Karhunen-Loève transform for compressive sampling hyperspectral images

Lei Liu; Jingwen Yan; Xianwei Zheng; Hong Peng; Di Guo; Xiaobo Qu

doi:10.1117/1.OE.54.1.014106

14 January 2015 Karhunen-Loève transform for compressive sampling hyperspectral images

Lei Liu, Jingwen Yan, Xianwei Zheng, Hong Peng, Di Guo, Xiaobo Qu

Author Affiliations +

Optical Engineering, Vol. 54, Issue 1, 014106 (January 2015). https://doi.org/10.1117/1.OE.54.1.014106

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.

Citation Download Citation

Lei Liu, Jingwen Yan, Xianwei Zheng, Hong Peng, Di Guo, and Xiaobo Qu "Karhunen-Loève transform for compressive sampling hyperspectral images," Optical Engineering 54(1), 014106 (14 January 2015). https://doi.org/10.1117/1.OE.54.1.014106

Published: 14 January 2015

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