Abstract

Infrared remote detection of chemical and biological agents in a complex environment depends on the ability to recognize threat signatures and differentiate them from the signatures of innocuous materials. In this paper, we addressed the methods of producing the constraint spectra needed to ensure reliable operation in a meteorologically changing environment. We collected arrays of background spectra of ground, woods, and low-angle sky on an irregular basis over a period of a year. Based on the hypothesis that the concentration fluctuations of species in the sensor's field of view can be exploited to form signatures, the standard deviations of the array (the result is characteristic of all fluctuations) and the difference array (the result is characteristic of sensor fluctuations) were computed. Subtracting these two spectra and filtering the result produced a spectrum, which is a measure of the IR fluctuations in the scene. The resulting set of scene spectra were processed into aberrant noise, and deterministic groups by numerical filtering and statistical methods.

© 2006 Optical Society of America

Full Article  |  PDF Article

References

  • View by:
  • |
  • |
  • |

  1. D. F. Flanigan, "Prediction of the limits of detection of hazardous vapors by passive infrared with the use of MODTRAN," Appl Opt 35, 6090-6098 (1996).
    [CrossRef] [PubMed]
  2. D. F. Flanigan and H. P. DeLong, "Spectral absorption characteristics of the major components of dust clouds," Appl. Opt 10, 51-56 (1971).
    [CrossRef] [PubMed]
  3. H. Walter, Jr. and D. Flanigan, "Detection of atmospheric pollutants," Appl. Opt. 14, 1423-1428 (1975).
    [CrossRef] [PubMed]
  4. E. N. Webb, H. A. Walter, and D. Flanigan, "Spectral classification techniques for remote sensing alarms," ARCSL-TR-77054 (U.S. Army Armament Research and Development Command, Aberdeen Proving Ground, Md., 1977).
  5. S. E. Carpenter and G. W. Small, "Selection of optimum training sets for use in pattern recognition analysis of chemical data," Anal. Chim. Acta 249, 305-321 (1991).
    [CrossRef]
  6. M. L. Polak, J. L. Hall, and K. C. Herr, "Passive Fourier-transform infrared spectroscopy of chemical plumes: an algorithm for quantitative interpretation and real-time background removal," Appl Opt 34, 5406-5412 (1995).
    [CrossRef] [PubMed]
  7. T. F. Kaltenbach and G. W. Small, "Ridge regression techniques for the optimization of piecewise linear discriminants: Application to Fourier transform infrared remote sensing measurements," Analy. Chim. Acta 279, 309-322 (1993).
    [CrossRef]
  8. A. Hayden, E. Niple, and B. Boyce, "Determination of trace-gas amounts in plumes by the use of orthogonal digital filtering of thermal-emission spectra," Appl. Opt. 35, 2802-2809 (1996).
    [CrossRef] [PubMed]
  9. A. C. Samuels, D. F. Flanigan, and A. Ben-David, "Analysis of noise in passive Fourier transform infrared measurements of some representative backgrounds as a function of meteorological conditions," The International Symposium on Optical Science and Technology, Conference 5159, SPIE's 48th Annual Meeting, 3-8 August 2003, San Diego. Calif.
  10. D. F. Flanigan, A. C. Samuels, and A. Ben-David, "Noise assessment of a Fourier transform infrared spectroradiometer subject to the stability of a conventional laboratory blackbody source," Appl. Opt. 43, 2767-2776 (2004).
    [CrossRef] [PubMed]
  11. D. F. Flanigan, "Hazardous cloud imaging: a new way of using passive infrared," Appl. Opt. 36, 7027-7036 (1997).
    [CrossRef]
  12. D. W. Hoock, "Modeling time dependent obscuration for simulated imaging of dust and smoke clouds," in Characterization, Propagation, and Simulation of Sources and Backgrounds, W. R. Watkins and D. Clement, eds., Proc. SPIE 1486, 164-175 (1991).
    [CrossRef]
  13. S. L. Marple, Jr., Digital Spectral Analysis with Applications (Prentice Hall, 1987), p. 116.
  14. R. Beer, Remote Sensing by Fourier Transform Spectrometry, (Wiley, 1992), p. 68.
  15. R. J. Combs, "Thermal stability evaluation for passive FTIR spectrometry," Field Anal. Chem. Technol. 3, 81-94 (1999).
    [CrossRef]

2004 (1)

1999 (1)

R. J. Combs, "Thermal stability evaluation for passive FTIR spectrometry," Field Anal. Chem. Technol. 3, 81-94 (1999).
[CrossRef]

1997 (1)

1996 (2)

