  We describe the application of Semantic Segmentation by using the Self
Organizing Map technique to an high spatial and spectral resolution dataset
acquired along the H$\alpha$ line at 656.28 nm by the Interferometric
Bi-dimensional Spectrometer installed at the focus plane of the Dunn Solar
Telescope. This machine learning approach allowed us to identify several
features corresponding to the main structures of the solar photosphere and
chromosphere. The obtained results show the capability and flexibility of this
method to identifying and analyzing the fine structures which characterize the
solar activity in the low atmosphere. This is a first successful application of
the SOM technique to astrophysical data sets.
