Hyperspectral imaging can be used for object detection and for discriminating between different objects based
on their spectral characteristics. One of the main problems of hyperspectral data analysis is the presence of
mixed pixels, due to the low spatial resolution of such images. This means that several spectrally pure signatures
(endmembers) are combined into the same mixed pixel. Linear spectral unmixing follows an unsupervised
approach which aims at inferring pure spectral signatures and their material fractions at each pixel of the scene.
The huge data volumes acquired by such sensors put stringent requirements on processing and unmixing methods.
This paper proposes an efficient implementation of a unsupervised linear unmixing method on GPUs using
CUDA. The method finds the smallest simplex by solving a sequence of nonsmooth convex subproblems using
variable splitting to obtain a constraint formulation, and then applying an augmented Lagrangian technique. The
parallel implementation of SISAL presented in this work exploits the GPU architecture at low level, using shared
memory and coalesced accesses to memory. The results herein presented indicate that the GPU implementation
can significantly accelerate the method’s execution over big datasets while maintaining the methods accuracy.
Keywords: Hyperspectral Endmember Extraction, Simplex Identification via Split Augmented Lagrangian
(SISAL), Graphics Processing Units (GPU), Onboard Processing.