Unmixing and pigment identification using visible and short-wavelength infrared: Reflectance vs logarithm reflectance hyperspaces

Valero, Eva M.; Martínez-Domingo, Miguel A.; López-Baldomero, Ana B.; López-Montes, Ana; Abad-Muñoz, David; Vílchez-Quero, José L.

doi:10.1016/j.culher.2023.10.016

Cited by 1 publication

(1 citation statement)

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“…One possible solution could be to use a simplified Kubelka-Munk (KM) model for opaque and infinitely thick samples, which approximates the reflectance of mixed pigments at the expense of intensive computational operations [32]. Deep learning (DL), particularly neural networks (NNs), has recently been tested for prior selection of pigments in hyperspectral data cubes, showing promising results [33]. However, to create accurate material maps using NNs, it is necessary to generate large training datasets of labelled reflectance spectra [34].…”

Section: Discussionmentioning

confidence: 99%

Spectral Mapping Techniques for the Stratigraphic and Compositional Characterisation of a 16th-Century Painting

Dal Fovo,

Morello,

Mazzinghi

et al. 2024

Heritage

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Identifying a painting’s pigment palette is crucial for comprehending the author’s technique, as well as for evaluating the degradation of the materials. This paper investigates the stratigraphy and pigments distribution of a 16th-century painting from the Uffizi Galleries collection. Firstly, we obtained compositional information through the cross-sectional analysis of samples using scanning electron microscopy. Secondly, we performed elemental mapping using macro-X-ray fluorescence followed by reflectance imaging spectroscopy. The painting image cube was analysed using the spectral correlation mapping (SCM) classification algorithm to accurately identify the distribution and composition of the pigment mixtures.

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Section: Discussionmentioning

confidence: 99%