Abstract

Infrared imagery sequences are used for detecting moving targets in the presence of evolving cloud clutter or background noise. This research concentrates on slow-moving point targets that are less than one pixel in size, such as aircraft at long ranges from a sensor. The infrared (IR) imagery sequences that are captured by ground sensors contain an enormous amount of data. Since transmitting this data to a base unit or storing it consumes considerable time and resources, a compression method that maintains the point target detection capabilities is desired. For this purpose, we developed two temporal compression methods that preserve the temporal profile properties of the point target. We evaluated the proposed compression methods using a signal-to-noise-ratio (SNR)-based measure for point target detection and showed that the compression may improve the SNR results compared to the IR sequence prior to compression.

© 2010 Optical Society of America

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References

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  1. I. E. G. Richardson, Video Codec Design (Wiley, 2004), Chaps. 3, 4, 5, 7.
  2. MPEG compression standard, www.mpeg.org.
  3. O. Hadar, R. Shmueli, R. Huber, and M. Huber, “Effects of compression parameters on the perceived quality of video stream over a lossy Internet protocol network,” Opt. Eng. 45, 087003 (2006).
    [CrossRef]
  4. S. Richard, F. Sims, J. A. Mills, and P. N. Topiwala, “Evaluation of video compression for 8 bit and 12 bit IR data with H.264 fidelity range extensions,” Proc. SPIE 5807, 329–340(2005).
    [CrossRef]
  5. N. Vaswani, A. K. Agrawal, Q. Zheng, and R. Chellappa, “Moving object detection and compression in IR sequences,” in Computer Vision Beyond the Visible Spectrum (Springer, 2004), Chap. 5, pp. 141–165.
  6. R. Saran, H. Babu, and A. Kumar, “Median predictor-based lossless video compression algorithm for IR image sequences,” Defence Sci. J. 59, 183–188 (2009).
  7. IR imagery sequences source, formerly located at http://www.sn.afrl.af.mil/pages/SNH/ir_sensor_branch/sequences.html.
  8. L. V. Rozenberg, “Point target tracking in hyperspectral images,” M.S. thesis (Ben-Gurion University of the Negev, 2005).
  9. C. E. Caefer, J. M. Mooney, and J. Silverman, “Point target detection in consecutive frame staring IR imagery with evolving cloud clutter,” Proc. SPIE 2561, 14–24 (1995).
    [CrossRef]
  10. C. E. Caefer, J. Silverman, J. M. Mooney, S. DiSalvo, and R. W. Taylor, “Temporal filtering for point target detection in staring IR imagery: I. Damped sinusoid filters,” Proc. SPIE 3373, 111–122 (1998).
    [CrossRef]
  11. C. E. Caefer, J. Silverman, S. DiSalvo, and R. W. Taylor, “Post-processing of point target detection sinusoidal filters,” Proc. SPlE 4048, 104–111 (2000).
    [CrossRef]
  12. S. R. Rotman, “Spatial and temporal point tracking in real hyperspectral images,” final report for United States Air Force Office of Scientific Research Infrared Imagery grant 033077 (2006).
  13. L. Varsano, I. Yatsker, and S. R. Rotman, “Temporal target tracking in hyperspectral images,” Opt. Eng. 45, 126201(2006).
    [CrossRef]
  14. C. E. Caefer, J. Silverman, and J. M. Mooney, “Optimization of point target tracking filters,” IEEE Trans. Aerosp. Electron. Syst. 36, 15–25 (2000).
    [CrossRef]
  15. S. Chatterjee and A. S. Hadi, “Influential observations, high leverage points, and outliers in linear regression,” Stat. Sci. 1, 379–393 (1986).
    [CrossRef]
  16. J. Silverman, C. E. Caefer, and J. M. Mooney, “Temporal filtering for point target detection in staring IR imagery part II: recursive variance filter,” Proc. SPIE 3373, 44–53(1998).
    [CrossRef]
  17. C. E. Caefer, M. S. Stefanou, E. D. Nielsen, A. P. Rizzuto, O. Raviv, and S. R. Rotman, “Analysis of false alarm distributions in the development and evaluation of hyperspectral point target detection algorithms,” Opt. Eng. 46, 076402(2007).
    [CrossRef]
  18. C. E. Caefer, J. Silverman, O. Orthal, D. Antonelli, Y. Sharoni, and S. R. Rotman, “Improved covariance matrices for point target detection in hyperspectral data,” Opt. Eng. 47, 076402(2008).
    [CrossRef]
  19. C. C. Liebe, “Accuracy performance of star trackers—a tutorial,” IEEE Trans. Aerosp. Electron. Syst. 38, 587–599(2002).
    [CrossRef]
  20. N. Ahmed, T. Natrajan, and K. R. Rao, “Discrete cosine transform,” IEEE Trans. Comput. C-23, 90–93 (1974).
    [CrossRef]
  21. G. Strang, “The discrete cosine transform,” SIAM Rev. 41, 135–147 (1999).
  22. Z. Wang and B. Hunt, “The discrete W-transform,” Appl. Math. Comput. 16, 19–48 (1985).
    [CrossRef]
  23. Vcdemo program, http://www-sipl.technion.ac.il/Info/Downloads_VCDemo_e.shtml.

