This paper proposes to use compression-based similarity measures to cluster spectral signatures on the basis of their similarities. Such
universal distances estimate the shared information between two objects by comparing their compression factors, which can be obtained
by any standard compressor. Experiments on spectra, both collected in the field and selected from a hyperspectral scene, show that
these methods may outperform traditional choices for spectral distances based on vector processing such as Spectral Angle, Spectral
Information Divergence, Spectral Correlation, and Euclidean Distance
