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Optical Engineering

Karhunen-Loève transform for compressive sampling hyperspectral images
Author(s): Lei Liu; Jingwen Yan; Xianwei Zheng; Hong Peng; Di Guo; Xiaobo Qu
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Paper Abstract

Compressed sensing (CS) is a new jointly sampling and compression technology for remote sensing. In hyperspectral imaging, a typical CS method encodes the two-dimensional (2-D) spatial information of each spectral band or encodes the third spectral information simultaneously. However, encoding the spatial information is much easier than encoding the spectral information. Therefore, it is crucial to make use of the spectral information to improve the compression rate on 2-D CS data. We propose to encode the third spectral information with an adaptive Karhunen–Loève transform. With a mathematical proof, we show that interspectral correlations are preserved among 2-D randomly encoded spatial information. This property means that one can compress 2-D CS data effectively with a Karhunen–Loève transform. Experiments demonstrate that the proposed method can better reconstruct both spectral curves and spatial images than traditional compression methods at the bit rates 0 to 1.

Paper Details

Date Published: 14 January 2015
PDF: 11 pages
Opt. Eng. 54(1) 014106 doi: 10.1117/1.OE.54.1.014106
Published in: Optical Engineering Volume 54, Issue 1
Show Author Affiliations
Lei Liu, Shantou Univ. (China)
Jingwen Yan, Shantou Univ. (China)
Xianwei Zheng, Shantou Univ. (China)
Hong Peng, Shantou Univ. (China)
Di Guo, Xiamen Univ. of Technology (China)
Xiaobo Qu, Xiamen Univ. (China)

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