2009 (1)

R. Saran, H. Babu, and A. Kumar, “Median predictor-based lossless video compression algorithm for IR image sequences,” Defence Sci. J. 59, 183–188 (2009).

2008 (1)

C. E. Caefer, J. Silverman, O. Orthal, D. Antonelli, Y. Sharoni, and S. R. Rotman, “Improved covariance matrices for point target detection in hyperspectral data,” Opt. Eng. 47, 076402(2008).
[CrossRef]

2007 (1)

C. E. Caefer, M. S. Stefanou, E. D. Nielsen, A. P. Rizzuto, O. Raviv, and S. R. Rotman, “Analysis of false alarm distributions in the development and evaluation of hyperspectral point target detection algorithms,” Opt. Eng. 46, 076402(2007).
[CrossRef]

2006 (2)

O. Hadar, R. Shmueli, R. Huber, and M. Huber, “Effects of compression parameters on the perceived quality of video stream over a lossy Internet protocol network,” Opt. Eng. 45, 087003 (2006).
[CrossRef]

L. Varsano, I. Yatsker, and S. R. Rotman, “Temporal target tracking in hyperspectral images,” Opt. Eng. 45, 126201(2006).
[CrossRef]

2005 (1)

S. Richard, F. Sims, J. A. Mills, and P. N. Topiwala, “Evaluation of video compression for 8 bit and 12 bit IR data with H.264 fidelity range extensions,” Proc. SPIE 5807, 329–340(2005).
[CrossRef]

2002 (1)

C. C. Liebe, “Accuracy performance of star trackers—a tutorial,” IEEE Trans. Aerosp. Electron. Syst. 38, 587–599(2002).
[CrossRef]

2000 (2)

C. E. Caefer, J. Silverman, and J. M. Mooney, “Optimization of point target tracking filters,” IEEE Trans. Aerosp. Electron. Syst. 36, 15–25 (2000).
[CrossRef]

C. E. Caefer, J. Silverman, S. DiSalvo, and R. W. Taylor, “Post-processing of point target detection sinusoidal filters,” Proc. SPlE 4048, 104–111 (2000).
[CrossRef]

1998 (2)

C. E. Caefer, J. Silverman, J. M. Mooney, S. DiSalvo, and R. W. Taylor, “Temporal filtering for point target detection in staring IR imagery: I. Damped sinusoid filters,” Proc. SPIE 3373, 111–122 (1998).
[CrossRef]

J. Silverman, C. E. Caefer, and J. M. Mooney, “Temporal filtering for point target detection in staring IR imagery part II: recursive variance filter,” Proc. SPIE 3373, 44–53(1998).
[CrossRef]

1995 (1)

