In this paper, an RVC-CAL library to implement dimensionality reduction and endmember extraction is presented. The results obtained show significant improvements with regard to a state-of-the-art analysis tool. A speedup of 30% is carried out using the complete processing chain and, in particular, a speedup of 5% has been achieved in the dimensionality reduction step. This dimensionality reduction takes ten of the thirteen seconds that the whole system needs to analyze one of the images. In addition, the RVC-CAL library is an excellent tool to simplify the implementation process of HI algorithms. Effectively, during the experimental test, the potential of the RVC-CAL library to reveal possible bottlenecks present in the HI processing chain and, therefore, to improve the system performance to achieve real-time constraints has been shown. Furthermore, the RVC-CAL library provides the possibility of system performance testing.
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D. Madroñal Quintín, R. Lazcano López, E. Juárez Martínez, C. Sanz Álvaro, "Dimensionality reduction and endmember extraction for hyperspectral imaging using an RVC-CAL library," Proc. SPIE 9646, High-Performance Computing in Remote Sensing V, 964604 (20 October 2015);