A. Hayden, E. Niple, and B. Boyce, "Determination of trace-gas amounts in plumes by the use of orthogonal digital filtering of thermal-emission spectra," Appl. Opt. 35, 2802-2809 (1996).
[CrossRef] [PubMed]

D. F. Flanigan, "Prediction of the limits of detection of hazardous vapors by passive infrared with the use of MODTRAN," Appl Opt 35, 6090-6098 (1996).
[CrossRef] [PubMed]

1995 (1)

M. L. Polak, J. L. Hall, and K. C. Herr, "Passive Fourier-transform infrared spectroscopy of chemical plumes: an algorithm for quantitative interpretation and real-time background removal," Appl Opt 34, 5406-5412 (1995).
[CrossRef] [PubMed]

1993 (1)

T. F. Kaltenbach and G. W. Small, "Ridge regression techniques for the optimization of piecewise linear discriminants: Application to Fourier transform infrared remote sensing measurements," Analy. Chim. Acta 279, 309-322 (1993).
[CrossRef]

1991 (2)

S. E. Carpenter and G. W. Small, "Selection of optimum training sets for use in pattern recognition analysis of chemical data," Anal. Chim. Acta 249, 305-321 (1991).
[CrossRef]

D. W. Hoock, "Modeling time dependent obscuration for simulated imaging of dust and smoke clouds," in Characterization, Propagation, and Simulation of Sources and Backgrounds, W. R. Watkins and D. Clement, eds., Proc. SPIE 1486, 164-175 (1991).
[CrossRef]

1975 (1)

1971 (1)

D. F. Flanigan and H. P. DeLong, "Spectral absorption characteristics of the major components of dust clouds," Appl. Opt 10, 51-56 (1971).
[CrossRef] [PubMed]

Beer, R.

R. Beer, Remote Sensing by Fourier Transform Spectrometry, (Wiley, 1992), p. 68.

Ben-David, A.

D. F. Flanigan, A. C. Samuels, and A. Ben-David, "Noise assessment of a Fourier transform infrared spectroradiometer subject to the stability of a conventional laboratory blackbody source," Appl. Opt. 43, 2767-2776 (2004).
[CrossRef] [PubMed]

A. C. Samuels, D. F. Flanigan, and A. Ben-David, "Analysis of noise in passive Fourier transform infrared measurements of some representative backgrounds as a function of meteorological conditions," The International Symposium on Optical Science and Technology, Conference 5159, SPIE's 48th Annual Meeting, 3-8 August 2003, San Diego. Calif.

Boyce, B.

Carpenter, S. E.

S. E. Carpenter and G. W. Small, "Selection of optimum training sets for use in pattern recognition analysis of chemical data," Anal. Chim. Acta 249, 305-321 (1991).
[CrossRef]

Combs, R. J.

R. J. Combs, "Thermal stability evaluation for passive FTIR spectrometry," Field Anal. Chem. Technol. 3, 81-94 (1999).
[CrossRef]

DeLong, H. P.

D. F. Flanigan and H. P. DeLong, "Spectral absorption characteristics of the major components of dust clouds," Appl. Opt 10, 51-56 (1971).
[CrossRef] [PubMed]

Flanigan, D.

H. Walter, Jr. and D. Flanigan, "Detection of atmospheric pollutants," Appl. Opt. 14, 1423-1428 (1975).
[CrossRef] [PubMed]

E. N. Webb, H. A. Walter, and D. Flanigan, "Spectral classification techniques for remote sensing alarms," ARCSL-TR-77054 (U.S. Army Armament Research and Development Command, Aberdeen Proving Ground, Md., 1977).

Flanigan, D. F.

D. F. Flanigan, A. C. Samuels, and A. Ben-David, "Noise assessment of a Fourier transform infrared spectroradiometer subject to the stability of a conventional laboratory blackbody source," Appl. Opt. 43, 2767-2776 (2004).
[CrossRef] [PubMed]

D. F. Flanigan, "Hazardous cloud imaging: a new way of using passive infrared," Appl. Opt. 36, 7027-7036 (1997).
[CrossRef]

D. F. Flanigan, "Prediction of the limits of detection of hazardous vapors by passive infrared with the use of MODTRAN," Appl Opt 35, 6090-6098 (1996).
[CrossRef] [PubMed]

D. F. Flanigan and H. P. DeLong, "Spectral absorption characteristics of the major components of dust clouds," Appl. Opt 10, 51-56 (1971).
[CrossRef] [PubMed]

A. C. Samuels, D. F. Flanigan, and A. Ben-David, "Analysis of noise in passive Fourier transform infrared measurements of some representative backgrounds as a function of meteorological conditions," The International Symposium on Optical Science and Technology, Conference 5159, SPIE's 48th Annual Meeting, 3-8 August 2003, San Diego. Calif.