C. E. Caefer, J. M. Mooney, and J. Silverman, “Point target detection in consecutive frame staring IR imagery with evolving cloud clutter,” Proc. SPIE 2561, 14–24 (1995).
[CrossRef]

1986 (1)

S. Chatterjee and A. S. Hadi, “Influential observations, high leverage points, and outliers in linear regression,” Stat. Sci. 1, 379–393 (1986).
[CrossRef]

1985 (1)

Z. Wang and B. Hunt, “The discrete W-transform,” Appl. Math. Comput. 16, 19–48 (1985).
[CrossRef]

1974 (1)

N. Ahmed, T. Natrajan, and K. R. Rao, “Discrete cosine transform,” IEEE Trans. Comput. C-23, 90–93 (1974).
[CrossRef]

Agrawal, A. K.

N. Vaswani, A. K. Agrawal, Q. Zheng, and R. Chellappa, “Moving object detection and compression in IR sequences,” in Computer Vision Beyond the Visible Spectrum (Springer, 2004), Chap. 5, pp. 141–165.

Ahmed, N.

N. Ahmed, T. Natrajan, and K. R. Rao, “Discrete cosine transform,” IEEE Trans. Comput. C-23, 90–93 (1974).
[CrossRef]

Antonelli, D.

C. E. Caefer, J. Silverman, O. Orthal, D. Antonelli, Y. Sharoni, and S. R. Rotman, “Improved covariance matrices for point target detection in hyperspectral data,” Opt. Eng. 47, 076402(2008).
[CrossRef]

Babu, H.

R. Saran, H. Babu, and A. Kumar, “Median predictor-based lossless video compression algorithm for IR image sequences,” Defence Sci. J. 59, 183–188 (2009).

Caefer, C. E.

C. E. Caefer, J. Silverman, O. Orthal, D. Antonelli, Y. Sharoni, and S. R. Rotman, “Improved covariance matrices for point target detection in hyperspectral data,” Opt. Eng. 47, 076402(2008).
[CrossRef]

C. E. Caefer, M. S. Stefanou, E. D. Nielsen, A. P. Rizzuto, O. Raviv, and S. R. Rotman, “Analysis of false alarm distributions in the development and evaluation of hyperspectral point target detection algorithms,” Opt. Eng. 46, 076402(2007).
[CrossRef]

C. E. Caefer, J. Silverman, S. DiSalvo, and R. W. Taylor, “Post-processing of point target detection sinusoidal filters,” Proc. SPlE 4048, 104–111 (2000).
[CrossRef]

C. E. Caefer, J. Silverman, and J. M. Mooney, “Optimization of point target tracking filters,” IEEE Trans. Aerosp. Electron. Syst. 36, 15–25 (2000).
[CrossRef]

C. E. Caefer, J. Silverman, J. M. Mooney, S. DiSalvo, and R. W. Taylor, “Temporal filtering for point target detection in staring IR imagery: I. Damped sinusoid filters,” Proc. SPIE 3373, 111–122 (1998).
[CrossRef]

J. Silverman, C. E. Caefer, and J. M. Mooney, “Temporal filtering for point target detection in staring IR imagery part II: recursive variance filter,” Proc. SPIE 3373, 44–53(1998).
[CrossRef]

C. E. Caefer, J. M. Mooney, and J. Silverman, “Point target detection in consecutive frame staring IR imagery with evolving cloud clutter,” Proc. SPIE 2561, 14–24 (1995).
[CrossRef]

Chatterjee, S.

S. Chatterjee and A. S. Hadi, “Influential observations, high leverage points, and outliers in linear regression,” Stat. Sci. 1, 379–393 (1986).
[CrossRef]

Chellappa, R.

N. Vaswani, A. K. Agrawal, Q. Zheng, and R. Chellappa, “Moving object detection and compression in IR sequences,” in Computer Vision Beyond the Visible Spectrum (Springer, 2004), Chap. 5, pp. 141–165.

DiSalvo, S.

