Compressive hyperspectral sensor for LWIR gas detection

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

doi:10.1117/12.919522

Cited by 12 publications

(5 citation statements)

References 7 publications

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“…However, the use of a LWIR FPA will limit cost reduction of this particular solution. An attempt has been made to integrate a tunable (Fabry-Perot) filter with a CS solution in the LWIR [2] with limited success in terms of performance, primarily owing to diffraction by the digital micromirror device (DMD) employed as a spatial light modulator (SLM). PSI in cooperation with Colorado State University (CSU) has demonstrated the feasibility of an innovative sensor that enables low-cost LWIR hyperspectral imaging for standoff chemical plume detection and identification.…”

Section: Capability Overviewmentioning

confidence: 99%

Longwave infrared compressive hyperspectral imager

2015

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Physical Sciences Inc. (PSI) is developing a longwave infrared (LWIR) compressive sensing hyperspectral imager (CS HSI) based on a single pixel architecture for standoff vapor phase plume detection. The sensor employs novel use of a high throughput stationary interferometer and a digital micromirror device (DMD) converted for LWIR operation in place of the traditional cooled LWIR focal plane array. The CS HSI represents a substantial cost reduction over the state of the art in LWIR HSI instruments. Radiometric improvements for using the DMD in the LWIR spectral range have been identified and implemented. In addition, CS measurement and sparsity bases specifically tailored to the CS HSI instrument and chemical plume imaging have been developed and validated using LWIR hyperspectral image streams of chemical plumes. These bases enable comparable statistics to detection based on uncompressed data.In this paper, we present a system model predicting the overall performance of the CS HSI system. Results from a breadboard build and test validating the system model are reported. In addition, the measurement and sparsity basis work demonstrating the plume detection on compressed hyperspectral images is presented.Key Words: Compressive sensing, hyperspectral imager, sparsity basis, single pixel camera TECHNOLOGY OVERVIEW Capability OverviewStandoff chemical vapor detection in the longwave infrared (LWIR) spectral range is currently dominated by high-priced interferometric systems, including the single pixel Joint Services Lightweight Standoff Chemical Agent Detector (JSLSCAD) and the Bruker Rapid as well as the imaging Telops HyperCam and Adaptive Infrared Imaging Spectroradiometer (AIRIS). The JSLSCAD employs an FTIR modulator and scanning mirrors to obtain a spatially coarse data cube acquired over 360° in azimuth and -10 to +50° in elevation. AIRIS employs a tunable Fabry-Perot filter and a LWIR FPA to acquire the data cube. Similar technologies are present in the HyperCam. The fundamental limitation of these systems with regard to wider deployment is the multiple $100k price. Secondary limitations include large size, interferometric moving parts, slow acquisition speed, and large data bandwidth. All of these factors can be mitigated with a low-cost hyperspectral sensor based on compressive sensing (CS) in a single pixel architecture.Efforts to apply CS in the LWIR spectral region include those by Fernandez et al [1] who explored the feasibility of a coded aperture LWIR HSI system. This approach employs an FPA to simultaneously capture spectral and spatial data in a single frame and boasts improved throughput relative to traditional grating based systems. However, the use of a LWIR FPA will limit cost reduction of this particular solution. An attempt has been made to integrate a tunable (Fabry-Perot) filter with a CS solution in the LWIR [2] with limited success in terms of performance, primarily owing to diffraction by the digital micromirror device (DMD) employed as a spatial light modulator (SLM). PSI in cooperation ...

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Section: Capability Overviewmentioning

confidence: 99%

Longwave infrared compressive hyperspectral imager

2015

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“…There are only few articles dealing with application of CS in mid-and far-IR HSI, e.g. [18,19,20]. The lack of publications could be attributed to the difficulties connected to the need of special optic elements and detectors in IR region.…”

Section: Using Compressed Sensing In Ir Hyperspectral Imagingmentioning

confidence: 99%

Hyperspectral imaging in infrared region using compressed sensing methods

Hlubuček

Žídek

2018

ACC

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We provide a review of hyperspectral imaging in infrared region as well as simulation of measurements and reconstructions of test datacubes using the compressed sensing method CASSI (Coded Aperture Snapshot Spectral Imaging). We simulate the presence of the chemical compounds on parts of the image and then we reconstruct its absorption spectrum and localization back from a single snapshot. In other words, we prove that in principle it is possible to reconstruct a sparse 3D datactube from a single 2D dataset. Furthermore, we discuss the quality of the reconstructed data and limitations of the chosen simulation method.

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“…Since there is only one sensor in the imaging system, the architecture of the imaging system can be much simpler than conventional cameras. The single-pixel detector can be much more efficient, less expensive while keeping higher sensitivity, and especially suitable for the case of infrared imaging [4].…”

Section: Introductionmentioning

confidence: 99%

A fast target detection and imaging method for compressive sensing Earth observation

Wang

Cao

et al. 2014

SPIE Proceedings

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The compressive sensing imaging technique, based on the realization of random measurement via active or passive devices (e.g., DMD), has attracted more and more attention. For imaging target of interest within large uniform scene (e.g., ships in the sea), high-resolution image was usually reconstructed and then used to detect targets, however the process is time-consuming, and moreover only part of the image consists of the targets of interest. In this paper, the stepwise multi-resolution fast target detection and imaging method through the combination of different numbers of DMD mirrors was explored. Low resolution image for larger area target searching and successively higher resolution image for smaller area containing the targets were reconstructed. Also, non-imaging fast target detection was realized based on the detector energy intensity, which includes the steps of rough target positioning by successively opening DMD blocks and accurate target positioning by adjusting the rough areas via intelligent search algorithm. Simulation experiments were carried out to compare the proposed method with traditional method. The result shows the area of the ships are accurately positioned without reconstructing the image by the proposed method and the multi-level scale imaging for suspect areas is realized. Compared with traditional target detection method from the reconstructed image, the proposed method not only highly enhances the measuring and reconstruction efficiency but also improves the positioning accuracy, which would be more significant for large area scene.

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Compressive hyperspectral sensor for LWIR gas detection

Cited by 12 publications

References 7 publications

Longwave infrared compressive hyperspectral imager

Longwave infrared compressive hyperspectral imager

Hyperspectral imaging in infrared region using compressed sensing methods

A fast target detection and imaging method for compressive sensing Earth observation

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