Sparse Representation Based on Principal Component for Compressed Sensing Hyperspectral Images

Bin Zhou; Kun Li; Guang Xu

doi:10.4028/www.scientific.net/AMM.321-324.1154

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 321-324Sparse Representation Based on Principal Component...

Sparse Representation Based on Principal Component for Compressed Sensing Hyperspectral Images

Abstract:

Compressed sensing (CS) is a new developed theoretical framework for information acquisition and processing that breaks through the conventional Nyquist sampling limit. This paper proposes a sparse representation schemes based on principal component analysis (PCA) for CS that will be used for hyperspectral images compressed sampling. This scheme employs the prediction transform matrix to remove the correlations among successive hyperspectral measurement vectors. Experiment processes using the hyperspectral image from Earth Observing One (EO-1), and it shows a desired result both at reconstruction and denoising.

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Periodical:

Applied Mechanics and Materials (Volumes 321-324)

Pages:

1154-1157

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.321-324.1154

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Online since:

June 2013

Authors:

Bin Zhou, Kun Li, Guang Xu

Keywords:

Compressed Sensing, Hyperspectral Image, Principal Component Analysis (PCA), Sparse Representation

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References

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