Hall, J. L.

M. L. Polak, J. L. Hall, and K. C. Herr, "Passive Fourier-transform infrared spectroscopy of chemical plumes: an algorithm for quantitative interpretation and real-time background removal," Appl Opt 34, 5406-5412 (1995).
[CrossRef] [PubMed]

Hayden, A.

Herr, K. C.

M. L. Polak, J. L. Hall, and K. C. Herr, "Passive Fourier-transform infrared spectroscopy of chemical plumes: an algorithm for quantitative interpretation and real-time background removal," Appl Opt 34, 5406-5412 (1995).
[CrossRef] [PubMed]

Hoock, D. W.

D. W. Hoock, "Modeling time dependent obscuration for simulated imaging of dust and smoke clouds," in Characterization, Propagation, and Simulation of Sources and Backgrounds, W. R. Watkins and D. Clement, eds., Proc. SPIE 1486, 164-175 (1991).
[CrossRef]

Kaltenbach, T. F.

T. F. Kaltenbach and G. W. Small, "Ridge regression techniques for the optimization of piecewise linear discriminants: Application to Fourier transform infrared remote sensing measurements," Analy. Chim. Acta 279, 309-322 (1993).
[CrossRef]

Marple, S. L.

S. L. Marple, Jr., Digital Spectral Analysis with Applications (Prentice Hall, 1987), p. 116.

Niple, E.

Polak, M. L.

M. L. Polak, J. L. Hall, and K. C. Herr, "Passive Fourier-transform infrared spectroscopy of chemical plumes: an algorithm for quantitative interpretation and real-time background removal," Appl Opt 34, 5406-5412 (1995).
[CrossRef] [PubMed]

Samuels, A. C.

D. F. Flanigan, A. C. Samuels, and A. Ben-David, "Noise assessment of a Fourier transform infrared spectroradiometer subject to the stability of a conventional laboratory blackbody source," Appl. Opt. 43, 2767-2776 (2004).
[CrossRef] [PubMed]

A. C. Samuels, D. F. Flanigan, and A. Ben-David, "Analysis of noise in passive Fourier transform infrared measurements of some representative backgrounds as a function of meteorological conditions," The International Symposium on Optical Science and Technology, Conference 5159, SPIE's 48th Annual Meeting, 3-8 August 2003, San Diego. Calif.

Small, G. W.

T. F. Kaltenbach and G. W. Small, "Ridge regression techniques for the optimization of piecewise linear discriminants: Application to Fourier transform infrared remote sensing measurements," Analy. Chim. Acta 279, 309-322 (1993).
[CrossRef]

S. E. Carpenter and G. W. Small, "Selection of optimum training sets for use in pattern recognition analysis of chemical data," Anal. Chim. Acta 249, 305-321 (1991).
[CrossRef]

Walter, H.

Walter, H. A.

E. N. Webb, H. A. Walter, and D. Flanigan, "Spectral classification techniques for remote sensing alarms," ARCSL-TR-77054 (U.S. Army Armament Research and Development Command, Aberdeen Proving Ground, Md., 1977).

Webb, E. N.

E. N. Webb, H. A. Walter, and D. Flanigan, "Spectral classification techniques for remote sensing alarms," ARCSL-TR-77054 (U.S. Army Armament Research and Development Command, Aberdeen Proving Ground, Md., 1977).

Anal. Chim. Acta (1)

S. E. Carpenter and G. W. Small, "Selection of optimum training sets for use in pattern recognition analysis of chemical data," Anal. Chim. Acta 249, 305-321 (1991).
[CrossRef]

Analy. Chim. Acta (1)

T. F. Kaltenbach and G. W. Small, "Ridge regression techniques for the optimization of piecewise linear discriminants: Application to Fourier transform infrared remote sensing measurements," Analy. Chim. Acta 279, 309-322 (1993).
[CrossRef]

Appl Opt (2)

D. F. Flanigan, "Prediction of the limits of detection of hazardous vapors by passive infrared with the use of MODTRAN," Appl Opt 35, 6090-6098 (1996).
[CrossRef] [PubMed]

M. L. Polak, J. L. Hall, and K. C. Herr, "Passive Fourier-transform infrared spectroscopy of chemical plumes: an algorithm for quantitative interpretation and real-time background removal," Appl Opt 34, 5406-5412 (1995).
[CrossRef] [PubMed]

Appl. Opt (1)

D. F. Flanigan and H. P. DeLong, "Spectral absorption characteristics of the major components of dust clouds," Appl. Opt 10, 51-56 (1971).
[CrossRef] [PubMed]

