Image preprocessing is commonly used in infrared (IR) small target detection to suppress background clutter and enhance target signature. To evaluate the performance of preprocessing algorithms, two performance metrics, namely PFTN (potential false targets number) decline ratio and BRI (background relative intensity) decline ratio are developed in this paper. The proposed metrics evaluate the performance of given preprocessing algorithm by comparing the qualities of input and output images. The new performance metrics are based on the theories of PFTN and BRI, which describe the quality of IR small target image, by representing the difficulty degree of target detection. Theoretical analysis and experimental results show that the proposed performance metrics can accurately reflect the effect of the image preprocessing stage on reducing false alarms and target shielding. Compared to the traditional metrics, such as signal-to-noise ratio gain and background suppression factor, the new ones are more intuitive and valid.
2. Crowgey, B. R., E. J. Rothwell, L. C. Kempel, and E. L. Mokole, "Comparison of UWB short-pulse and stepped-frequency radar systems for imaging through barriers," Progress In Electromagnetics Research, Vol. 110, 403-419, 2010.
3. Tsai, H. C., "Investigation into time- and frequency-domain EMI-induced noise in bistable multivibrator," Progress In Electromagnetics Research, Vol. 100, 327-349, 2010.
4. Maskooki, A., E. Gunawan, C. B. Soh, and K. S. Low, "Frequency domain skin artifact removal method for ultra-wideband breast cancer detection," Progress In Electromagnetics Research, Vol. 98, 299-314, 2009.
5. Crowgey, B. R., E. J. Rothwell, L. C. Kempel, and E. L. Mokole, "Comparison of UWB short-pulse and stepped-frequency radar systems for imaging through barriers," Progress In Electromagnetics Research, Vol. 110, 403-419, 2010.
6. Ffrench, P. A., J. R. Zeidler, and W. H. Ku, "Enhanced detectability of small objects in correlated clutter using an improved 2-D adaptive lattice algorithm," IEEE Transactions on Image Processing, Vol. 6, No. 3, 383-397, 1997.
7. Khan, J. F. and M. S. Alam, "Target detection in cluttered forward-looking infrared imagery," Optical Engineering, Vol. 44, No. 7, 0764041-0764048, 2005.
8. Yang, L., J. Yang, and K. Yang, "Adaptive detection for infrared small target under sea-sky complex background," Electronics Letters, Vol. 40, No. 17, 1803-1805, 2004.
9. Barnett, J., "Statistical analysis of median subtraction filtering with application to point target detection in infrared back-grounds," Proc. of SPIE, Vol. 1050, 10-18, 1989.
10. Kaplan, L. M., "Small target detection in clutter using recursive nonlinear prediction," IEEE Transactions on Aerospace and Electronic Systems, Vol. 36, No. 2, 713-717, 2000.
11. Huang, C. W. and K. C. Lee, "Frequency-diversity RCS based target recognition with ica projection," Journal of Electromagnetic Waves and Applications, Vol. 24, No. 17--18, 2547-2559, 2010.
12. Yang, L., Y. Zhou, J. Yang., and L. Chen, "Variance WIE based infrared images processing," Electronics Letters, Vol. 42, No. 15, 857-859, 2006.
13. Xiong, Y., et al., "An extended track-before-detect algorithm for infrared target detection," IEEE Transactions on Aerospace and Electronic Systems, Vol. 33, No. 3, 1087-1092, 1997.
14. Hilliard, C. I., "Selection of a clutter rejection algorithm for real-time target detection from an airborne platform," Proc. SPIE, Vol. 4048, 74-78, 2000.
15. Chan, D. S. K., D. A. Langan, and D. A. Stayer, "Spatial processing techniques for the detection of small targets in IR clutter," Proc. SPIE, Vol. 1305, 53-62, 1990.
16. Mao, X. and W.-H. Diao, "Criterion to evaluate the quality of infrared small target images," Journal of Infrared, Millimeter, and Terahertz Waves, Vol. 30, No. 1, 56-64, 2009.
17. Xu, J., Research on the detection of small and dim targets in infrared images, Ph.D. Thesis, Xi Dian University, 2001.
18. Yonoviz, D., "Tunable wavelet target extraction preprocessor," Proc. of SPIE, Vol. 6569, 1-12, 2007.
19. Yang, L., J. Yang, and K. Yang, "Adaptive detection for infrared small target under sea-sky complex background," Electronics Letters, Vol. 40, No. 17, 1083-1085, 2004.
20. Song, H. B., H. G. Wang, K. Hong, and L. Wang, "A novel source localization scheme based on unitary esprit and city electronic maps in urban environments," Progress In Electromagnetics Research, Vol. 94, 243-262, 2009.
21. Lee, H. H., J. H. Lee, H. K. Song, and C. K. Song, "Simple and efficient received signal detection technique using channel information for mimo-ofdm," Journal of Electromagnetic Waves and Applications, Vol. 23, No. 11--12, 1417-1428, 2009.
22. Tsen, W. F. and H. J. Li, "Optimal impedance matching for capacity maximization of MIMO systems with coupled antennas and noisy amplilfiers," Progress In Electromagnetics Research C, Vol. 15, 23-36, 2010.
23. Nevis, A., "Image characterization and target recognition the surf zone environment," Proc. of SPIE, Vol. 2765, 46-58, 1996.
24. Otsu, N., "A threshold selection method from gray-level histograms," IEEE Transactions on Systems, Man, and Cybernetics, Vol. 9, No. 1, 919-926, 1979.
25. Trievdi, M. M. and M. V. Schirvaikar, "Quantitative characterization of image clutter: Problems, progress, and promises," Characterization, Propagation, and Simulation of Sources and Backgrounds, 288-299, 1993.
26. Li, M. and G. Zhang, "Image measures for segmentation algorithm evaluation of automatic target recognition system," 1st International Symposium on Systems and Control in Aerospace and Astronautics, 673-679, 2006.
27. Victor, T., "Morphology-based algorithm for point target detection in infrared backgrounds," Proc. of SPIE, Vol. 1954, 2-11, 1993.
28. Reed, I. S. and R. M. Gagliardi, "Optical moving target detection with 3-D matched filtering," IEEE Transactions on Aerospace and Electronic System, Vol. 24, No. 4, 327-336, 1988.