| Issue |
EPJ Web Conf.
Volume 335, 2025
EOS Annual Meeting (EOSAM 2025)
|
|
|---|---|---|
| Article Number | 03024 | |
| Number of page(s) | 2 | |
| Section | Topical Meeting - Applications of Optics and Photonics | |
| DOI | https://doi.org/10.1051/epjconf/202533503024 | |
| Published online | 22 September 2025 | |
https://doi.org/10.1051/epjconf/202533503024
Integrating innovative Spatial and Spectral Data Fusion strategies in Hyperspectral Imaging for Cultural Heritage
1 Department of Physics, Politecnico di Milano, Piazza Leonardo da Vinci 32, Milano, Italy
2 IFN-CNR, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
3 Delft University of Technology, Department of Material Science and Engineering, Mekelweg 2, 2628 CD Delft
* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.
Published online: 22 September 2025
Abstract
The study of cultural heritage (CH) objects benefits greatly from non-invasive techniques like hyperspectral imaging (HSI), which enables material identification and spatial mapping. Due to the heterogeneous composition of CH artifacts, combining complementary techniques is essential for comprehensive analysis. However, handling such high-dimensional datasets remains a challenge. We present a computational protocol that combines spatial and spectral dimensionality reduction to enable early-stage fusion and efficient analysis of fused data, through multivariate methods, with a focus on Uniform Manifold Approximation and Projection (UMAP). We introduce an open-source plugin for Napari viewer, which allows for UMAP-based exploration of fused multimodal datasets. Our approach is demonstrated in case studies involving reflectance and photoluminescence data fusion, showcasing its effectiveness in detecting degradation phenomena and revealing material complexity in both plastic artifacts and historical paintings.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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