C. E. Caefer, J. Silverman, S. DiSalvo, and R. W. Taylor, “Post-processing of point target detection sinusoidal filters,” Proc. SPlE 4048, 104–111 (2000).
[CrossRef]

C. E. Caefer, J. Silverman, J. M. Mooney, S. DiSalvo, and R. W. Taylor, “Temporal filtering for point target detection in staring IR imagery: I. Damped sinusoid filters,” Proc. SPIE 3373, 111–122 (1998).
[CrossRef]

Hadar, O.

O. Hadar, R. Shmueli, R. Huber, and M. Huber, “Effects of compression parameters on the perceived quality of video stream over a lossy Internet protocol network,” Opt. Eng. 45, 087003 (2006).
[CrossRef]

Hadi, A. S.

S. Chatterjee and A. S. Hadi, “Influential observations, high leverage points, and outliers in linear regression,” Stat. Sci. 1, 379–393 (1986).
[CrossRef]

Huber, M.

O. Hadar, R. Shmueli, R. Huber, and M. Huber, “Effects of compression parameters on the perceived quality of video stream over a lossy Internet protocol network,” Opt. Eng. 45, 087003 (2006).
[CrossRef]

Huber, R.

O. Hadar, R. Shmueli, R. Huber, and M. Huber, “Effects of compression parameters on the perceived quality of video stream over a lossy Internet protocol network,” Opt. Eng. 45, 087003 (2006).
[CrossRef]

Hunt, B.

Z. Wang and B. Hunt, “The discrete W-transform,” Appl. Math. Comput. 16, 19–48 (1985).
[CrossRef]

Kumar, A.

R. Saran, H. Babu, and A. Kumar, “Median predictor-based lossless video compression algorithm for IR image sequences,” Defence Sci. J. 59, 183–188 (2009).

Liebe, C. C.

C. C. Liebe, “Accuracy performance of star trackers—a tutorial,” IEEE Trans. Aerosp. Electron. Syst. 38, 587–599(2002).
[CrossRef]

Mills, J. A.

S. Richard, F. Sims, J. A. Mills, and P. N. Topiwala, “Evaluation of video compression for 8 bit and 12 bit IR data with H.264 fidelity range extensions,” Proc. SPIE 5807, 329–340(2005).
[CrossRef]

Mooney, J. M.

C. E. Caefer, J. Silverman, and J. M. Mooney, “Optimization of point target tracking filters,” IEEE Trans. Aerosp. Electron. Syst. 36, 15–25 (2000).
[CrossRef]

J. Silverman, C. E. Caefer, and J. M. Mooney, “Temporal filtering for point target detection in staring IR imagery part II: recursive variance filter,” Proc. SPIE 3373, 44–53(1998).
[CrossRef]

C. E. Caefer, J. Silverman, J. M. Mooney, S. DiSalvo, and R. W. Taylor, “Temporal filtering for point target detection in staring IR imagery: I. Damped sinusoid filters,” Proc. SPIE 3373, 111–122 (1998).
[CrossRef]

C. E. Caefer, J. M. Mooney, and J. Silverman, “Point target detection in consecutive frame staring IR imagery with evolving cloud clutter,” Proc. SPIE 2561, 14–24 (1995).
[CrossRef]

Natrajan, T.

N. Ahmed, T. Natrajan, and K. R. Rao, “Discrete cosine transform,” IEEE Trans. Comput. C-23, 90–93 (1974).
[CrossRef]

Nielsen, E. D.

C. E. Caefer, M. S. Stefanou, E. D. Nielsen, A. P. Rizzuto, O. Raviv, and S. R. Rotman, “Analysis of false alarm distributions in the development and evaluation of hyperspectral point target detection algorithms,” Opt. Eng. 46, 076402(2007).
[CrossRef]

Orthal, O.

C. E. Caefer, J. Silverman, O. Orthal, D. Antonelli, Y. Sharoni, and S. R. Rotman, “Improved covariance matrices for point target detection in hyperspectral data,” Opt. Eng. 47, 076402(2008).
[CrossRef]

Rao, K. R.

