Calibration of high dimensional compressive sensing systems: A case study in compressive hyperspectral imaging

Phillip Poon, Matthew Dunlop, ESTEBAN MAURICIO VERA ROJAS, Dathon Golish, Michael Gehm

Research output: Contribution to journalConference articlepeer-review

Abstract

Compressive Sensing (CS) is a set of techniques that can faithfully acquire a signal from sub- Nyquist measurements, provided the class of signals have certain broadly-applicable prop- erties. Reconstruction (or exploitation) of the signal from these sub-Nyquist measurements requires a forward model-knowledge of how the system maps signals to measurements. In high-dimensional CS systems, determination of this forward model via direct measurement of the system response to the complete set of impulse functions is impractical. In this paper, we will discuss the development of a parameterized forward model for the Adaptive, Feature- Specific Spectral Imaging Classifier (AFSSI-C), an experimental compressive spectral image classifier. This parameterized forward model drastically reduces the number of calibration measurements.

Original languageEnglish
JournalProceedings of the International Telemetering Conference
Volume49
StatePublished - 1 Dec 2013
EventITC/USA 2013: 49th Annual International Telemetering Conference and Technical Exhibition - Meeting all the Challenges of Telemetry, 2013 - Las Vegas,NV, United States
Duration: 21 Oct 201324 Oct 2013

Keywords

  • Calibration
  • Compressive sensing
  • Hyperspectral imaging

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