OSA's Digital Library

Optics Express

Optics Express

  • Editor: C. Martijn de Sterke
  • Vol. 17, Iss. 20 — Sep. 28, 2009
  • pp: 17391–17411

Decision Boundaries in Two Dimensions for Target Detection in Hyperspectral Imagery

Bernard R. Foy, James Theiler, and Andrew M. Fraser  »View Author Affiliations

Optics Express, Vol. 17, Issue 20, pp. 17391-17411 (2009)

View Full Text Article

Enhanced HTML    Acrobat PDF (693 KB)

Browse Journals / Lookup Meetings

Browse by Journal and Year


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools



We present an approach to the problems of weak plume detection and sub-pixel target detection in hyperspectral imagery that operates in a two-dimensional space. In this space, one axis is a matched-filter projection of the data and the other axis is the magnitude of the residual after matched-filter subtraction. Although it is only two-dimensional, this space is rich enough to include several well-known signal detection algorithms, including the adaptive matched filter, the adaptive coherence estimator, and the finite-target matched filter. Because this space is only two-dimensional, adaptive machine learning methods can produce new plume detectors without being stymied by the curse of dimensionality. We investigate, in particular, the utility of the support vector machine for learning boundaries in this matched-filter-residual space, and compare the performance of the resulting nonlinearly adaptive detector to well-known alternatives.

© 2009 Optical Society of America

OCIS Codes
(280.0280) Remote sensing and sensors : Remote sensing and sensors
(280.1545) Remote sensing and sensors : Chemical analysis
(110.4234) Imaging systems : Multispectral and hyperspectral imaging
(280.4991) Remote sensing and sensors : Passive remote sensing

ToC Category:
Remote Sensing and Sensors

Original Manuscript: June 1, 2009
Revised Manuscript: September 4, 2009
Manuscript Accepted: September 5, 2009
Published: September 15, 2009

Bernard R. Foy, James Theiler, and Andrew M. Fraser, "Decision boundaries in two dimensions for target detection in hyperspectral imagery," Opt. Express 17, 17391-17411 (2009)