N. Ahmed, T. Natrajan, and K. R. Rao, “Discrete cosine transform,” IEEE Trans. Comput. C-23, 90–93 (1974).
[CrossRef]

Raviv, O.

C. E. Caefer, M. S. Stefanou, E. D. Nielsen, A. P. Rizzuto, O. Raviv, and S. R. Rotman, “Analysis of false alarm distributions in the development and evaluation of hyperspectral point target detection algorithms,” Opt. Eng. 46, 076402(2007).
[CrossRef]

Richard, S.

S. Richard, F. Sims, J. A. Mills, and P. N. Topiwala, “Evaluation of video compression for 8 bit and 12 bit IR data with H.264 fidelity range extensions,” Proc. SPIE 5807, 329–340(2005).
[CrossRef]

Richardson, I. E. G.

I. E. G. Richardson, Video Codec Design (Wiley, 2004), Chaps. 3, 4, 5, 7.

Rizzuto, A. P.

C. E. Caefer, M. S. Stefanou, E. D. Nielsen, A. P. Rizzuto, O. Raviv, and S. R. Rotman, “Analysis of false alarm distributions in the development and evaluation of hyperspectral point target detection algorithms,” Opt. Eng. 46, 076402(2007).
[CrossRef]

Rotman, S. R.

C. E. Caefer, J. Silverman, O. Orthal, D. Antonelli, Y. Sharoni, and S. R. Rotman, “Improved covariance matrices for point target detection in hyperspectral data,” Opt. Eng. 47, 076402(2008).
[CrossRef]

C. E. Caefer, M. S. Stefanou, E. D. Nielsen, A. P. Rizzuto, O. Raviv, and S. R. Rotman, “Analysis of false alarm distributions in the development and evaluation of hyperspectral point target detection algorithms,” Opt. Eng. 46, 076402(2007).
[CrossRef]

L. Varsano, I. Yatsker, and S. R. Rotman, “Temporal target tracking in hyperspectral images,” Opt. Eng. 45, 126201(2006).
[CrossRef]

S. R. Rotman, “Spatial and temporal point tracking in real hyperspectral images,” final report for United States Air Force Office of Scientific Research Infrared Imagery grant 033077 (2006).

Rozenberg, L. V.

L. V. Rozenberg, “Point target tracking in hyperspectral images,” M.S. thesis (Ben-Gurion University of the Negev, 2005).

Saran, R.

R. Saran, H. Babu, and A. Kumar, “Median predictor-based lossless video compression algorithm for IR image sequences,” Defence Sci. J. 59, 183–188 (2009).

Sharoni, Y.

C. E. Caefer, J. Silverman, O. Orthal, D. Antonelli, Y. Sharoni, and S. R. Rotman, “Improved covariance matrices for point target detection in hyperspectral data,” Opt. Eng. 47, 076402(2008).
[CrossRef]

Shmueli, R.

O. Hadar, R. Shmueli, R. Huber, and M. Huber, “Effects of compression parameters on the perceived quality of video stream over a lossy Internet protocol network,” Opt. Eng. 45, 087003 (2006).
[CrossRef]

Silverman, J.

C. E. Caefer, J. Silverman, O. Orthal, D. Antonelli, Y. Sharoni, and S. R. Rotman, “Improved covariance matrices for point target detection in hyperspectral data,” Opt. Eng. 47, 076402(2008).
[CrossRef]

C. E. Caefer, J. Silverman, and J. M. Mooney, “Optimization of point target tracking filters,” IEEE Trans. Aerosp. Electron. Syst. 36, 15–25 (2000).
[CrossRef]

C. E. Caefer, J. Silverman, S. DiSalvo, and R. W. Taylor, “Post-processing of point target detection sinusoidal filters,” Proc. SPlE 4048, 104–111 (2000).
[CrossRef]

