TY - JOUR
T1 - Multivariate analysis on fused hyperspectral datasets within Cultural Heritage field
AU - Di Benedetto, Alessia
AU - de Almieda Nieto, Luìs Manuel
AU - Candeo, Alessia
AU - Valentini, Gianluca
AU - Comelli, Daniela
AU - Alfeld, Matthias
PY - 2024
Y1 - 2024
N2 - This work introduces a novel method to multivariate analysis applied to fused hyperspectral datasets in the field of Cultural Heritage (CH). Hyperspectral Imaging is a well-established approach for the non-invasive examination of artworks, offering insights into their composition and conservation status. In CH field, a combination of hyperspectral techniques is usually employed to reach a comprehensive understanding of the artwork. To deal with hyperspectral data, multivariate statistical methods are essential due to the complexity of the data. The process involves factorizing the data matrix to highlight components and reduce dimensionality, with techniques such as Non-negative Matrix Factorization (NMF) gaining prominence. To maximize the synergies between multimodal datasets, the fusion of hyperspectral datasets can be coupled with multivariate analysis, with potential applications in CH. In this work, I will show examples of this approach with different combinations of datasets, including reflectance and transmittance spectral imaging, Fluorescence Lifetime Imaging and Time-Gated Hyperspectral Imaging, and Raman and fluorescence spectroscopy micro-mapping.
AB - This work introduces a novel method to multivariate analysis applied to fused hyperspectral datasets in the field of Cultural Heritage (CH). Hyperspectral Imaging is a well-established approach for the non-invasive examination of artworks, offering insights into their composition and conservation status. In CH field, a combination of hyperspectral techniques is usually employed to reach a comprehensive understanding of the artwork. To deal with hyperspectral data, multivariate statistical methods are essential due to the complexity of the data. The process involves factorizing the data matrix to highlight components and reduce dimensionality, with techniques such as Non-negative Matrix Factorization (NMF) gaining prominence. To maximize the synergies between multimodal datasets, the fusion of hyperspectral datasets can be coupled with multivariate analysis, with potential applications in CH. In this work, I will show examples of this approach with different combinations of datasets, including reflectance and transmittance spectral imaging, Fluorescence Lifetime Imaging and Time-Gated Hyperspectral Imaging, and Raman and fluorescence spectroscopy micro-mapping.
UR - http://www.scopus.com/inward/record.url?scp=85212515946&partnerID=8YFLogxK
U2 - 10.1051/epjconf/202430914007
DO - 10.1051/epjconf/202430914007
M3 - Conference article
AN - SCOPUS:85212515946
SN - 2101-6275
VL - 309
JO - EPJ Web of Conferences
JF - EPJ Web of Conferences
M1 - 14007
T2 - 2024 EOS Annual Meeting, EOSAM 2024
Y2 - 9 September 2024 through 13 September 2024
ER -