Parallel implementation of the N-FINDR endmember extraction algorithm on commodity graphics processing units
Endmember extraction is an important technique in the context of spectral unmixing of remotely sensed hyperspectral data. Winter's N-FINDR algorithm is one of the most widely used and successfully applied methods for endmember extraction from remotely sensed hyperspectral images. Depending on the dimensionality of the hyperspectral data, the algorithm can be time consuming. In this paper, we propose a new parallel implementation of the N-FINDR algorithm. The proposed implementation is quantitatively assessed in terms of both endmember extraction accuracy and parallel efficiency, using two different generations of commercial graphical processing units (GPUs) from NVidia. Our experimental results indicate that the parallel implementation performs better with latest-generation GPUs, thus taking advantage of the increased processing power of such units.