Target detection with compressive sensing hyperspectral images

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

Research output: Contribution to conferencePaperpeer-review

6 Scopus citations

Abstract

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 GB
DOIs
StatePublished - 1 Jan 2017
EventImage and Signal Processing for Remote Sensing XXIII 2017 - Warsaw, Poland
Duration: 11 Sep 201713 Sep 2017

Conference

ConferenceImage and Signal Processing for Remote Sensing XXIII 2017
Country/TerritoryPoland
CityWarsaw
Period11/09/1713/09/17

Keywords

  • 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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