Appl. Opt. (4)

Field Anal. Chem. Technol. (1)

R. J. Combs, "Thermal stability evaluation for passive FTIR spectrometry," Field Anal. Chem. Technol. 3, 81-94 (1999).
[CrossRef]

Proc. SPIE (1)

D. W. Hoock, "Modeling time dependent obscuration for simulated imaging of dust and smoke clouds," in Characterization, Propagation, and Simulation of Sources and Backgrounds, W. R. Watkins and D. Clement, eds., Proc. SPIE 1486, 164-175 (1991).
[CrossRef]

Other (4)

S. L. Marple, Jr., Digital Spectral Analysis with Applications (Prentice Hall, 1987), p. 116.

R. Beer, Remote Sensing by Fourier Transform Spectrometry, (Wiley, 1992), p. 68.

A. C. Samuels, D. F. Flanigan, and A. Ben-David, "Analysis of noise in passive Fourier transform infrared measurements of some representative backgrounds as a function of meteorological conditions," The International Symposium on Optical Science and Technology, Conference 5159, SPIE's 48th Annual Meeting, 3-8 August 2003, San Diego. Calif.

E. N. Webb, H. A. Walter, and D. Flanigan, "Spectral classification techniques for remote sensing alarms," ARCSL-TR-77054 (U.S. Army Armament Research and Development Command, Aberdeen Proving Ground, Md., 1977).

Cited By

OSA participates in CrossRef's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.

Alert me when this article is cited.


Figures (13)

Fig. 1
Fig. 1

IR image of the scene used to gather data: ground data from the lower grass-covered portion of the scene, woods data from the tree line (about 100 m away) in the middle of the scene, and low-angle sky data from the upper portion of the scene.

Fig. 2
Fig. 2

(a) Expanded view of the mean STSR and NESR for the entire sequence of tests. (b) Expanded view of the mean STSR, NESR, and SSSR for the entire sequence of tests.

Fig. 3
Fig. 3

NESR, STSR, and SSSR for the 24 October 2002, woods2 data computed by direct subtraction (std) and variance subtraction (var).

Fig. 4
Fig. 4

Demonstration of flattening the SSSR signature by subtracting a fourth-order polynomial fit to the low fluctuating scene (021024\woods2).

Fig. 5
Fig. 5

NESR, STSR, and SSSR for the 021021\sky1 data computed by direct subtraction (std) and variance subtraction (var).

Fig. 6
Fig. 6

Demonstration of flattening the SSSR signature by subtracting a fourth-order polynomial fit to highly fluctuating data (021021∕sky1).

Fig. 7
Fig. 7

Profile of the signal (top figure) based on the mean and standard deviation of each scan. The bottom figure is the same behavior for the difference signal (adjacent scans subtracted).

Fig. 8
Fig. 8

Distribution correlation coefficients of unflattened SSSR spectra.

Fig. 9
Fig. 9

Distribution correlation coefficients of flattened SSSR spectra.

Fig. 10
Fig. 10

Standard deviation of SSSR and flattened SSSR spectra.

Fig. 11
Fig. 11

Distributions of the standard deviations of SSSR spectra (left) and correlation coefficients (right) of low SSSR group.

Fig. 12
Fig. 12

Distributions of the standard deviations of SSSR spectra (left) and correlation coefficients (right) for the high SSSR group.

Fig. 13
Fig. 13

The top figure shows two selected 4 cm 1 resolution constraint spectra; the bottom figure shows the standard deviation of the five 4 cm 1 resolution constraint spectra.

Tables (4)

Tables Icon

Table 1 Scan Time and Total Measurement Time for 500 Scans

Tables Icon

Table 2 Mean NESR from Nine Calibration Sets and the Mean STSR, NESR, and Standard Deviation of SSSR Values for Three Special Data Sets a

Tables Icon

Table 3 SSSR Boundary and Separation Results for All Resolutions a

Tables Icon

Table 4 Summary of Results for the Formation of a Constraint Database

Equations (7)

Equations on this page are rendered with MathJax. Learn more.

SSSR = STSR NESR .
STSR = σ S t s     1 / 2 ,
NESR = σ Δ B ( t s 2 ) 1 / 2 ,
NESR = σ ΔS ( t s 2 ) 1 / 2
R ( i , j ) = C ( i , j ) [ C ( i , i ) C ( j , j ) ] 1 / 2 ,
f std = STSR ¯ NESR ¯ STSR ¯ 100
f var = ( STSR ¯ 2 NESR ¯ 2 ) 1 / 2 STSR ¯ 100 ,

Metrics