Compressive hyperspectral imaging by random separable projections in both the spatial and the spectral domains

Yitzhak August; Chaim Vachman; Yair Rivenson; Adrian Stern

doi:10.1364/AO.52.000D46

Compressive hyperspectral imaging by random separable projections in both the spatial and the spectral domains

Yitzhak August, Chaim Vachman, Yair Rivenson, Adrian Stern

Unit of Photonics and Electro-Optics Engineering

Research output: Contribution to journal › Article › peer-review

153 Scopus citations

Abstract

An efficient method and system for compressive sensing of hyperspectral data is presented. Compression efficiency is achieved by randomly encoding both the spatial and the spectral domains of the hyperspectral datacube. Separable sensing architecture is used to reduce the computational complexity associated with the compressive sensing of a large volume of data, which is typical of hyperspectral imaging. The system enables optimizing the ratio between the spatial and the spectral compression sensing ratios. The method is demonstrated by simulations performed on real hyperspectral data.

Original language	English
Pages (from-to)	D46-D54
Journal	Applied Optics
Volume	52
Issue number	10
DOIs	https://doi.org/10.1364/AO.52.000D46
State	Published - 1 Apr 2013

ASJC Scopus subject areas

Atomic and Molecular Physics, and Optics
Engineering (miscellaneous)
Electrical and Electronic Engineering

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abstract = "An efficient method and system for compressive sensing of hyperspectral data is presented. Compression efficiency is achieved by randomly encoding both the spatial and the spectral domains of the hyperspectral datacube. Separable sensing architecture is used to reduce the computational complexity associated with the compressive sensing of a large volume of data, which is typical of hyperspectral imaging. The system enables optimizing the ratio between the spatial and the spectral compression sensing ratios. The method is demonstrated by simulations performed on real hyperspectral data.",

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AB - An efficient method and system for compressive sensing of hyperspectral data is presented. Compression efficiency is achieved by randomly encoding both the spatial and the spectral domains of the hyperspectral datacube. Separable sensing architecture is used to reduce the computational complexity associated with the compressive sensing of a large volume of data, which is typical of hyperspectral imaging. The system enables optimizing the ratio between the spatial and the spectral compression sensing ratios. The method is demonstrated by simulations performed on real hyperspectral data.

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Compressive hyperspectral imaging by random separable projections in both the spatial and the spectral domains

Abstract

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