OSA's Digital Library

Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Editor: James C. Wyant
  • Vol. 46, Iss. 25 — Sep. 1, 2007
  • pp: 6368–6374

Detection of gaseous plumes in IR hyperspectral images using hierarchical clustering

Eitan Hirsch and Eyal Agassi  »View Author Affiliations


Applied Optics, Vol. 46, Issue 25, pp. 6368-6374 (2007)
http://dx.doi.org/10.1364/AO.46.006368


View Full Text Article

Enhanced HTML    Acrobat PDF (2767 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

The emergence of IR hyperspectral sensors in recent years enables their use in remote environmental monitoring of gaseous plumes. IR hyperspectral imaging combines the unique advantages of traditional remote sensing methods such as multispectral imagery and nonimaging Fourier transform infrared spectroscopy, while eliminating their drawbacks. The most significant improvement introduced by hyperspectral technology is the capability of standoff detection and discrimination of effluent gaseous plumes without need for a clear reference background or any other temporal information. We introduce a novel approach for detection and discrimination of gaseous plumes in IR hyperspectral imagery using a divisive hierarchical clustering algorithm. The utility of the suggested detection algorithm is demonstrated on IR hyperspectral images of the release of two atmospheric tracers. The application of the proposed detection method on the experimental data has yielded a correct identification of all the releases without any false alarms. These encouraging results show that the presented approach can be used as a basis for a complete identification algorithm for gaseous pollutants in IR hyperspectral imagery without the need for a clear background.

© 2007 Optical Society of America

OCIS Codes
(100.5010) Image processing : Pattern recognition
(280.1120) Remote sensing and sensors : Air pollution monitoring

ToC Category:
Remote Sensing and Sensors

History
Original Manuscript: March 20, 2007
Revised Manuscript: June 29, 2007
Manuscript Accepted: July 2, 2007
Published: August 27, 2007

Citation
Eitan Hirsch and Eyal Agassi, "Detection of gaseous plumes in IR hyperspectral images using hierarchical clustering," Appl. Opt. 46, 6368-6374 (2007)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-46-25-6368


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. D. F. Flanigan, "Vapor-detection sensitivity as a function of spectral resolution for a single Lorentzian band," Appl. Opt. 34, 2636-2639 (1995).
  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).
  3. 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).
  4. 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).
  5. S. J. Young, "Detection and quantification of gases in industrial-stack plumes using thermal-infrared hyperspectral imaging," Tech. Rep. ATR-2002(8407)-1 (The Aerospace Corporation, 2002).
  6. D. W. Messinger, "Gaseous plume detection in hyperspectral images: a comparison of methods," Proc. SPIE 5425, 592-603 (2004). [CrossRef]
  7. J. S. McGonigle, C. L. Thomson, V. I. Tsanev, and C. Oppenheimer, "A simple technique for measuring power station SO2 and NO2 emissions," Atmos. Environ. 38, 21-25 (2001). [CrossRef]
  8. R. C. Carlson, A. F. Hayden, and W. B. Telfair, "Remote observations of effluents from small building smokestacks using FTIR spectroscopy," Appl. Opt. 27, 4952-4959 (1988).
  9. J. Sandsten, H. Edner, and S. Svanberg, "Gas visualization of industrial hydrocarbon emissions," Opt. Express 12, 1443-1451 (2004). [CrossRef]
  10. R. Harig, G. Matz, P., Rusch, H. H. Gerhard, and J. H. Gerhard, "New scanning infrared gas imaging system (SIGIS 2) for emergency response," Proc. SPIE 5995, 174-181 (2006).
  11. E. Agassi and E. Hirsch, "Remote detection of SF6 plumes in a stable boundary layer," Proc. SPIE 5988, 131-141 (2006).
  12. L. Grenier, G. Pelous, and P. Adam, "Passive stand-off detection of gas clouds in open field by IR imagery," Proc. SPIE 3553, 86-92 (1998).
  13. Y. Guern, L. Grenier, and F. Carpentier, "Uncooled IRFPA for low-cost multispectral/hyperspectral LWIR imaging device," Proc. SPIE 5093, 126-135 (2003). [CrossRef]
  14. M. Hinnrichs and B. Piatek, "Hand held hyperspectral imager for chemical/biological and environmental applications," Proc. SPIE 5270, 10-18 (2003). [CrossRef]
  15. V. Farley, A. Vallières, M. Chamberland, and J. Legault, "Performance of the FIRST, a longwave infrared hyperspectral imaging sensor," Proc. SPIE 6398 (2006).
  16. W. J. Marinelli, C. M. Gittins, A. H. Gelb, and B. D. Green, "A tunable Fabry-Perot etalon-based long-wave infrared imaging spectroradiometer," Appl. Opt. 38, 2594-2604 (2000).
  17. J. C. Harsanyi and C.-I. Chang, "Hyperspectral image classification and dimensionality reduction: an orthogonal subspace projection," IEEE Trans. Geosci. Remote Sens. 32, 794-795 (1994).
  18. R. T. Behrens and L. L. Scharf, "Signal processing applications of oblique projection operators," IEEE Trans. Signal Process. 42, 1413-1424 (1994). [CrossRef]
  19. A. Iffarraguerri and C. Gittins, "Chemical cloud mapping and identification using convex cone analysis of AIRIS multispectral imaging data," Proc. SPIE 3533, 114-121 (1998). [CrossRef]
  20. T. Burr and B. R. Foy, "Characterizing clutter in the context of detecting weak gaseous plumes in hyperspectral imagery," Sensors 6, 1587-1615 (2006).
  21. T. Burr and N. Hegarther, "Overview of physical models and statistical approaches for weak gaseous plume detection using passive infrared hyperspectral imagery," Sensors 6, 1721-1750 (2006).
  22. J. Theiler, B. R. Foy, and A. M. Fraser, "Nonlinear signal contamination effects for gaseous plume detection in hyperspectral imagery," Proc. SPIE 6233, 62331U (2006). [CrossRef]
  23. J. Theiler, B. R. Foy, and A. M. Fraser, "Characterizing non-Gaussian clutter and detecting weak gaseous plumes in hyperspectral imagery," Proc. SPIE 5806, 182-193 (2005). [CrossRef]
  24. J. Theiler and B. R. Foy, "The effect of signal contamination in matched-filter detection of the signal on a cluttered background," IEEE Geosci. Remote Sens. Lett. 3, 98-102 (2006). [CrossRef]
  25. C. T. Funk, J. Theiler, D. A. Roberts, and C. C. Boerl, "Clustering to improve matched filter detection of weak gas plumes in hyperspectral thermal imagery," IEEE Trans. Geosci. Remote Sens. 39, 1410-1420 (2001). [CrossRef]
  26. A. Beil, R. Daum, R. Harig, and G. Matz, "Remote sensing of atmospheric pollution by passive FTIR spectroscopy," Proc. SPIE 3493, 32-43 (1998). [CrossRef]
  27. R. Duda, P. Hart, and D. Stork, Pattern Classification, 2nd ed. (Wiley, 2001).
  28. B. Jahne, Digital Image Processing (Springer-Verlag, 1997).

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.


« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited