Abstract
Spectral imaging modalities, including reflectance and X-ray fluorescence, play an important role in conservation science. In reflectance hyperspectral imaging, the data are classified into areas having similar spectra and turned into labeled pigment maps using spectral features and fusing with other information. Direct classification and labeling remain challenging because many paints are intimate pigment mixtures that require a non-linear unmixing model for a robust solution. Neural networks have been successful in modeling non-linear mixtures in remote sensing with large training datasets. For paintings, however, existing spectral databases are small and do not encompass the diversity encountered. Given that painting practices are relatively consistent within schools of artistic practices, we tested the suitability of using reflectance spectra from a subgroup of well-characterized paintings to build a large database to train a one-dimensional (spectral) convolutional neural network. The labeled pigment maps produced were found to be robust within similar styles of paintings.
Highlights
The development of spectral macroscale mapping modalities has provided conservators, scientists and art historians with the ability to examine the distribution of pigments across works of art with unprecedented detail
In this paper we explore the suitability of building a training dataset from regions of well-characterized paintings for an end-to-end supervised one-dimensional convolutional neural network (1D-convolutional neural networks (CNN))
Data and experimental Setup The workflow to create a neural network with an appropriate training dataset and to produce labeled pigment maps of paintings is outlined in Fig. 1 and consists of four steps: 1. collect a sufficiently large spectral training dataset in which the pigments for each spectra are labeled; 2. create a neural network to predict pigments present in the input reflectance imaging spectroscopy (RIS) spectra; 3. validate the accuracy of the network with a hold-out sample (10% of the training data); and 4. test the network prediction of pigments present on two well-characterized paintings that were not part of the training dataset
Summary
The development of spectral macroscale mapping modalities has provided conservators, scientists and art historians with the ability to examine the distribution of pigments across works of art with unprecedented detail. This allows for a more robust understanding of an artist’s creative process, and helps answer certain art historical research questions It informs conservators and museums on how to better preserve these works based on their materiality, propensity for degradation, or Currently the most widely used macroscale imaging modalities for art examination are imaging X-ray fluorescence (XRF) spectroscopy [1], and reflectance hyperspectral imaging (typically 400 to ∼ 1000 nanometer (nm) and sometimes out to 2500 nm) [2], otherwise known as reflectance imaging spectroscopy (RIS). Analysis of RIS data cubes of paintings is more challenging, and has typically utilized workflows and algorithms developed for remote sensing of minerals and vegetation
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