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Proceedings Paper

Compressive hyperspectral sensor for LWIR gas detection
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Paper 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.

Paper Details

Date Published: 8 June 2012
PDF: 13 pages
Proc. SPIE 8365, Compressive Sensing, 83650C (8 June 2012); doi: 10.1117/12.919522
Show Author Affiliations
Thomas A. Russell, Raytheon Applied Signal Technology, Inc. (United States)
Lenore McMackin, Inview Technology Corp. (United States)
Bob Bridge, Inview Technology Corp. (United States)
Richard Baraniuk, Rice Univ. (United States)


Published in SPIE Proceedings Vol. 8365:
Compressive Sensing
Fauzia Ahmad, Editor(s)

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