OSA's Digital Library

Applied Optics

Applied Optics


  • Editor: Jospeh N. Mait
  • Vol. 48, Iss. 3 — Jan. 20, 2009
  • pp: 464–476

Automatic target detection in forward-looking infrared imagery via probabilistic neural networks

Jesmin F. Khan, Mohammad S. Alam, and Sharif M. A. Bhuiyan  »View Author Affiliations

Applied Optics, Vol. 48, Issue 3, pp. 464-476 (2009)

View Full Text Article

Enhanced HTML    Acrobat PDF (1139 KB)

Browse Journals / Lookup Meetings

Browse by Journal and Year


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools



This paper presents a technique for automatic detection of the targets in forward-looking infrared (FLIR) imagery. Mathematical morphology is applied for the preliminary selection of possible regions of interest (ROI). An efficient clutter rejecter module based on probabilistic neural network is proposed, which is trained by using both target and background features to ensure excellent classification performance by moving the ROI in several directions with respect to the center of the detected target patch. Experimental results using real-life FLIR imagery confirm the excellent performance of the detector and the effectiveness of the proposed clutter rejecter module.

© 2009 Optical Society of America

OCIS Codes
(040.2480) Detectors : FLIR, forward-looking infrared
(100.4996) Image processing : Pattern recognition, neural networks

ToC Category:

Original Manuscript: May 30, 2008
Revised Manuscript: November 28, 2008
Manuscript Accepted: December 7, 2008
Published: January 12, 2009

Jesmin F. Khan, Mohammad S. Alam, and Sharif M. A. Bhuiyan, "Automatic target detection in forward-looking infrared imagery via probabilistic neural networks," Appl. Opt. 48, 464-476 (2009)

