Chemical vapor detection with a multispectral thermal imager

Althouse, Mark L.G.; Chang, Chein‐I

doi:10.1117/12.55995

Cited by 21 publications

(4 citation statements)

References 11 publications

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“…With forward-looking IR (FLIR) systems enhanced by spectral filters 9 it is possible to visualize vapor clouds, but the selectivity of this method is low because of the limited spectral information offered by these systems. Imaging spectrometers with high spatial resolution based on FLIR detector systems combined with a tunable filter, such as an acousto-optical filter 10 or a tunable etalon, 11 have been developed.…”

Section: Introductionmentioning

confidence: 99%

Toxic cloud imaging by infrared spectrometry: A scanning FTIR system for identification and visualization

Harig

Matz

2001

Field Analytical Chem & Tech

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Passive remote sensing by Fourier-transform infrared (FTIR) spectrometry allows detection and identification of toxic clouds in the atmosphere. However, the probability of detection is dependent upon the background of the field of view of the spectrometer because the signal is a function of the difference between the temperature of the cloud and the brightness temperature of the background. For small temperature differences the signal is proportional to this difference. Thus, it is possible to enhance the probability of detection by aiming the spectrometer in a direction that yields a high temperature difference between the cloud and the background. If this alignment is performed manually, the probability of detection is strongly influenced by the operator. By scanning all fields of view within the area in which a cloud is expected the probability of detection is maximized. Moreover, it is possible to visualize the cloud. An imaging passive remote-sensing system comprised of an FTIR spectrometer, an azimuth-elevationscanning mirror, a data-acquisition and control system with a digital signal processor (DSP), and a personal computer has been developed. The DSP system controls the scanning mirror, collects the interferograms of the FTIR spectrometer, and performs the Fourier transformation. The spectra are transferred to the personal computer and analyzed by a real-time identification algorithm that does not require background spectra for the analysis. The results of the identification algorithm are visualized in false color images. The system has a high selectivity and low noise-equivalent spectral radiance, and it allows localization of clouds and their sources. The automatic identification algorithm, the scanner system, the software for real-time identification and imaging, and the results of field measurements are pre-

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Section: Introductionmentioning

confidence: 99%

Toxic cloud imaging by infrared spectrometry: A scanning FTIR system for identification and visualization

Harig

Matz

2001

Field Analytical Chem & Tech

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“…It designs an adaptive filter that minimizes the filter output energy while constraining a desired target signature by a specific gain. The idea of CEM was derived from the MVDR beamformer in array processing [21], [22] and was first used in chemical remote sensing [23]. It a special case of Frost's linearly constrained adaptive beamforming approach [24].…”

Section: Introductionmentioning

confidence: 99%

Constrained subpixel target detection for remotely sensed imagery

Chang

Heinz

2000

IEEE Trans. Geosci. Remote Sensing

310

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Target detection in remotely sensed images can be conducted spatially, spectrally or both. The difficulty of detecting targets in remotely sensed images with spatial image analysis arises from the fact that the ground sampling distance is generally larger than the size of targets of interest in which case targets are embedded in a single pixel and cannot be detected spatially. Under this circumstance target detection must be carried out at subpixel level and spectral analysis offers a valuable alternative. In this paper, the problem of subpixel spectral detection of targets in remote sensing images is considered, where two constrained target detection approaches are studied and compared. One is a target abundance-constrained approach, referred to as nonnegatively constrained least squares (NCLS) method. It is a constrained least squares spectral mixture analysis method which implements a nonnegativity constraint on the abundance fractions of targets of interest. Another is a target signature-constrained approach, called constrained energy minimization (CEM) method. It constrains the desired target signature with a specific gain while minimizing effects caused by other unknown signatures. A quantitative study is conducted to analyze the advantages and disadvantages of both methods. Some suggestions are further proposed to mitigate their disadvantages. Index Terms-Constrained energy minimization (CEM), nonnegatively constrained least squares (NCLS), orthogonal subspace projection (OSP). ACRONYMSANC Abundance nonnegativity constraint. ASC Abundance sum-to-one constraint. AVIRIS Airborne visible/infrared imaging spectrometer. CEM Constrained energy minimization. FCLS Fully constrained least squares. FIR Finite impulse response. FNNLS Fast NNLS. FNNLSb Second version of FNNLS. HYDICE Hyperspectral digital imagery collection experiment. LSE Least equares error. LSMA Linear spectral mixture analysis. MVDR Minimum variance distortionless response. NCLS Nonnegatively constrained least squares. NNLS Nonnegative least squares. OSP Orthogonal subspace projection. SCLS Sum-to-one constrained least squares. UCEM Unsupervised constrained energy minimization.

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“…Multicolor mid-infrared imaging is a highly desirable technology in a number of applications such as night vision, missile tracking, medical diagnostics, and environmental monitoring [1]. The current dominant technologies in the field of multispectral imaging rely on the use of either a broadband focal plane array (FPA) with a spinning filter wheel in front of it [2], or a bank of FPAs with a dispersive element such as a grating or prism to separate light of different frequencies.…”

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confidence: 99%

A multispectral and polarization-selective surface-plasmon resonant midinfrared detector

Rosenberg¹,

Shenoi²,

Vandervelde³

et al. 2009

Applied Physics Letters

171

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We demonstrate a multispectral polarization sensitive midinfrared dots-in-a-well photodetector utilizing surface-plasmonic resonant elements, with tailorable frequency response and polarization selectivity. The resonant responsivity of the surface-plasmon detector shows an enhancement of up to five times that of an unpatterned control detector. As the plasmonic resonator involves only surface patterning of the top metal contact, this method is independent of light-absorbing material and can easily be integrated with current focal plane array processing for imaging applications.

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Chemical vapor detection with a multispectral thermal imager

Cited by 21 publications

References 11 publications

Toxic cloud imaging by infrared spectrometry: A scanning FTIR system for identification and visualization

Toxic cloud imaging by infrared spectrometry: A scanning FTIR system for identification and visualization

Constrained subpixel target detection for remotely sensed imagery

A multispectral and polarization-selective surface-plasmon resonant midinfrared detector

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