  Minerals detection over large volume of spectra is the challenge addressed by
current hyperspectral imaging spectrometer in Planetary Science. Instruments
such OMEGA (Mars Express), CRISM (Mars Reconnaissance Orbiter), M^{3}
(Chandrayaan-1), VIRTIS (Rosetta) and many more, have been producing very large
datasets since one decade. We propose here a fast supervised detection
algorithm called LinMin, in the framework of linear unmixing, with innovative
arrangement in order to treat non-linear cases due to radiative transfer in
both atmosphere and surface. We use reference laboratory and synthetic spectral
library. Additional spectra are used in order to mimic the effect of Martian
aerosols, grain size, and observation geometry discrepancies between reference
and observed spectra. The proposed algorithm estimates the uncertainty on
mixing coefficient from the uncertainty of observed spectra. Both numerical and
observational tests validate the approach. Fast parallel implementation of the
best algorithm (IPLS) on Graphics Processing Units (GPU) allows to
significantly reduce the computation cost by a factor of 40.
