Instrumentation, Techniques, and Measurement

Karhunen-Loève transform for compressive sampling hyperspectral images

[+] Author Affiliations
Lei Liu

Shantou University, Medical College, Shantou 515063, China

Shantou University, Department of Mathematics, Shantou 515063, China

Jingwen Yan

Shantou University, Guangdong Provincial Key Laboratory of Digital Signal and Image Processing Techniques, Department of Electrical Engineering, Shantou 515063, China

Xianwei Zheng

Shantou University, Department of Mathematics, Shantou 515063, China

Hong Peng

Shantou University, Guangdong Provincial Key Laboratory of Digital Signal and Image Processing Techniques, Department of Electrical Engineering, Shantou 515063, China

Di Guo

Xiamen University of Technology, School of Computer and Information Engineering, Fujian Provincial University Key Laboratory of Internet of Things Application Technology, Xiamen 361024, China

Xiaobo Qu

Xiamen University, Department of Electronic Science, Xiamen 361005, China

Opt. Eng. 54(1), 014106 (Jan 14, 2015). doi:10.1117/1.OE.54.1.014106
History: Received April 22, 2014; Accepted November 25, 2014
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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.

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© 2015 Society of Photo-Optical Instrumentation Engineers

Citation

Lei Liu ; Jingwen Yan ; Xianwei Zheng ; Hong Peng ; Di Guo, et al.
"Karhunen-Loève transform for compressive sampling hyperspectral images", Opt. Eng. 54(1), 014106 (Jan 14, 2015). ; http://dx.doi.org/10.1117/1.OE.54.1.014106


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