Data Reduction of Hyperspectral Radio-astronomical Images for Galaxy Cluster Segmentation

This paper proposes a reduction-segmentation scheme for radioastronomical cubes. In order to avoid the curse of dimensionality phenomenon, a reduction technique is proposed as preprocessing step before classification. On each site of the image a spectrum is observed, exhibiting few spectral rays, modeled as a weighted mixture of selected Gaussian functions. These weights feed a Hierarchical Markovian classifier, in order to cluster spatially homogeneous areas with similar spectra behaviors. Such approach is very useful in radioastronomical context, because it allows to highlight regions of astronomical interest where astrophysical investigations may focuse.