###### Abstract  
Reflectance imaging spectroscopy (RIS) is invaluable in mapping and identifying artists' materials in paintings. The analysis of the RIS image cube first involves classifying the cube into spatial regions, each having a unique reflectance spectrum (endmember). Second, endmember spectra are analyzed for spectral features useful to identify the pigments present to create labeled classes. The analysis process for paintings remains semi-automated because of the complex diffuse reflectance spectra due to the use of intimate pigment mixtures and optically thin paint layers by the artist. As a result, even when a group of related paintings are analyzed, each RIS cube is analyzed individually, which is time consuming. There is a need for new approaches to more efficiently analyze RIS cubes of related paintings to address the growing interest in the study of related paintings within a group of artists or artistic schools. This work builds upon prior investigations of 1-D spectral convolutional neural networks (CNNs) to address this need in two ways. First, an expanded training set was used--ten illuminated manuscripts created by artists stylistically grouped under the notname \"Master of the Cypresses\" (15 th century Seville, Spain). Second, two 1-D CNN models were trained from the RIS cubes: reflectance and the first derivative. The results showed that the first derivative-trained CNN generally performed better than the reflectance-trained CNN in creating accurate labeled material maps for these illuminated manuscripts. reflectance imaging spectroscopy; neural networks; pigment mapping; illuminated manuscripts +
Footnote †: journal: Medical Imaging  
0000-0001-8891-5800]R.R. In particular, reflectance imaging spectroscopy (RIS) has emerged as a powerful imaging modality in conservation science to help identify and map pigments on polychrome works of art [10; 11; 12]. Each pixel in the recorded image contains a diffuse reflectance spectrum representing the localized interaction of light with the painting surface [13]. This results in a 2D spatial image with a third dimension, the diffuse reflectance spectra, in the form of a data-rich 3D image cube [14]. The reflectance spectrum encodes information about electronic transitions and vibrational features, which can aid in the identification of the pigments present. The analytical objective is to find the pigments used by the artist based on representative spectra extracted from the data cube (a set of endmembers) and to create a labeled material map that shows the spatial distribution of these endmember spectra across the image scene, which gives direct insight into the painting techniques employed by the artist [15; 16]. Various approaches can be used to create material maps that demonstrate the variability and distribution of the pigments present in an artwork. Since the image cubes are large, computational approaches require varying amounts of processing time. The increased use of RIS hypersepctral cameras in conservation science laboratories has been driven by the wider availability of lower cost hyperspectral cameras [17]. However, the analysis of the reflectance image data cubes, especially from paintings, remains laborious and time-intensive, even for an expert. Thus, processing of the image cubes remains the bottleneck in the overall workflow of RIS. Currently, the processing of these image cubes is performed using multivariate statistical software tools developed for remote sensing applications. One of the software tools used for the analysis of reflectance cubes of paintings is the ENVI Spectral Hourglass Wizard, which first involves reducing the spectral dimensionality, through principal component transforms, to ease the manual process of finding endmembers. While the ENVI Spectral Hourglass Wizard has been found to be robust, it works with a reduced set of reflectance spectra from the image cube and is semi-manual, requiring an experienced user [18; 19]. Making a good classification map with endmembers can take hours to a few days.