Spatial versus spectral compression ratio in compressive sensing of hyperspectral imaging

Yitzhak August, Chaim Vachman, Adrian Stern

Research output: Contribution to conferencePaperpeer-review

13 Scopus citations

Abstract

Compressive hyperspectral imaging is based on the fact that hyperspectral data is highly redundant. However, there is no symmetry between the compressibility of the spatial and spectral domains, and that should be taken into account for optimal compressive hyperspectral imaging system design. Here we present a study of the influence of the ratio between the compression in the spatial and spectral domains on the performance of a 3D separable compressive hyperspectral imaging method we recently developed.

Original languageEnglish GB
DOIs
StatePublished - 8 Aug 2013
EventCompressive Sensing II - Baltimore, MD, United States
Duration: 2 May 20133 May 2013

Conference

ConferenceCompressive Sensing II
Country/TerritoryUnited States
CityBaltimore, MD
Period2/05/133/05/13

Keywords

  • CHISSS
  • Compressive sensing
  • Hyperspectral imaging
  • Separable sensing

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Spatial versus spectral compression ratio in compressive sensing of hyperspectral imaging'. Together they form a unique fingerprint.

Cite this