Sort:  Author  |  Year  |  Journal  |  Reset  


  1. J. A. Richards and X. Jia, Remote Sensing Digital Image Analysis (Springer, New York, 2006). [CrossRef]
  2. J. B. Adams and A. R. Gillespie, Remote Sensing of Landscapes with Spectral Images (Cambridge Univ. Press, New York, 2006). [CrossRef]
  3. 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, 2803-2809 (1996). [CrossRef]
  4. D. Manolakis, L. Jairam, D. Zhang, and M. Rossacci, "Statistical models for LWIR hyperspectral backgrounds and their applications in chemical agent detection," Proc. SPIE 6565, 656525-1, (2007). [CrossRef]
  5. A. Schaum and A. Stocker, "Spectrally-selective target detection," in Proc. Intl. Symp. Spectral Sens. Research, San Diego, B. A. Mandel, ed., p. 23. Available at http://leupold.gis.usu.edu/docs/protected/procs/isssr/1997/.
  6. L. Kirkland, K. Herr, E. Keim, P. Adams, J. Salisbury, J. Hackwell, and A. Treiman, "First use of an airborne thermal infrared hyperspectral scanner for compositional mapping," Remote Sens. Environ. 80, 447-59 (2002). [CrossRef]
  7. I. S. Reed, J. D. Mallett, and L. E. Brennan, "Rapid convergence rate in adaptive arrays," IEEE Trans. Aerosp. Electron. Syst. 10, 853-863 (1974). [CrossRef]
  8. S. Kay, Fundamentals of Statistical Signal Processing, Volume 2: Detection Theory (Prentice Hall, NJ, 1998).
  9. S. Kraut, L. L. Scharf, and L. T. McWhorter, "Adaptive subspace detectors," IEEE Trans. Signal Process. 49, 1-16 (2001). [CrossRef]
  10. L. L. Scharf, Statistical Signal Processing (Addison-Wesley, Reading, MA, 1990).
  11. J. Schott, Remote Sensing: the Image Chain Approach (Oxford University Press, New York, 1997).
  12. N. B. Gallagher, B. M. Wise, and D. M. Sheen, "Estimation of trace vapor concentration-pathlength in plumes for remote sensing applications from hyperspectral images," Analytica Chimica Acta 490, 139-152 (2003). [CrossRef]
  13. J. Theiler and B. R. Foy, "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]
  14. 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).
  15. 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]
  16. D. Manolakis, D. Marden, J. Kerekes, and G. Shaw, "On the Statistics of Hyperspectral Imaging Data," Proc. SPIE 4381, 308-316 (2001). [CrossRef]
  17. B. R. Foy, R. R. Petrin, C. R. Quick, T. Shimada, and J. J. Tiee, "Comparisons between hyperspectral passive and multispectral active sensor measurements." Proc. SPIE 4722, 98-109 (2002). [CrossRef]
  18. D. Manolakis, "Taxonomy of detection algorithms for hyperspectral imaging applications," Opt. Eng. 44, 066403 (2005). [CrossRef]
  19. J. Theiler, B. R. Foy, and A. M. Fraser, "Nonlinear signal contamination effects for gaseous plume detection in hyperspectral imagery," Proc. SPIE 6233, 62331U-1 (2006).
  20. S. J. Young, B. R. Johnson, and J. A. Hackwell, "An in-scene method for atmospheric compensation of thermal hyperspectral data," J. Geophys. Res. Atm. 107, 4774 (2002). [CrossRef]
  21. D. Morgan, "Spectral absorption pattern detection and estimation. I. Analytical techniques," Appl. Spectrosc. 31, 404-415 (1977). [CrossRef]
  22. D. Morgan, "Spectral absorption pattern detection and estimation. II. System applications and design procedures," Appl. Spectrosc. 31, 415-424 (1977). [CrossRef]
  23. J. Theiler, B. Foy, and A. Fraser, "Beyond the adaptive matched filter: nonlinear detectors for weak signals in high-dimensional clutter," Proc. SPIE 6565, 656503-1 (2007).
  24. J. Theiler and B. Foy, "EC-GLRT: Detecting weak plumes in non-Gaussian hyperspectral clutter using an elliptically-contoured generalized likelihood ratio test," Proc. IEEE Intl. Geosci. Remote Sensing Symp. (2008).
  25. D. B. Marden and D. Manolakis, "Using elliptically contoured distributions to model hyperspectral imaging data and generate statistically similar synthetic data," Proc. SPIE 5425, 558-572 (2004). [CrossRef]
  26. B. Scholkopf and A. J. Smola, Learning with Kernels (MIT Press, Cambridge, MA, 2002).
  27. C.-C. Chang and C.-J. Lin, LIBSVM: a library for support vector machines (2001). Software available at www.csie.ntu.edu.tw/?cjlin/libsvm.
  28. T. J. Cudahy, J. Wilson, R. Hewson, P. Linton, P. Harris, M. Sears, K. Okada, and J. A. Hackwell, "Mapping variations in plagioclase felspar mineralogy using airborne hyperspectral TIR imaging data," in Proc. IEEE 2001 Intl. Geosci. Remote Sensing Symp., Sydney, Australia, T. Milne and B. Cechet, eds., pp. 730-732 (IEEE, Piscataway, NJ, 2001).
  29. G. Vane, R. O. Green, T. G. Chrien, H. T. Enmark, E. G. Hansen, and W. M. Porter, "The Airborne Visible/ Infrared Imaging Spectrometer (AVIRIS)," Remote Sens. Environ. 44, 127-143 (1993). [CrossRef]
  30. AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) Free Standard Data Products, Jet Propulsion Laboratory (JPL), National Aeronautics and Space Administration (NASA), http://aviris.jpl.nasa.gov/html/aviris.freedata.html.
  31. M. E. Winter, "N-FINDR: an algorithm for fast autonomous spectral end-member determination in hyperspectral data," Proc. SPIE 3753, 266-275 (1999). [CrossRef]
  32. A. M. Mood, F. A. Graybill, and D. C. Boes, Introduction to the Theory of Statistics, 3rd ed. (McGraw-Hill, New York, 1974).
  33. A. Schaum, "Hyperspectral Target Detection using a Bayesian Likelihood Ratio Test," Proc. IEEE Aerospace Conf. 3, 1537-1540 (2002).
  34. A. C. Rencher, Linear Models in Statistics (Wiley, New York, 2000).
  35. N. R. Draper and H. Smith, Applied Regression Analysis (Wiley, New York, 1998).
  36. F. C. Robey, D. R. Fuhrmann, E. J. Kelly, and R. Nitzberg, "A CFAR adaptive matched filter detector," IEEE Trans. Aerosp. Electron. Syst. 28, 208-216 (1992). [CrossRef]
  37. C. Lee and D. A. Landgrebe, "Analyzing high-dimensional multispectral data," IEEE Trans. Geosci. Remote Sens. 31, 792-800 (1993). [CrossRef]
  38. P. V. Villeneuve, H. A. Fry, J. Theiler, B. W. Smith, and A. D. Stocker, "Improved matched-filter detection techniques," Proc. SPIE 3753, 278-285 (1999). [CrossRef]

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