Datacube segmentation via Deep Spectral Clustering
CoRR(2024)
摘要
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.
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