## 2 Materials and Methods
### RIS Image Cube Collection and Pre-Processing  
#### 2.2.1 Reflectance Imaging Spectroscopy  
Visible to near-infrared (VNIR, 400-1000 nm) reflectance imaging spectroscopy (RIS) was used to collect image cubes of the illuminations. These miniatures were imaged using a modified hyperspectral camera (SOC-730, Surface Optics Corporation, San Diego, CA, USA) with a V10E transmission grating spectrometer (Specim Corporation, Oulu, Finland) and a high sensitivity backside electron-multiplying (EM) CCD detector array (ProEM-1024, Princeton Instruments, Trenton, NJ, USA), with a per line exposure time of 150 milliseconds [30]. The camera features 2.4 nm spectral sampling, 280 spectral channels, and 1024 spatial pixels along the slit of the spectrometer. Two 50 watt Solux lamps (4700 K, Tailored Lighting, Inc., Rochester, NY, USA) powered by a DC power supply were used to illuminate the miniatures at \(\sim\)45 degrees (Figure 2) from the painting normal. An image cube of a diffuse white panel (Labsphere, Inc., North Sutton, NH, USA) was collected to correct for non-uniformity in lighting and to calibrate the spectral data to achieve apparent reflectance. This was carried out by dividing each illumination's dark noise-corrected image cube by the white panel's dark-noise corrected image cube. The reflectance image cubes were collected with a spatial sampling of 0.2 mm per pixel.  
Each of the RIS image cubes was initially pre-processed by applying masks across the image scene to exclude some of the areas with gold foil (due to issues with specular reflectance) as well as background content not relevant to the artistic decoration (e.g., bulk areas of parchment). These masks were created by using the region of interest (ROI) tool in ENVI (Environment for Visualizing Images, L3 Harris Technologies, Melbourne, FL, USA), an image processing software for processing and analyzing remote sensing data. While the spectra from the reflectance image cubes have some noise (approximately \(<\)0.3% RMS in reflectance), before proceeding to take the first derivative of the image cubes, it was necessary to reduce the noise further. This was carried out using the Minimum Noise Fraction (MNF) transform function within ENVI [31]. This produces a set of eigenimages that represents relevant image content as well as noise in the image cube. To de-noise the image cube and bring it back to the original diffuse reflectance space, we applied an inverse MNF transform, keeping only the set of eigenimages with image content (i.e., observable painting features). The first-derivative RIS cubes were then calculated from the MNF de-noised diffuse reflectance image cubes using derivative filtering implemented  
Figure 1: Color images of the illuminations used to test the classification algorithms. (**a**) Illumination 1964.8.1226 (_Initial I with [PERSON] [PERSON]_) and (**b**) 1964.8.1223 (_Initial D_) from the [PERSON] collection at the National Gallery of Art in Washington D.C.  
in a Matlab script. The derivative filtering uses a top hat filter whose width was set to 5 channels. Because of the width of the filter, the spectral range of the derivative cubes was reduced from 400 to 950 nm to 414 to 900 nm.