Parallel Implementation of the Modified Vertex Component Analysis Algorithm for Hyperspectral Unmixing Using OpenCL

Hyperspectral imaging represents the state-of-theart technique in those applications related to environmental monitoring, military surveillance, or rare mineral detection. However, one of the requirements of paramount importance when dealing with such scenarios is the ability to achieve real-time constraints taking into account the huge amount of data involved in processing this type of images. In this paper, the authors present for the first time a combination of the newly introduced modified vertex component analysis (MVCA) algorithm for the process of endmembers extraction together with the ability of GPUs to exploit its parallelism, giving, as a result, important speedup factors with respect to its sequential counterpart, while maintaining the same levels of endmember extraction accuracy than the vertex component analysis (VCA) algorithm. Furthermore, OpenCL ensures the use of generic computing platforms without being restricted to a particular vendor. The proposed approach has been assessed on a set of synthetic images as well as on the well-known Cuprite real image, showing that the most time-consuming operations are located on the matrix projection and the maximum search processes. Comparison of the proposed technique with a single-threaded C-based implementation of the MVCA algorithm shows a speedup factor of 8.87 for a 500 × 500 pixel artificial image with 20 endmembers and 7.14 for the wellknown Cuprite hyperspectral data set, including in both cases I/O transfers. Moreover, when the proposed implementation is compared with respect to a C-based sequential implementation of the VCA algorithm, a speedup of 115 has been achieved. In all the cases, the results obtained by the MVCA are the same as the ones obtained with the VCA; thus, the accuracy of the proposed algorithm is not compromised.