C. E. Caefer, J. Silverman, J. M. Mooney, S. DiSalvo, and R. W. Taylor, “Temporal filtering for point target detection in staring IR imagery: I. Damped sinusoid filters,” Proc. SPIE 3373, 111–122 (1998).
[CrossRef]

J. Silverman, C. E. Caefer, and J. M. Mooney, “Temporal filtering for point target detection in staring IR imagery part II: recursive variance filter,” Proc. SPIE 3373, 44–53(1998).
[CrossRef]

C. E. Caefer, J. M. Mooney, and J. Silverman, “Point target detection in consecutive frame staring IR imagery with evolving cloud clutter,” Proc. SPIE 2561, 14–24 (1995).
[CrossRef]

Sims, F.

S. Richard, F. Sims, J. A. Mills, and P. N. Topiwala, “Evaluation of video compression for 8 bit and 12 bit IR data with H.264 fidelity range extensions,” Proc. SPIE 5807, 329–340(2005).
[CrossRef]

Stefanou, M. S.

C. E. Caefer, M. S. Stefanou, E. D. Nielsen, A. P. Rizzuto, O. Raviv, and S. R. Rotman, “Analysis of false alarm distributions in the development and evaluation of hyperspectral point target detection algorithms,” Opt. Eng. 46, 076402(2007).
[CrossRef]

Strang, G.

G. Strang, “The discrete cosine transform,” SIAM Rev. 41, 135–147 (1999).

Taylor, R. W.

C. E. Caefer, J. Silverman, S. DiSalvo, and R. W. Taylor, “Post-processing of point target detection sinusoidal filters,” Proc. SPlE 4048, 104–111 (2000).
[CrossRef]

C. E. Caefer, J. Silverman, J. M. Mooney, S. DiSalvo, and R. W. Taylor, “Temporal filtering for point target detection in staring IR imagery: I. Damped sinusoid filters,” Proc. SPIE 3373, 111–122 (1998).
[CrossRef]

Topiwala, P. N.

S. Richard, F. Sims, J. A. Mills, and P. N. Topiwala, “Evaluation of video compression for 8 bit and 12 bit IR data with H.264 fidelity range extensions,” Proc. SPIE 5807, 329–340(2005).
[CrossRef]

Varsano, L.

L. Varsano, I. Yatsker, and S. R. Rotman, “Temporal target tracking in hyperspectral images,” Opt. Eng. 45, 126201(2006).
[CrossRef]

Vaswani, N.

N. Vaswani, A. K. Agrawal, Q. Zheng, and R. Chellappa, “Moving object detection and compression in IR sequences,” in Computer Vision Beyond the Visible Spectrum (Springer, 2004), Chap. 5, pp. 141–165.

Wang, Z.

Z. Wang and B. Hunt, “The discrete W-transform,” Appl. Math. Comput. 16, 19–48 (1985).
[CrossRef]

Yatsker, I.

L. Varsano, I. Yatsker, and S. R. Rotman, “Temporal target tracking in hyperspectral images,” Opt. Eng. 45, 126201(2006).
[CrossRef]

Zheng, Q.

N. Vaswani, A. K. Agrawal, Q. Zheng, and R. Chellappa, “Moving object detection and compression in IR sequences,” in Computer Vision Beyond the Visible Spectrum (Springer, 2004), Chap. 5, pp. 141–165.

Appl. Math. Comput. (1)

Z. Wang and B. Hunt, “The discrete W-transform,” Appl. Math. Comput. 16, 19–48 (1985).
[CrossRef]

Defence Sci. J. (1)

R. Saran, H. Babu, and A. Kumar, “Median predictor-based lossless video compression algorithm for IR image sequences,” Defence Sci. J. 59, 183–188 (2009).

