Overview of Physical Models and Statistical Approaches for Weak Gaseous Plume Detection using Passive Infrared Hyperspectral Imagery

Burr, Tom; Hengartner, Nicolas W.

doi:10.3390/s6121721

Cited by 37 publications

(57 citation statements)

References 46 publications

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“…where A(ν) is the absorbance coefficient of the gas at wavenumber ν and has units of inverse ppm-m. For optically thin plumes [2], this term is well-approximated by a Taylor series expansion to the linear term, i.e.,…”

Section: Radiance Modelmentioning

confidence: 99%

“…Hyperspectral images may contain on-plume pixels (pixels that have a gas plume that influences the signal) and off-plume pixels (pixels that do not have a gas plume influencing the signal) [2]. Using Equation (6) as a guide, the at-sensor observed radiance for an on-plume pixel, indexed i, can be modeled as:…”

Section: An Application To Hyperspectral Imagerymentioning

confidence: 99%

“…In order to gain insight into how background conditions affect the at-sensor observed plume signal, the three-layer physics-based radiance model is explored [2,4,6]. This model can be written as…”

Section: Radiance Modelmentioning

confidence: 99%

“…In the field of hyperspectral imagery, the characterization of gaseous plumes often includes the following three main tasks: detection, identification, and quantification [3]. Techniques for the characterization of gaseous plumes have been studied extensively [2,3,4,8].…”

Section: Introductionmentioning

confidence: 99%

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Statistics for the Relative Detectability of Chemicals in Weak Gaseous Plumes in LWIR Hyperspectral Imagery

Metoyer¹,

Walsh²,

Tardiff³

et al. 2008

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Executive SummaryThe detection and identification of weak gaseous plumes using thermal imaging data is complicated by many factors. These include variability due to atmosphere, ground and plume temperature, and background clutter. This report presents an analysis of one formulation of the physics-based model that describes the at-sensor observed radiance. The motivating question for the analyses performed in this report is as follows. Given a set of backgrounds, is there a way to predict the background over which the probability of detecting a given chemical will be the highest? Two statistics were developed to address this question. These statistics incorporate data from the long-wave infrared band to predict the background over which chemical detectability will be the highest. These statistics can be computed prior to data collection. As a preliminary exploration into the predictive ability of these statistics, analyses were performed on synthetic hyperspectral images. Each image contained one chemical (either carbon tetrachloride or ammonia) spread across six distinct background types. The statistics were used to generate predictions for the background ranks. Then, the predicted ranks were compared to the empirical ranks obtained from the analyses of the synthetic images. For the simplified images under consideration, the predicted and empirical ranks showed a promising amount of agreement. One statistic accurately predicted the best and worst background for detection in all of the images. Future work may include explorations of more complicated plume ingredients, background types, and noise structures.iii

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Section: Radiance Modelmentioning

confidence: 99%

Section: An Application To Hyperspectral Imagerymentioning

confidence: 99%

Section: Radiance Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Statistics for the Relative Detectability of Chemicals in Weak Gaseous Plumes in LWIR Hyperspectral Imagery

Metoyer¹,

Walsh²,

Tardiff³

et al. 2008

View full text Add to dashboard Cite

show abstract

“…His group performed many measurement campaigns with excellent results, mainly by ensuring proper modelling of the scene and by taking into account the signature of the instrument used. Other groups developed similar approaches, however limiting their study to optically thin plumes [3,4], to dedicated instruments performing optical subtraction [5,6], or introducing Bayesian algorithms to maximize the use of a priori information [7]. On the other hand, detection and quantification activities with an imaging Fourier-transform spectrometer (IFTS) have been first presented in [8].…”

Section: Introductionmentioning

confidence: 99%

Infrared Imaging Fourier Transform Spectrometer as the Stand-Off Gas Detection System

Kastek

Piątkowski²,

Trzaskawka³

2011

Metrology and Measurement Systems

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The article presents the detection of gases using an infrared imaging Fourier-transform spectrometer (IFTS). The Telops company has developed the IFTS instrument HyperCam, which is offered as a short-or long-wave infrared device. The principle of HyperCam operation and methodology of gas detection has been shown in the paper, as well as theoretical evaluation of gas detection possibility. Calculations of the optical path between the IFTS device, cloud of gases and background have been also discussed. The variation of a signal reaching the IFTS caused by the presence of a gas has been calculated and compared with the reference signal obtained without the presence of a gas in IFTS's field of view. Verification of the theoretical result has been made by laboratory measurements. Some results of the detection of various types of gases has been also included in the paper.

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Decompositions using maximum signal factors

Gallagher

Shaver

Bishop

et al. 2014

Journal of Chemometrics

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Maximum autocorrelation factors (MAF) and whitened principal components analysis are gaining popularity as tools for exploratory analysis of hyperspectral images. This paper shows that the two approaches are mathematically identical when signal and noise (clutter) are defined similarly. It also shows that the MAF metaphor can be generalized to encompass a wide variety of signal processing objectives referred to generically as maximum signal factors while retaining the interpretability of principal components analysis. A subspace projection approximation of the data prior to decomposition is also introduced, which reduces computational memory requirements. For the hyperspectral images studied, it was demonstrated to bring more signal of interest into the first factor as compared with the approach that did not use the subspace approximation. Also, it is expected to significantly reduce the number of scores images needed to be inspected during exploratory analysis. Copyright © 2014 John Wiley & Sons, Ltd.

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Overview of Physical Models and Statistical Approaches for Weak Gaseous Plume Detection using Passive Infrared Hyperspectral Imagery

Cited by 37 publications

References 46 publications

Statistics for the Relative Detectability of Chemicals in Weak Gaseous Plumes in LWIR Hyperspectral Imagery

Statistics for the Relative Detectability of Chemicals in Weak Gaseous Plumes in LWIR Hyperspectral Imagery

Infrared Imaging Fourier Transform Spectrometer as the Stand-Off Gas Detection System

Decompositions using maximum signal factors

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