Reflectance Hyperspectral data processing on a set of Picasso paintings: Which algorithm provides what? A comparative analysis of multivariate, statistical and artificial intelligence methods

Recently a new trend towards a more systematic use of reflectance Hyperspectral Imaging (HSI) has emerged in major museums. Extensive acquisition of HSI data opens up new research topics in terms of comparative analysis, creation and population of spectral databases, linking and crossing information. However, a full exploitation of these big-size data-sets unavoidably raises new issues about data-handling and processing methods. Along with statistical and multivariate analysis, solutions can be borrowed from the Artificial Intelligence (AI) area, using Machine Learning (ML) and Deep Learning (DL) methods. In this explorative study, different algorithms based on AI methods are applied to process HSI data acquired on three Picasso' paintings from the Museu Picasso collection in Barcellona. By using a "data-mining approach" the HSI-data are examined to unveil new correlations and extract embedded information.