In this paper, the PCA (Principal Component Analysis) algorithm is implemented using the RVC-CAL dataflow language, which specifies a system as a set of blocks or actors and allows its parallelization by scheduling the blocks over different processing units. Two implementations of PCA for hyperspectral images have been compared when aiming at obtaining the first few principal components: first, the algorithm has been implemented using the Jacobi approach for obtaining the eigenvectors; thereafter, the NIPALS-PCA algorithm, which approximates the principal components iteratively, has also been studied. Both implementations have been compared in terms of accuracy and computation time; then, the parallelization of both models has also been analyzed.
These comparisons show promising results in terms of computation time and parallelization: the performance of the NIPALS-PCA algorithm is clearly better when only the first principal component is achieved, while the partitioning of the algorithm execution over several cores shows an important speedup for the PCA-Jacobi. Thus, experimental results show the potential of RVC–CAL to automatically generate implementations which process in real-time the large volumes of information of hyperspectral sensors, as it provides advanced semantics for exploiting system parallelization.