Networks for Hyperspectral Imaging of Historical Paintings : A Practical Review.” SENSORS, vol. 23, no. 5, 2023, doi:10.3390/s23052419.
APA
Liu, L., Miteva, T., Delnevo, G., Mirri, S., Walter, P., de Viguerie, L., & Pouyet, E. (2023). Neural networks for hyperspectral imaging of historical paintings : a practical review. SENSORS, 23(5). https://doi.org/10.3390/s23052419
Chicago author-date
Liu, Lingxi, Tsveta Miteva, Giovanni Delnevo, Silvia Mirri, Philippe Walter, Laurence de Viguerie, and Emeline Pouyet. 2023. “Neural Networks for Hyperspectral Imaging of Historical Paintings : A Practical Review.” SENSORS 23 (5). https://doi.org/10.3390/s23052419.
Chicago author-date (all authors)
Liu, Lingxi, Tsveta Miteva, Giovanni Delnevo, Silvia Mirri, Philippe Walter, Laurence de Viguerie, and Emeline Pouyet. 2023. “Neural Networks for Hyperspectral Imaging of Historical Paintings : A Practical Review.” SENSORS 23 (5). doi:10.3390/s23052419.
Vancouver
1.
Liu L, Miteva T, Delnevo G, Mirri S, Walter P, de Viguerie L, et al. Neural networks for hyperspectral imaging of historical paintings : a practical review. SENSORS. 2023;23(5).
IEEE
[1]
L. Liu et al., “Neural networks for hyperspectral imaging of historical paintings : a practical review,” SENSORS, vol. 23, no. 5, 2023.
@article{01JXMJQVQQYY9GBCFJ3VS8TD1G,
  abstract     = {{Hyperspectral imaging (HSI) has become widely used in cultural heritage (CH). This very efficient method for artwork analysis is connected with the generation of large amounts of spectral data. The effective processing of such heavy spectral datasets remains an active research area. Along with the firmly established statistical and multivariate analysis methods, neural networks (NNs) represent a promising alternative in the field of CH. Over the last five years, the application of NNs for pigment identification and classification based on HSI datasets has drastically expanded due to the flexibility of the types of data they can process, and their superior ability to extract structures contained in the raw spectral data. This review provides an exhaustive analysis of the literature related to NNs applied for HSI data in the CH field. We outline the existing data processing workflows and propose a comprehensive comparison of the applications and limitations of the various input dataset preparation methods and NN architectures. By leveraging NN strategies in CH, the paper contributes to a wider and more systematic application of this novel data analysis method.}},
  articleno    = {{2419}},
  author       = {{Liu, Lingxi and Miteva, Tsveta and Delnevo, Giovanni and Mirri, Silvia and Walter, Philippe and de Viguerie, Laurence and Pouyet, Emeline}},
  issn         = {{1424-8220}},
  journal      = {{SENSORS}},
  keywords     = {{hyperspectral imaging,neural network,deep learning,cultural heritage,SPECTRAL REFLECTANCE CURVES,SPECTROSCOPY,IMAGES,CLASSIFICATION,SEGMENTATION,PIGMENTS,FUSION}},
  language     = {{eng}},
  number       = {{5}},
  pages        = {{25}},
  title        = {{Neural networks for hyperspectral imaging of historical paintings : a practical review}},
  url          = {{http://doi.org/10.3390/s23052419}},
  volume       = {{23}},
  year         = {{2023}},
}
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