  Old master drawings were mostly created step by step in several layers using
different materials. To art historians and restorers, examination of these
layers brings various insights into the artistic work process and helps to
answer questions about the object, its attribution and its authenticity.
However, these layers typically overlap and are oftentimes difficult to
differentiate with the unaided eye. For example, a common layer combination is
red chalk under ink.
  In this work, we propose an image processing pipeline that operates on
hyperspectral images to separate such layers. Using this pipeline, we show that
hyperspectral images enable better layer separation than RGB images, and that
spectral focus stacking aids the layer separation. In particular, we propose to
use two descriptors in hyperspectral historical document analysis, namely
hyper-hue and extended multi-attribute profile (EMAP). Our comparative results
with other features underline the efficacy of the three proposed improvements.
