Compressive Hyperspectral Sensor for LWIR gas detection

Thomas A. Russell, Lenore McMackin, Bob Bridge, Richard G. Baraniuk

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

Abstract

Focal plane arrays with associated electronics and cooling are a substantial portion of the cost, complexity, size, weight, and power requirements of Long-Wave IR (LWIR) imagers. Hyperspectral LWIR imagers add significant data volume burden as they collect a high-resolution spectrum at each pixel. We report here on a LWIR Hyperspectral Sensor that applies Compressive Sensing (CS) in order to achieve benefits in these areas. The sensor applies single-pixel detection technology demonstrated by Rice University. The single-pixel approach uses a Digital Micro-mirror Device (DMD) to reflect and multiplex the light from a random assortment of pixels onto the detector. This is repeated for a number of measurements much less than the total number of scene pixels. We have extended this architecture to hyperspectral LWIR sensing by inserting a Fabry-Perot spectrometer in the optical path. This compressive hyperspectral imager collects all three dimensions on a single detection element, greatly reducing the size, weight and power requirements of the system relative to traditional approaches, while also reducing data volume. The CS architecture also supports innovative adaptive approaches to sensing, as the DMD device allows control over the selection of spatial scene pixels to be multiplexed on the detector. We are applying this advantage to the detection of plume gases, by adaptively locating and concentrating target energy. A key challenge in this system is the diffraction loss produce by the DMD in the LWIR. We report the results of testing DMD operation in the LWIR, as well as system spatial and spectral performance.

Original languageEnglish (US)
Title of host publicationCompressive Sensing
Volume8365
DOIs
StatePublished - Jul 23 2012
EventCompressive Sensing - Baltimore, MD, United States
Duration: Apr 26 2012Apr 27 2012

Other

OtherCompressive Sensing
CountryUnited States
CityBaltimore, MD
Period4/26/124/27/12

Keywords

  • Adaptive sensing
  • Compressive Sensing
  • Gas detection
  • Hyperspectral
  • LWIR

ASJC Scopus subject areas

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

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