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

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2 Author(s)
Jian Zhang ; Centre for Remote Imaging, Sensing & Process. (CRISP), Nat. Univ. of Singapore, Singapore, Singapore ; Kim Hwa Lim

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.

Published in:

Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International

Date of Conference:

22-27 July 2012