Sort:  Author  |  Year  |  Journal  |  Reset  


  1. B. Bhanu, “Automatic target recognition: state of the art survey,” IEEE Trans. Aerosp. Electron. Syst. AES-22, 364-379(1986). [CrossRef]
  2. A. Mahalanobis, “Correlation filters for object tracking target re-acquisition and smart aimpoint selection,” Proc. SPIE 3073, 25-32 (1997). [CrossRef]
  3. A. Bal and M. S. Alam, “Dynamic target tracking using fringe-adjusted joint transform correlation and template matching,” Appl. Opt. 43, 4874-4881 (2004). [CrossRef] [PubMed]
  4. M. S. Alam and A. Bal, “Improved multiple target tracking via global motion compensation and optoelectronic correlation,” IEEE Trans. Ind. Electron. 54, 522-529 (2007). [CrossRef]
  5. A. Dawoud, M. S. Alam, A. Bal, and C. Loo, “Target tracking in infrared imagery using weighted composite reference function-based decision fusion,” IEEE Trans. Image Process. 15, 404-410 (2006). [CrossRef]
  6. A. Bal and M. S. Alam, “Automatic target tracking in FLIR image sequences using intensity variation function and template modeling,” IEEE Trans. Instrum. Meas. 54, 1846-1852 (2005). [CrossRef]
  7. S. R. F. Sims and A. Mahalanobis, “Performance evaluation of quadratic correlation filters for target detection and discrimination in infrared imagery,” Opt. Eng. 43, 1705-1711 (2004). [CrossRef]
  8. D. Casasent and R. Shenoy, “Feature space trajectory for distorted object classification and pose estimation in SAR,” Opt. Eng. 36, 2719-2728 (1997). [CrossRef]
  9. B. Bhanu and J. Ahn, “A system for model-based recognition of articulated objects,” in Proceedings of the International Conference on Pattern Recognition (IEEE, 1998), Vol. 2, pp. 1812-1815.
  10. S. Z. Der, Q. Zheng, R. Chellappa, B. Redman, and H. Mahmoud, “View based recognition of military vehicles in LADAR imagery using CAD model matching,” in Image Recognition and Classification, Algorithms, Systems and Applications, B. Javidi, ed. (Marcel Dekker, 2002), pp. 151-187.
  11. A. D. Lanterman, M. I. Miller, and D. L. Snyder, “Automatic target recognition via the simulation of infrared scenes,” in Proceedings of the 6th Annual Ground Target Modeling and Validation Conference (Keweenaw Research Center, Michigan Tech. University, 1995), pp. 195-204.
  12. T. R. Crimmins and W. M. Brown, “Image algebra and automatic shape recognition,” IEEE Trans. Aerosp. Electron. Syst. AES-21, 60-69 (1985). [CrossRef]
  13. L. G. Shapiro, R. S. MacDonald, and S. R. Sternberg, “Shape recognition with mathematical morphology,” in Proc. 8th Int. Conference on Pattern Recognition, Paris, France, 27-31 October 1986.
  14. F. Y. Shih and O. R. Mitchell, “Automated fast recognition and location of arbitrarily shaped objects by image morphology,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 1988), pp. 774-779.
  15. V. Tom and T. Joo, “Morphological detection for scanning IRST sensor,” Final Report TR-1167-90-1 (Atlantic Aerospace Electronics Corporation, 1990).
  16. V. Tom and T. Joo, “Morphological-based front-end processing for IR-based ATR systems,” Final Report (Atlantic Aerospace Electronics Corporation, 1992).
  17. M. W. Roth, “Survey of neural network technology for automatic target recognition,” IEEE Trans. Neural Netw. 1, 28-43(1990). [CrossRef] [PubMed]
  18. K. W. Przytula and D. Thompson, “Evaluation of neural networks for automatic target recognition,” in Proc. IEEE Conf. Aerospace (IEEE, 1997), Vol 3, pp. 423-439.
  19. A. L. Chan, S. Z. Der, and N. M. Nasarabadi, “Multistage infrared target detection,” Opt. Eng. 42, 2746-2754 (2003). [CrossRef]
  20. S. Z. Der, A. L. Chan, N. M. Nasarabadi, and H. Kwon, “Automated vehicle detection in forward-looking infrared imagery,” Appl. Opt. 43, 333-348 (2004). [CrossRef] [PubMed]
  21. A. L. Chan, S. A. Rizvi, and N. M. Nasarabadi, “Dualband FLIR for automatic target recognition,” Information Fusion 4, 35-45 (2003). [CrossRef]
  22. D. Borghys, P. Verlinde, C. Perneel, and M. Acheroy, “Multilevel data fusion for the detection of targets using multispectral image sequences,” Opt. Eng. 37, 477-484 (1998). [CrossRef]
  23. W. Lie-Chan, S. Z. Der, and N. M. Nasarabadi, “Automatic target recognition using feature-decomposition and data-decomposition modular neural network,” IEEE Trans. Image Process. 7, 1113-1121 (1998). [CrossRef]
  24. B. Ernisse, S. K. Rogers, M. P. DeSimio, and R. A. Ranies, “Complete automatic target cuer/recognition system for tactical forward-looking infrared images,” Opt. Eng. 36, 2593-2603 (1997). [CrossRef]
  25. J. Waldemark, V. Becanovic, T. Lindblad, and C. S. Lindsey, “Hybrid neural networks for automatic target recognition,” in Systems, Man, and Cybernetics, IEEE Int. Conf. Computational Cybernetics and Simulation (IEEE, 1997), Vol. 4, pp. 4016-4021. [CrossRef]
  26. S. Rogers, J. Colombi, C. Martin, J. Gainey, K. Fielding, T. Burns, D. Ruck, M. Kabrisky, and M. Oxley, “Neural networks for automatic target recognition,” Neural Networks 8, 1153-1184 (1995). [CrossRef]
  27. G. A. Carpenter, “Neural network models for pattern recognition and associative memory,” Neural Networks 2, 243-257 (1989). [CrossRef]
  28. I. E. Dror, M. Zagaeski, and C. F. Moss, “Three-dimensional target recognition via sonar: a neural network model,” Neural Networks 8, 149-160 (1995). [CrossRef]
  29. A. Ravichandran and B. Yegnanarayana, “Studies on object recognition from degraded images using neural networks,” Neural Networks 8, 481-488 (1995). [CrossRef]
  30. B. Bhanu and T. Jones, “Image understanding research for automatic target recognition,” in IEEE Trans. Aerospace and Electronic Systems Magazine (IEEE, 1993), pp. 15-22. [CrossRef]
  31. S. A. Rizvi and N. M. Nasarabadi, “A modular clutter rejection technique for FLIR imagery using region based principal component analysis,” Pattern Recogn. 35, 2895-2904 (2002). [CrossRef]
  32. S. A. Rizvi and N. M. Nasarabadi, “Fusion of FLIR automatic target recognition algorithms,” Information Fusion 4, 247-258(2003). [CrossRef]
  33. J. F. Khan and M. S. Alam, “Target detection in cluttered forward-looking infrared imagery,” Opt. Eng. 44, 076404 (2005). [CrossRef]
  34. R. C. Gonzalez and R. E. Woods, Digital Image Processing (Addison-Wesley, 1992).
  35. T. C. Wang and N. B. Karayiannis, “Detection of microcalcifications in digital mammograms using wavelets,” IEEE Trans. Med. Imaging 17 (1998). [CrossRef] [PubMed]
  36. I. Daubechies, Ten Lectures on Wavelets (SIAM, 1992). [CrossRef]
  37. C. K. Chui, An Introduction to Wavelets (Academic, 1992).
  38. G. Strang and T. Nguyen, Wavelets and Filter Banks (Wellesley Cambridge, 1996).
  39. D. Davies, P. Palmer, and M. Mirmehdi, “Detection and tracking of very small low contrast objects,” in Proceedings of the 9th British Machine Vision Conference (BMVA, 1998), pp. 599-608.
  40. I. Daubechies, “Orthonormal bases of compactly supported wavelets,” Commun. Pure Appl. Math. 41, 909-996 (1988). [CrossRef]
  41. K. Messer, D. de Ridder, and J. Kittler, “Adaptive texture representation methods for automatic target recognition,” in Proceedings of the 10th British Machine Vision Conference (BMVA, 1999), pp. 443-452.
  42. D. F. Specht, “Probabilistic neural networks,” Neural Networks 3, 109-118 (1990). [CrossRef]
  43. G. S. Gill and J. S. Sohal, “Battlefield decision making: a neural network approach,” J. Theoret. Appl. Inform. Technol. 4, 697-699 (2008).
  44. T. Sando, “Modeling highway crashes using Bayesisn belief networks and GIS,” Ph.D dissertation (Florida State University, 2005).

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