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

Yitzhak August, Chaim Vachman, Yair Rivenson, Adrian Stern

Research output: Contribution to journalArticlepeer-review

157 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 languageEnglish
Pages (from-to)D46-D54
JournalApplied Optics
Volume52
Issue number10
DOIs
StatePublished - 1 Apr 2013

ASJC Scopus subject areas

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

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