IEEE Trans. Aerosp. Electron. Syst. (2)

C. E. Caefer, J. Silverman, and J. M. Mooney, “Optimization of point target tracking filters,” IEEE Trans. Aerosp. Electron. Syst. 36, 15–25 (2000).
[CrossRef]

C. C. Liebe, “Accuracy performance of star trackers—a tutorial,” IEEE Trans. Aerosp. Electron. Syst. 38, 587–599(2002).
[CrossRef]

IEEE Trans. Comput. (1)

N. Ahmed, T. Natrajan, and K. R. Rao, “Discrete cosine transform,” IEEE Trans. Comput. C-23, 90–93 (1974).
[CrossRef]

Opt. Eng. (4)

L. Varsano, I. Yatsker, and S. R. Rotman, “Temporal target tracking in hyperspectral images,” Opt. Eng. 45, 126201(2006).
[CrossRef]

C. E. Caefer, M. S. Stefanou, E. D. Nielsen, A. P. Rizzuto, O. Raviv, and S. R. Rotman, “Analysis of false alarm distributions in the development and evaluation of hyperspectral point target detection algorithms,” Opt. Eng. 46, 076402(2007).
[CrossRef]

C. E. Caefer, J. Silverman, O. Orthal, D. Antonelli, Y. Sharoni, and S. R. Rotman, “Improved covariance matrices for point target detection in hyperspectral data,” Opt. Eng. 47, 076402(2008).
[CrossRef]

O. Hadar, R. Shmueli, R. Huber, and M. Huber, “Effects of compression parameters on the perceived quality of video stream over a lossy Internet protocol network,” Opt. Eng. 45, 087003 (2006).
[CrossRef]

Proc. SPIE (4)

S. Richard, F. Sims, J. A. Mills, and P. N. Topiwala, “Evaluation of video compression for 8 bit and 12 bit IR data with H.264 fidelity range extensions,” Proc. SPIE 5807, 329–340(2005).
[CrossRef]

C. E. Caefer, J. M. Mooney, and J. Silverman, “Point target detection in consecutive frame staring IR imagery with evolving cloud clutter,” Proc. SPIE 2561, 14–24 (1995).
[CrossRef]

C. E. Caefer, J. Silverman, J. M. Mooney, S. DiSalvo, and R. W. Taylor, “Temporal filtering for point target detection in staring IR imagery: I. Damped sinusoid filters,” Proc. SPIE 3373, 111–122 (1998).
[CrossRef]

J. Silverman, C. E. Caefer, and J. M. Mooney, “Temporal filtering for point target detection in staring IR imagery part II: recursive variance filter,” Proc. SPIE 3373, 44–53(1998).
[CrossRef]

Proc. SPlE (1)

C. E. Caefer, J. Silverman, S. DiSalvo, and R. W. Taylor, “Post-processing of point target detection sinusoidal filters,” Proc. SPlE 4048, 104–111 (2000).
[CrossRef]

Stat. Sci. (1)

S. Chatterjee and A. S. Hadi, “Influential observations, high leverage points, and outliers in linear regression,” Stat. Sci. 1, 379–393 (1986).
[CrossRef]

Other (8)

IR imagery sequences source, formerly located at http://www.sn.afrl.af.mil/pages/SNH/ir_sensor_branch/sequences.html.

L. V. Rozenberg, “Point target tracking in hyperspectral images,” M.S. thesis (Ben-Gurion University of the Negev, 2005).

S. R. Rotman, “Spatial and temporal point tracking in real hyperspectral images,” final report for United States Air Force Office of Scientific Research Infrared Imagery grant 033077 (2006).

N. Vaswani, A. K. Agrawal, Q. Zheng, and R. Chellappa, “Moving object detection and compression in IR sequences,” in Computer Vision Beyond the Visible Spectrum (Springer, 2004), Chap. 5, pp. 141–165.

I. E. G. Richardson, Video Codec Design (Wiley, 2004), Chaps. 3, 4, 5, 7.

MPEG compression standard, www.mpeg.org.

G. Strang, “The discrete cosine transform,” SIAM Rev. 41, 135–147 (1999).

Vcdemo program, http://www-sipl.technion.ac.il/Info/Downloads_VCDemo_e.shtml.

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Figures (12)

Fig. 1
Fig. 1

Temporal profiles of (a) a target pixel and (b) a noise pixel from the original movie NA23A. The numbers in parentheses are the pixel locations (row, column).

