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Compressive Sensing Hyperspectral Imager in the LWIR

Single pixel hyperspectral imager (HSI) utilizing compressive sensing (CS) and operating in the longwave infrared (LWIR) for chemical plume detection and tracking.

Capability Description: PSI has developed an environmentally hardened compressive sensing hyperspectral imager (CS-HSI) operating in the LWIR. The CS-HSI employs a single pixel architecture achieving an order of magnitude cost reduction relative to conventional HSIs employing an LWIR cooled focal plane array (FPA). A low-cost digital micromirror device is used to spatially encode the image of the scene and a Fabry-Perot tunable filter in conjunction with a single element photodetector is used to spectrally resolve the spatially compressed data. A CS processing module reconstructs the spatially compressed spectral data, where the module has been specifically tailored to chemical plume imaging. An automated target recognition algorithm is applied to the reconstructed hyperspectral data to detect and track chemical plumes in cluttered and dynamic backgrounds.

Key Features

The CS-HSI is sufficiently compact and environmentally hardened for integration in stationary or moving ground-based platforms. The sensor has demonstrated detection of surrogate chemical plumes with >90% compression supporting faster data acquisition and faster platform speeds of advance. Other key features of the CS-HSI include:

  •  A DMD to spatially encode an image of the scene exploiting LWIR radiometrically efficient micro-gratings,
  •  A LWIR tunable filter to achieve 20 spectral data bins ranging from 1050-1250 cm-1,
  •  A single element photodetector to capture the spatially compressed scene at a 10× lower cost than traditional LWIR FPAs,
  •  A CS processing module tailored towards chemical plume imaging to reconstruct the hyperspectral image, and
  •  Automated target recognition algorithms for detection of chemical plumes in cluttered backgrounds.
CS HSI

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Wide area persistent surveillance of chemical plumes at a 10x cost reduction relative to the state of the art.

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