Accelerating hyperspectral manifold learning using graphical processing units

Abstract

Manifold learning has been widely studied in pattern recognition, image processing, and machine learning. A large number of nonlinear manifold learning methods have been proposed attempting to preserve a different geometrical property of the underlying manifold. In contrast, its application to hyperspectral images is computationally difficult due to the calculation of distances among spectral values in high-dimensional spaces. This paper compares feature extraction algorithms using isomap, Laplacian Eigenmaps, and local linear embedding in real hyperspectral images. They are implemented using massively parallel general purpose Graphical Processor Units (GPUs) to speed up computation. Their performance in classification of hyperspectral images and speed up of their computation is presented. Results using real and synthetic hyperspectral scenarios are presented. Additionally, a formulation including spatial information in these manifold learning algorithms is presented.