  Extended Vision techniques are ubiquitous in physics. However, the data cubes
steaming from such analysis often pose a challenge in their interpretation, due
to the intrinsic difficulty in discerning the relevant information from the
spectra composing the data cube.
  Furthermore, the huge dimensionality of data cube spectra poses a complex
task in its statistical interpretation; nevertheless, this complexity contains
a massive amount of statistical information that can be exploited in an
unsupervised manner to outline some essential properties of the case study at
hand, e.g.~it is possible to obtain an image segmentation via (deep) clustering
of data-cube's spectra, performed in a suitably defined low-dimensional
embedding space.
  To tackle this topic, we explore the possibility of applying unsupervised
clustering methods in encoded space, i.e. perform deep clustering on the
spectral properties of datacube pixels. A statistical dimensional reduction is
performed by an ad hoc trained (Variational) AutoEncoder, in charge of mapping
spectra into lower dimensional metric spaces, while the clustering process is
performed by a (learnable) iterative K-Means clustering algorithm.
  We apply this technique to two different use cases, of different physical
origins: a set of Macro mapping X-Ray Fluorescence (MA-XRF) synthetic data on
pictorial artworks, and a dataset of simulated astrophysical observations.
