  Hyperspectral imaging has become an increasingly used tool in the analysis of
works of art. However, the quality of the acquired data and the processing of
that data to produce accurate and reproducible spectral image cubes can be a
challenge to many cultural heritage users. The calibration of data that is both
spectrally and spatially accurate is an essential step in order to obtain
useful and relevant results from hyperspectral imaging. Data that is too noisy
or inaccurate will produce sub-optimal results when used for pigment mapping,
the detection of hidden features, change detection or for quantitative spectral
documentation. To help address this, therefore, we will examine the specific
acquisition and calibration workflows necessary for works of art. These
workflows includes the key parameters that must be addressed during acquisition
and the essential steps and issues at each of the stages required during
post-processing in order to fully calibrate hyperspectral data. In addition we
will look in detail at the key issues that affect data quality and propose
practical solutions that can make significant differences to overall
hyperspectral image quality.
