Implmentation of a covariance-based principal component analysis algorithm for hyperspectral imaging applications with multi-threading in both CPU and GPU

Abstract

Principle component analysis (PCA) [1] is widely utilized in hyperspectral image analysis [3, 4, 5]. There are three major approaches of principle component analysis: singular value decomposition (SVD) [2], covariance-matrix and iterative method (NIPALS) [6, 7]. In our previous work [9], we have demonstrated the advantage of the GPU implementation of covariance method for medium-sized hyperspectral images. In this paper, we present an improvement which combines the multithreading in CPU, GPU and CUDA's graphics interoperability [8]. It is found that this combined framework approaches real-time processing much further. © 2012 IEEE.