## 2 Materials and Methods
### RIS Image Cube Collection and Pre-Processing
#### 2.2.2 Spectral Training Libraries  
Two spectral libraries of material-labeled classes were built to train the Reflectance and Derivative CNN classification models from the 10 illuminations reserved for training (see Table 1). The materials needed to build classes are those present in the RIS image cube: the paints, gold leaf, and parchment. To do this, the classes for the Reflectance CNN were manually built using the Region of Interest (ROI) tool in ENVI to extract spatial areas of high spectral similarity from the reflectance image cube (Figure 3a), and, hence, the same material composition. The spectral library for the Derivative CNN were created using the exact same ROIs used for the Reflectance CNN but directly from the first-derivative calculated RIS cube. See Figure 3b, where the false color image of the 1964.8.1218's derivative cube shows that the ROIs extracted correspond exactly to those in the reflectance cube in Figure 3a. The most complex part of building the library was to create the pigment-labeled classes representing the colorants in the illuminations. The objective was to create labeled classes encompassing a wide range of color tones and hues involving a primary colorant or mixture/layers of colorants. For a single colorant, this would involve collecting reflectance spectra having a range of hues and tones. Each class in the library spans the range of hues and tones the artistic school is achieving with these pigments. For example, in defining the pigment malachite class, several green colored areas having the same spectral features specific to malachite in the VNIR were manually selected from the dragon heads with a light blue-green ROI (Figure 3a). The spectra in each class were then plotted to make sure that no clear spectral outliers (e.g., other materials) were accidentally included in the training class. Care was also taken to ensure the spectra for a given class encompassed the range of variation in hue and tone present. This was carried out for all malachite applications across the reserved training illuminations; extracted spectra with similar profiles across different  
Figure 2: Images of the experimental setup: RIS capture of an attributed Master of the Cypresses illumination from the Rosenwald Collection at the National Gallery of Art in Washington D.C.  
illuminations were placed into the same training class, \"Malachite\". The same process was applied to create the other labeled paint classes. In the end, twenty-seven training classes were created for each spectral library. To confirm or supplement the pigment assignments by an analysis of the RIS spectra, results from single-spot spectroscopic techniques fiber-optic reflectance spectroscopy (FORS, 350-2500 nm, ASD Fieldspec 3, Malvern PANalytical, Malvern, UK) and X-ray fluorescence spectroscopy (XRF, in-house design: Rh X-ray source, 50 kV, 0.75 mA (XOS, NY) with a silicon drift detector (Vortex-90 EX, Hitachi High-Technologies Science America, Inc., Schaumburg, IL, USA)) were used. The labels for classes representing paint applications were assigned by the pigment or pigments that dominate the RIS spectral signature. For example, a class label of \"Ultramarine\" meant that absorptions due to ultramarine's molecular structure dominated the spectral features of the reflectance spectra in that class. The reflectance values for ultramarine's absorption centered at 600 nm are low, below 20% (see Figure 3c). Reflectance values increase in areas that have generous amounts of white pigment mixed into paints. If another pigment had a significant contribution, the paint was noted as either a mixed pigment class (with) or a layered pigment class (over) with two pigment names, e.g., \"Azurite with red lake\" or \"Red lake over red lead\". Areas of layered pigments were examined and confirmed under the microscope.