Parallel hyperspectral unmixing problem is considered in this paper.
A semisupervised approach is developed under the linear mixture
model, where the abundance's physical constraints are taken into
account. The proposed approach relies on the increasing availability
of spectral libraries of materials measured on the ground instead of
resorting to endmember extraction methods.

Since Libraries are potentially very large and hyperspectral
datasets are of high dimensionality a parallel implementation in a
pixel-by-pixel fashion is derived to properly exploits the graphics
processing units (GPU) architecture at low level, thus taking full
advantage of the computational power of GPUs. Experimental results
obtained for real hyperspectral datasets reveal significant speedup
factors, up to 164 times, with regards to optimized serial
implementation.