Target detection with compressive sensing hyperspectral images

Yaniv Oiknine, Daniel Gedalin, Isaac August, Dan G. Blumberg, Stanley R. Rotman, Adrian Stern

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations


During the past years, several compressive spectral imaging techniques were developed. With these techniques, an optically compressed version of the spectral datacube is captured. Consequently, the information about the object and targets is captured in a lower dimensional space. A question that rises is whether the reduction of the captured space affects the target detection performance. The answer to this question depends on the compressive spectral imaging technique employed. In most compressive spectral imaging techniques, the target detection performance is deteriorated. We show that our recently introduced technique, dubbed Compressive Sensing Miniature Ultra-Spectral Imaging (CSMUSI), yields similar target detection and false detection rates to that of conventional hyperspectral cameras.

Original languageEnglish
Title of host publicationImage and Signal Processing for Remote Sensing XXIII
EditorsFrancesca Bovolo, Lorenzo Bruzzone
ISBN (Electronic)9781510613188
StatePublished - 1 Jan 2017
EventImage and Signal Processing for Remote Sensing XXIII 2017 - Warsaw, Poland
Duration: 11 Sep 201713 Sep 2017

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


ConferenceImage and Signal Processing for Remote Sensing XXIII 2017


  • Compressive sensing
  • Cs-musi
  • Hyperspectral
  • Liquid crystal
  • Multiplexing system
  • Point target detection
  • Spectral modulation

ASJC Scopus subject areas

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


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