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Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Editor: Joseph N. Mait
  • Vol. 49, Iss. 9 — Mar. 20, 2010
  • pp: 1614–1622

Spectral anomaly detection in deep shadows

Andrey V. Kanaev and Jeremy Murray-Krezan  »View Author Affiliations


Applied Optics, Vol. 49, Issue 9, pp. 1614-1622 (2010)
http://dx.doi.org/10.1364/AO.49.001614


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Abstract

Although several hyperspectral anomaly detection algorithms have proven useful when illumination conditions provide for enough light, many of these same detection algorithms fail to perform well when shadows are also present. To date, no general approach to the problem has been demonstrated. In this paper, a novel hyperspectral anomaly detection algorithm that adapts the dimensionality of the spectral detection subspace to multiple illumination levels is described. The novel detection algorithm is applied to reflectance domain hyperspectral data that represents a variety of illumination conditions: well illuminated and poorly illuminated (i.e., shadowed). Detection results obtained for objects located in deep shadows and light–shadow transition areas suggest superiority of the novel algorithm over standard subspace RX detection.

© 2010 Optical Society of America

OCIS Codes
(100.3010) Image processing : Image reconstruction techniques
(100.4145) Image processing : Motion, hyperspectral image processing
(280.4991) Remote sensing and sensors : Passive remote sensing

ToC Category:
Image Processing

History
Original Manuscript: October 1, 2009
Revised Manuscript: January 28, 2010
Manuscript Accepted: February 3, 2010
Published: March 11, 2010

Citation
Andrey V. Kanaev and Jeremy Murray-Krezan, "Spectral anomaly detection in deep shadows," Appl. Opt. 49, 1614-1622 (2010)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-49-9-1614


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References

  1. M. T. Eismann, J. Meola, A. D. Stocker, S. G. Beaven, and A. P. Schaum, “Airborne hyperspectral detection of small changes,” Appl. Opt. 47, F27-F45 (2008). [CrossRef] [PubMed]
  2. R. Mayer, J. Antoniades, M. Baumback, D. Chester, J. Edwards, A. Goldstein, D. Haas, and S. Henderson, “Shadowed target detection for hyperspectral imagery,” Proc. SPIE 6678, 66780L (2007). [CrossRef]
  3. S. M. Adler-Golden, R. Y. Levine, M. W. Matthew, S. C. Richtsmeier, L. S. Bernstein, J. Gruninger, G. Felde, M. Hoke, G. P. Anderson, and A. Ratkowski, “Shadow-insensitive material detection/classification with atmospherically corrected hyperspectral imagery,” Proc. SPIE 4381, 460-469 (2001). [CrossRef]
  4. E. A. Ashton, B. D. Wemett, R. A. Leathers, and T. V. Downes, “A novel method for illumination suppression in hyperspectral images,” Proc. SPIE 6966, 69660C (2008). [CrossRef]
  5. M. J. Carlotto, “A cluster based approach for detecting man-made objects and changes in imagery,” IEEE Trans. Geosci. Remote Sens. 43, 374-387 (2005). [CrossRef]
  6. D. W. J. Stein, S. G. Beaven, L. E. Hoff, Edwin M. Winter, A. P. Schaum, and A. D. Stocker, “Anomaly detection from hyperspectral imagery,” IEEE Signal Process. Mag. 19, 58-69(2002). [CrossRef]
  7. P. C. Hytle, R. C. Hardie, M. T. Eismann, and J. Meola, “Anomaly detection in hyperspectral imagery: comparison of methods using diurnal and seasonal data,” J. Appl. Remote Sens. 3, 033546 (2009). [CrossRef]
  8. A. Schaum and A. D. Stocker, “Hyperspectral change detection and supervised matched filtering based on covariance equalization,” Proc. SPIE 5425, 77-90 (2004). [CrossRef]
  9. A. Schaum, “Joint subspace detection of hyperspectral targets,” in IEEE Aerospace Conference Proceedings (IEEE, 2004), Vol. 3, pp. 1818-1824.
  10. A. Schaum, “Autonomous hyperspectral target detection with quasi-stationarity violation at background boundaries,” in 35th Applied Imagery and Pattern Recognition Workshop (IEEE, 2006). [CrossRef]
  11. T. N. Pappas, “An adaptive clustering algorithm for image segmentation,” IEEE Trans. Signal Process. 40, 901-914 (1992). [CrossRef]
  12. M. Sezgin and B. Sankur, “Survey over image thresholding techniques and quantitative performance evaluation,” J. Electron. Imaging 13,146-165 (2004) and references therein. [CrossRef]
  13. E. Ensafi and A. D. Stocker, “An adaptive CFAR algorithm for real-time hyperspectral target detection,” Proc. SPIE 6966, 696605 (2008). [CrossRef]
  14. A. V. Kanaev, E. Allman, and J. Murray-Krezan, “Reduction of false alarms caused by background boundaries in real time subspace RX anomaly detection,” Proc. SPIE 7334, 733405(2009). [CrossRef]

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