A Comparison of Detection Strategies for Solids and Organic
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10/10/11 1 A Comparison of Detection Strategies for Solids and Organic Liquids on Surfaces Using Long Wave Infrared Hyperspectral Imaging. Neal B. Gallagher 1 , Thomas A. Blake 2 , James F. Kelly 2 1 Eigenvector Research, Inc., [email protected] 2 Pacific Northwest National Laboratory, Richland, WA USA A Comparison of Detec1on Strategies for Solids and Organic Liquids on Surfaces Using Long Wave Infrared Hyperspectral Imaging. Neal B. Gallagher 1 , Thomas A. Blake 2 , James F. Kelly 2 1 Eigenvector Research, Inc., 160 Gobblers Knob Lane, Manson, WA 98831, USA [email protected] 2 Pacific Northwest NaJonal Laboratory, P.O. Box 999, Mail Stop K888, Richland, WA 99352, USA Passive standoff detecJon of condensed chemical analytes on surfaces is of interest for rapid screening in security, environmental and precision agriculture applicaJons. This paper compares and contrasts the detecJon of solid explosive residues and thin films of liquid organophosphorus compounds on a steel plate using hyperspectral imaging in the long wave infrared at standoff distances of tens of meters. The objecJve is to idenJfy the appropriate modeling approach for detecJon and potenJal classificaJon of both types of compounds in field applicaJons. Of parJcular interest is the use of the matched filter (generalized least squares) esJmator under the assumpJon of “parJal closure” on the esJmated contribuJons. IniJal results indicate that parJal closure is appropriate for detecJon of solids on surfaces but may be less useful for detecJon of thin liquid films.
Transcript of A Comparison of Detection Strategies for Solids and Organic
10/10/11
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A Comparison of Detection Strategies for Solids and Organic Liquids on
Surfaces Using Long Wave Infrared Hyperspectral Imaging.
Neal B. Gallagher1, Thomas A. Blake2, James F. Kelly2
1 Eigenvector Research, Inc., [email protected] 2 Pacific Northwest National Laboratory, Richland, WA
USA
A Comparison of Detec1on Strategies for Solids and Organic Liquids on Surfaces Using Long Wave Infrared Hyperspectral Imaging. Neal B. Gallagher1, Thomas A. Blake2, James F. Kelly2 1 Eigenvector Research, Inc., 160 Gobblers Knob Lane, Manson, WA 98831, USA [email protected] 2 Pacific Northwest NaJonal Laboratory, P.O. Box 999, Mail Stop K8-‐88, Richland, WA 99352, USA Passive standoff detecJon of condensed chemical analytes on surfaces is of interest for rapid screening in security, environmental and precision agriculture applicaJons. This paper compares and contrasts the detecJon of solid explosive residues and thin films of liquid organophosphorus compounds on a steel plate using hyperspectral imaging in the long wave infrared at standoff distances of tens of meters. The objecJve is to idenJfy the appropriate modeling approach for detecJon and potenJal classificaJon of both types of compounds in field applicaJons. Of parJcular interest is the use of the matched filter (generalized least squares) esJmator under the assumpJon of “parJal closure” on the esJmated contribuJons. IniJal results indicate that parJal closure is appropriate for detecJon of solids on surfaces but may be less useful for detecJon of thin liquid films.
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Standoff Detection • Passive standoff detection of condensed chemicals on surfaces
• environmental cleanup, rapid screening for security and precision ag • Traditional lab calibration procedures are not applicable
• measured image and analyte spectrum available • clutter (interferences) highly variable image to image
Experiment and Analysis
• 1 m2 bare metal plates with • solid explosive residues RDX and Tetryl • thin layer of organophosphorous DBP and DBBP • 14, 31 and 50 m at known areal dosage
• Telops, Inc. longwave infrared imager • End-member extraction (exploratory analysis) and • Two generalized least squares (GLS) target detection
approaches • a.k.a., matched filter
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Detection Models
x = xc + cs
The simple additive model assumes that the background is static with Gaussian noise and adding target increases the signal.
