Skip to main content
Log in

Criterion to Evaluate the Quality of Infrared Small Target Images

  • Published:
Journal of Infrared, Millimeter, and Terahertz Waves Aims and scope Submit manuscript

Abstract

In this paper, we propose a new criterion to estimate the quality of infrared small target images. To describe the criterion quantitatively, two indicators are defined. One is the “degree of target being confused” that represents the ability of infrared small target image to provide fake targets. The other one is the “degree of target being shielded”, which reflects the contribution of the image to shield the target. Experimental results reveal that this criterion is more robust than the traditional method (Signal-to-Noise Ratio). It is not only valid to infrared small target images which Signal-to-Noise Ratio could correctly describe, but also to the images that the traditional criterion could not accurately estimate. In addition, the results of this criterion can provide information about the cause of background interfering with target detection.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Y. L. Wang, and J. M. Dai, Moving targets detection and tracking based on nonlinear adaptive filtering. Proc. of the 2007 International Conference on Computational Intelligence and Security Workshops, Washington, DC, USA, 691–694 (2007).

  2. S. Gao, and P.-L. Shui, Method for moving point target detection in image sequences based on directional cumulation. Proc. of SPIE 6795, 67952I-1–67952I-6 (2007).

    Google Scholar 

  3. S.-M. Wang, J.-H. Han, and W. Wang, Wavelet de-noising based on high-order-statistics for infrared target detection. Proc. of SPIE 6790, 67903W-1–67903W-4 (2007).

    Google Scholar 

  4. E. Rich et al., Single-frame image processing technique for low-SNR infrared imagery. Proc. of SPIE 6940, 69402G-1–69402G-12 (2008).

    Google Scholar 

  5. L. Yang, Y. Zhou, J. Yang, and L. Chen, Variance WIE based infrared images processing. Electron. Lett. 42(15), 857–859 (2006).

    Article  Google Scholar 

  6. Y.-L. Zou, G.-Y. Wang, and L. Zhang, Fast small offshore target detection based on object region characteristic. Acta Automatica Sinica 31(3), 427–433 (2005).

    Google Scholar 

  7. Y. Xiong et al., An extended track-before-detect algorithm for infrared target detection. IEEE Trans. Aerosp. Electron. Syst. 33(3), 1087–1092 (1997).

    Article  Google Scholar 

  8. P.F. Singer, and D.M. Sasaki, Analysis of the cascade of track-before-detect and track-after-detect tracking algorithm. Proc. of SPIE. 3373, 156–165 (1998).

    Article  Google Scholar 

  9. L. Yang, Study on infrared small target detection and tracking algorithm under complex backgrounds. PhD thesis, Institute of Image Processing and Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai China, 2006, pp. 6–7.

  10. J. Xu, Research on the detection of small and dim targets in infrared images. PhD thesis, Xi dian University, Xi’an China, 2001.

  11. D. Yonoviz, Tunable wavelet target extraction preprocessor. Proc. of SPIE 6569(65690A), 1–12 (2007).

    Google Scholar 

  12. N. Andrew, Image characterization and target recognition the surf zone environment. Proc. of SPIE, 2765, 46–58 (1996).

    Article  Google Scholar 

  13. Q.-P. Zhao, The research of infrared image preprocessing and small target detection under complex background. Masters thesis, Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai China, 2007.

  14. Y. Lei, Y. Jie, and L. Jiangguo, New criterion to evaluate the complex degree of sea-sky infrared background. Opt. Eng. 44(12), 1–5 (2005).

    Google Scholar 

  15. P.A. Ffrench, J.H. Zeidler, and W.H. Ku, Enhanced detectability of small objects in correlated clutter using an improved 2-D adaptivelattice algorithm. IEEE Trans. Image Process. 6(3), 383–397 (1997).

    Article  Google Scholar 

  16. W. Sun, and L.-Z. Xia, Infrared target segmentation algorithm based on morphological method. J. Infrared Millim. Waves 23(3), 233–236 (2004).

    Google Scholar 

  17. L. Yang, J. Yang, and K. Yang, Adaptive detection for infrared small target under sea-sky complex background. Electron. Lett. 40(17), 1803–1805 (2004).

    Article  Google Scholar 

  18. J. Barnett, Statistical analysis of median subtraction filtering with application to point target detection in infrared backgrounds. Proc. of SPIE 1050, 10–18 (1989).

    Google Scholar 

  19. L.M. Kaplan, Small target detection in clutter using recursive nonlinear prediction. IEEE Trans. on Aerospace and Electronic Systems 36(2), 713–717 (2000).

    Article  Google Scholar 

  20. A. Mahalanobis, R. Muise, S. Stanfill et al., Design and application of quadratic correlation filters for target detection. IEEE Trans. on Aerospace and Electronic Systems 40(3), 837–850 (2004).

    Article  Google Scholar 

  21. R. Liu, E. Liu, J. Yang, T. Zhang, and Y. Cao, Point target detection of infrared images with eigentargets. OE Lett. 46(11), 501–503 (2007).

    Google Scholar 

  22. N. Otsu, A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 919–926 (1979).

    MathSciNet  Google Scholar 

  23. H.-Y. Wang, D.-L. Pan, and D.-S. Xia, A fast algorithm for two-dimensional Otsu adaptive threshold algorithm. Acta Automatica Sinica 33(9), 968–971 (2007).

    MathSciNet  Google Scholar 

Download references

Acknowledgements

This work was supported by the Aviation Science Foundation of China (NO. 20070112001).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Wei-he Diao.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Mao, X., Diao, Wh. Criterion to Evaluate the Quality of Infrared Small Target Images. J Infrared Milli Terahz Waves 30, 56–64 (2009). https://doi.org/10.1007/s10762-008-9410-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10762-008-9410-5

Keywords

Navigation