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Parallel computing of covariance matrix and its application on hyperspectral data process

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5 Author(s)
Mao-zhi Wang ; Geomathematics Key Lab of Sichuan Province, Chengdu University of Technology, 610059, China ; Da-ming Wang ; Wen-xi Xu ; Bin-yang Chen
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A parallel algorithm of covariance matrix, which is used to realize the dimensionality reduction process of hyperspectral image based on Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF), is proposed in this paper. The performance of the parallel algorithm according to the experiment under cluster circumstance with message passing interface (MPI) is discussed. The Gustafsun Law and Amdahl Law usually used to analyze the parallel algorithm results are also discussed in this experiment. At last, some further research areas and questions have been listed.

Published in:

2012 IEEE International Geoscience and Remote Sensing Symposium

Date of Conference:

22-27 July 2012