The linear mixture model assumes that the background signal is not static and can change as target is added.
x = ccxc + cs x
xc
cs s
x1
x2
x ccxc
cs s
x1
x2
x
ccxc
cs s
x1
x2
x ! xc
If target ‘blocks’ the background, the simple additive model can ‘subtract too much’ of the background signal and there is ‘loss of target information.’ (the thin magenta signal has shorter projection on s).
x
ccxc
cs s
x1
x2
x ! xc
… but how does the signal truly manifest?
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08-Jul-2008 3:51 pm S6_TETRYL_8cm-1_14m_1 _000011476 110 µg/cm2
08-Jul-2008 4:43 pm S6_TETRYL_8cm-1_31m_PASSIVE_1 _000011950 110 µg/cm2
Steel Plate S6
Tetryl
Steel Plate S6
9001000110012000
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Wavenumber (1/cm)
on steel at 14m
on steel at 31m
Example: Tetryl on Steel. At 14 m the signal looks like reflected sky shine. At 31 m the signal looks like reflected black body.
90095010001050110011501200125013000
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Wavenumber (1/cm)
Nor
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TETTET MCR
Comparison of the TET reflectance spectrum and the end-member estimate of the target’s spectrum
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Factor 1: Target
Factor 2: Steel
RMSE
The contributions shows that signal due to the steel plate decreases when target is present.
08-Jul-2008 3:51 pm S6_TETRYL_8cm-1_14m_1 _000011476 110 µg/cm2
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90095010001050110011501200125013000
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RDXRDX MCR
PLATE_S9_RDX_4cm-‐1_14m_1_000011692 90 μg/cm2 Comparison of the RDX reflectance spectrum and the end-member estimate of the target’s spectrum
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Factor 1: Target
Factor 2 (interference?)
Factor 3: Steel
RDX End-Member Contributions The contributions suggest that signal due to the steel plate increases slightly when target is present.
RMSE (Highest residuals are at highest target contributions.)
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Factor 1 referenced to 99% CL for T-‐stat GLS 2 > GLS 1
Factor 2 referenced to 99% CL for T-‐stat decrease in bkg
Q referenced to 99% CL GLS 2 < GLS 1
GLS 2
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GLS 1 GLS 2
Target detection results for TET: GLS 2 performs better than GLS 1
GLS 1 GLS 2
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Target detection results for RDX: GLS 1 performs ~better than GLS 2
Factor 1 referenced to 99% CL for T-‐stat GLS 2 < GLS 1
Factor 2 referenced to 99% CL for T-‐stat ~increase in bkg
Q referenced to 99% CL GLS 2 ~ GLS 1
GLS 1 GLS 2
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MCR GLS Image effect on bkg contributions dist dosage when target target bkg resids chem (m) (μg/cm2) present 2?1 (2 only) (2?1) RDX 14 40 ~> < > ~ RDX 14 90 ~> < ~> ~ RDX 31 40 ~> < > ~ TET 14 110 < > < < TET 31 110 < > < < DBP 14 90 ~> < ~> ~ DBP 30 90 ~> > < < DBBP 14 90 - ~ ~< ~
Summary Table
Conclusions
• When GLS 2 works better (when target blocks background) it works significantly better than GLS 1. • e.g., TET
• When GLS 2 works worse (when target has little effect on background) it works slightly worse. • e.g., RDX. • RDX end-member showed shift of ~ 1000 1/cm peak.
• GLS 2 added information via the background factor that may lead to enhanced detection capability.
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Acknowledgements
• Funding by the Pacific Northwest National Laboratory • Laboratory Directed Research and Development program,
Initiative for Explosives Detection. • Thanks to David Atkinson, Greg Holter, Robert Ewing
and John Hartman at PNNL. • Thanks to Vincent Farley and Jean-Pierre Allard of
Telops, Inc.
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Blake, T.A., Kelly, J.F., Gallagher, N.B, Gassman, P.L., Johnson, T.J., “Passive Detection of Solid Explosives in Mid-IR Hyperspectral Images” Anal. Bioanal. Chem. (2009). DOI 10.1007/s00216-009-2907-5.
Gallagher, N.B., Kelly, J.F., Blake, T.A., "Passive Infrared Hyperspectral Imaging for Standoff Detection of Tetryl Explosive Residue on a Steel Surface," proceedings WHISPERS'10, Reykjavik, Iceland June 14-16 (2010), DOI: 10.1109/WHISPERS.2010.5594839.
