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Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Editor: Joseph N. Mait
  • Vol. 52, Iss. 8 — Mar. 10, 2013
  • pp: 1646–1654

Compression of infrared imagery sequences containing a slow-moving point target, part II

Revital Huber-Shalem, Ofer Hadar, Stanley R. Rotman, and Merav Huber-Lerner  »View Author Affiliations


Applied Optics, Vol. 52, Issue 8, pp. 1646-1654 (2013)
http://dx.doi.org/10.1364/AO.52.001646


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Abstract

Infrared (IR) imagery sequences are commonly 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. Because transmitting IR imagery sequences to a base unit or storing them consumes considerable time and resources, a compression method that maintains the point-target detection capabilities is highly desirable. In our previous work, we introduced two temporal compression methods that preserve the temporal profile properties of the point target in the form of discrete cosine transform (DCT) quantization and parabola fit. In the present work, we extend the compression task method of DCT quantization by applying spatial compression over the temporally compressed coefficients, which is followed by bit encoding. We evaluate the proposed compression method using a signal-to-noise ratio (SNR)–based measure for point target detection and find that it yields better results than the compression standard H.264. Furthermore, we introduce an automatic detection algorithm that extracts the target location from the SNR scores image, which is acquired during the evaluation process and has a probability of detection and a probability of false alarm close to those of the original sequences. We previously determined that it is necessary to establish a minimal noise level in the SNR-based measure to compensate for smoothing that is induced by the compression. Here, the noise level calculation process is modified in order to allow detection of targets traversing all background types.

© 2013 Optical Society of America

OCIS Codes
(100.2000) Image processing : Digital image processing
(100.4999) Image processing : Pattern recognition, target tracking

ToC Category:
Image Processing

History
Original Manuscript: November 2, 2012
Revised Manuscript: January 26, 2013
Manuscript Accepted: February 7, 2013
Published: March 7, 2013

Citation
Revital Huber-Shalem, Ofer Hadar, Stanley R. Rotman, and Merav Huber-Lerner, "Compression of infrared imagery sequences containing a slow-moving point target, part II," Appl. Opt. 52, 1646-1654 (2013)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-52-8-1646


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References

  1. R. Huber-Shalem, O. Hadar, S. R. Rotman, and M. Huber-Lerner, “Compression of infrared imagery sequences containing a slow moving point target,” Appl. Opt. 49, 3798–3813 (2010). [CrossRef]
  2. M. Bar-Tal and S. R. Rotman, “Performance measurement in point source target detection,” Infrared Phys. Technol. 37, 231–238 (1996). [CrossRef]
  3. S. Richard, F. Sims, J. A. Mills, and P. N. Topiwala, “Evaluation of video compression for 8 and 12 bit IR data with H.264 fidelity range extensions,” Proc. SPIE 5807, 329–340 (2005). [CrossRef]
  4. 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.
  5. R. Saran, H. Babu, and A. Kumar, “Median predictor-based lossless video compression algorithm for IR image sequences,” Def. Sci. J. 59, 183–188 (2009). [CrossRef]
  6. I. E. G. Richardson, Video Codec Design (Wiley, 2004), Chaps. 3, 4, 5, 7.
  7. http://www.sn.afrl.af.mil/pages/SNH/ir_sensor_branch/sequences.html .
  8. L. Varsano Rozenberg, “Point target tracking in hyperspectral images,” M. Sc. thesis, Ben-Gurion University of the Negev, June2005.
  9. C. E. Caefer, J. M. Mooney, and J. Silverman, “Point target detection in consecutive frame staring IR imagery with evolving cloud clutter,” Opt. Eng. 34, 2772–2784 (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 part 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. L. Varsano, I. Yatsker, and S. R. Rotman, “Temporal target tracking in hyperspectral images,” Opt. Eng. 45, 126201 (2006). [CrossRef]
  13. 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]
  14. O. Nichtern and S. R. Rotman, “Parameter adjustment for a dynamic programming track-before-detect-based target detection algorithm,” EURASIP J. Appl. Signal Process. 2008, 146925 (2008). [CrossRef]
  15. B. Aminov, O. Nichtern, and S. R. Rotman, “Spatial and temporal point tracking in real hyperspectral images,” EURASIP J. Appl. Signal Process. 2011, 30 (2011). [CrossRef]
  16. N. Ahmed, T. Natrajan, and K. R. Rao, “Discrete cosine transform,” IEEE Trans. Comput. C-23, 90–93 (1974). [CrossRef]
  17. G. Strang, “The discrete cosine transform,” SIAM Rev. 41, 135–147 (1999). [CrossRef]
  18. Z. Wang and B. Hunt, “The discrete W-transform,” Appl. Math. Comput. 16, 19–48 (1985). [CrossRef]
  19. D. A. Huffman, “A method for the construction of minimum-redundancy codes,” Proc. Inst. Radio Eng. 40, 1098–1101 (1952). [CrossRef]
  20. S. P. Lloyd, “Least squares quantization in PCM,” IEEE Trans. Inf. Theory 28(2), 129–137 (1982). [CrossRef]
  21. J. Max, “Quantizing for minimum distortion,” IRE Trans. Inf. Theory 6(1), 7–12 (1960). [CrossRef]
  22. R. Succary, A. Cohen, P. Yaractzi, and S. R. Rotman, “A dynamic programming algorithm for point target detection: practical parameters for DPA,” Proc. SPIE 4473, 96–100 (2001). [CrossRef]
  23. Y. Bar-Shalom and T. E. Fortmann, Tracking and Data Association, Mathematics in Science and Engineering (Academic, 1988), Vol. 179, Chap. 4, pp. 123–149.

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