Fig. 2
Fig. 2

Temporal profiles of (a) cloud-edge and (b) cloud pixels from the original movie NA23A. The numbers in parentheses are the pixel locations (row, column).

Fig. 3
Fig. 3

Temporal profiles of target pixels (solid curves) from the original movie NA23A. The circles represent the suspected target elements, and the dotted line represents the median value of the temporal profile. The numbers in parentheses are the pixel locations (row, column).

Fig. 4
Fig. 4

Variance estimation ratio scores (pixel scores) of the original movies: (a) NA23A, (b) NPA, and (c) J13C.

Fig. 5
Fig. 5

Temporal profiles of (a) cloud-edge and (b) cloud pixels from the original movie NA23A, after temporal median filtering and after WGN addition. The number pairs in parentheses are the pixel locations (row, column).

Fig. 6
Fig. 6

(a) Variance estimation ratio scores histogram for movie after median filtering, and (b) variance estimation ratio scores histogram for movie after median filtering and WGN addition.

Fig. 7
Fig. 7

Target temporal profiles of the movie NA23A before and after parabola fit compression and decompression. The numbers in parentheses are the pixel locations (row, column).

Fig. 8
Fig. 8

Noise temporal profiles of the movie NA23A before and after parabola fit compression and decompression. The numbers in parentheses are the pixel locations (row, column).

Fig. 9
Fig. 9

Cloud temporal profiles of the movie NA23A before and after parabola fit compression and decompression. The numbers in parentheses are the pixel locations (row, column).

Fig. 10
Fig. 10

Variance estimation ratio scores of the pixels after parabola fit and decompression of the movies (a) NA23A with WGN 0.5 and (b) J13C with WGN 0.7 (focus on the target area).

Fig. 11
Fig. 11

Variance ratio estimation scores of the pixels of NA23A after DCT quantization when preserving the first (a) ten and (b) eight DCT coefficients, followed by IDCT and WGN 0.5 addition to each temporal profile (focus on the target area).

Fig. 12
Fig. 12

Pixel profiles of NA23A before and after DCT quantization when preserving the first eight DCT coefficients. The numbers in parentheses are the pixel locations (row, column).

Tables (5)

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Table 1 Infrared Imagery Sequence Characteristics

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Table 2 Movie Scores for Original and TMF Movies

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Table 3 Movie Scores for Parabola Fit Compression and Decompression

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Table 4 Movie Scores of Temporal DCT Quantization Compression with Different Parameters and Decompression

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Table 5 MPEG-2 and H.264 Compression Summary, NPA and NA23A

Equations (13)

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W = N L 0 L L 0 + 1 ,
score = σ ^ max 2 1 K i = 1 K σ ˜ i 2 ,
score block ( i ) = E [ v ( i M ) ] E [ v ( i M ) ] std [ v ( i M ) ] ,
score movie = E [ Score block ( i TB ) ] E [ score block ( i TB ) ] std [ score block ( i TB ) ] .
CR = N / L .
y = a 2 · x 2 + a 1 · x + a 0
β = [ a 0 a 1 a 2 ] , X = [ 1 1 1 1 2 2 2 1 N x N x 2 ] , β ^ = [ a ^ 0 a ^ 1 a ^ 2 ] ,
β ^ = ( X T X ) 1 X T y ,
y ^ = X β ^ .
CR _ParabolaFit = K · number_of_frames_original N 1 · ( 8 + 1 ) + N 2 · ( 2 + 1 ) .
quant_DCT_vec = { 1 , 1 k start_point 1 a , start_point k length ( DCT_sequence ) , start_point = p _start_point · length ( DCT_sequence ) + 1 ,
quant_DCT_sequence ( k ) = sign ( DCT_sequence ( k ) ) · | DCT_sequence ( k ) | DCT_quant_vec ( k ) .
CR _DCTquant = K · number_of_frames_original number_of_non_zero_coefficients + K · m .

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