предыдущий|следующий

CFAR detection method of polarimetric SAR imagery based on whitening filter under G0 distribution

  • role： First author第一作者
  • Affiliation：

    School of Electronic Engineering, Naval University of Engineering, Wuhan 430030, China

    No.92118 Unit of PLA, Zhoushan 316000, China

  • Email：751971863@qq.com
  • Introduction：张嘉峰，1993年生，男，助理工程师，研究方向为雷达极化信息处理、新体制雷达技术及电子战建模与仿真。E-mail：751971863@qq.com
Jiafeng ZHANG12,  
  • Affiliation：

    School of Electronic Engineering, Naval University of Engineering, Wuhan 430030, China

Peng ZHANG1,  
  • Affiliation：

    School of Electronic Engineering, Naval University of Engineering, Wuhan 430030, China

Mingchun WANG1,  
  • Affiliation：

    School of Electronic Engineering, Naval University of Engineering, Wuhan 430030, China

Tao LIU1

реферат

Poor applicability has been a common issue among high-resolution clutter models that use the existing constant false alarm rate (CFAR) detection methods for polarimetric synthetic aperture radar (PolSAR) imageries. To solve the problem, a CFAR detection method with G0 distribution and closed analytical expression for PolSAR imageries is proposed. CFAR loss (CL) is used to quantitatively evaluate the maintenance performance of CFAR detection methods. First, the probability density function (PDF) of the multi-look polarization whitening filter (MPWF) metric is derived on the basis of product modeling by combining the hypotheses of inverse Gamma distribution texture. Second, the PDF of the MPWF metric is integrated, and the analytical expression of the false alarm rate with respect to the CFAR detection threshold is obtained. The process of the proposed CFAR detection method is also designed. Finally, the simulation data and the AIRSAR real data are used to compare the running times of the methods, the fitting performances of the detection metrics, and the target detection performances of the existing and proposed methods. Results show that the running time of the proposed method is 3—30 times shorter than those of the existing methods, and the real-time performance of the proposed method is also enhanced. The analytical results of the AIRSAR real data of Tamano (Japan) show thatG0 distribution has a good fitting performance with high-resolution non-uniform sea areas. The proposed method can detect targets effectively in both G0 and non-G0 distribution sea areas, and its robustness is better than those of the existing methods. Moreover, the figure of merit of the proposed method is 15.78% higher than those of the other detection methods. The analytical results of CL indicate that the proposed method has good CFAR maintenance performance. Moreover, the closer the clutter log-cumulant scatter points are to the G0 distribution curve, the better the CFAR maintenance performance of the proposed method.

ключеви́че слова́

polarimetric synthetic aperture radar (PolSAR);constant false alarm rate (CFAR);product model;G0 distribution;multi-look polarization whitening filter (MPWF);false alarm rate;CFAR Loss (CL);figure of merit (FoM)

References

  1. 1.
    Bateman H. 1954. Tables of Integral Transforms. New York: McGraw-Hill Book Company, Inc.: 313
  2. 2.
    Boerner W M, Kostinski A and James B. 1988. On the concept of the polarimetric matched filter in high resolution radar imagery: an alternative for speckle reduction//Proceedings of 1988 International Geoscience and Remote Sensing Symposium. Edinburg: [s.n.]: 69–72
  3. 3.
    Chong J S and Zhu M H. 2003. Target detection algorithm of SAR image based on local window K-distribution. Journal of Electronics and Information Technology, 25(9): 1276–1280 (
  4. 4.
    Cui H G. 2014. Research on Statistical Modelling and Parameters Estimation of PolSAR Imagery. Wuhan: Naval University of Engineering: 123–125
  5. 5.
    Frery A C, Muller H J, Yanasse C C F and Sant’Anna S J S. 1997. A model for extremely heterogeneous clutter. IEEE Transactions on Geoscience and Remote Sensing, 35(3): 648–659
  6. 6.
    Goldstein G B. 1973. False-alarm regulation in log-normal and weibull clutter. IEEE Transactions on Aerospace and Electronic Systems, AES-9(1): 84–92
  7. 7.
    Gradshteyn I S and Ryzhik I M. 2007. Table of Integrals, Series, and Products. 7th ed. San Diego, CA: Academic Press: 316
  8. 8.
    He Z G, Zhou X G, Lu J and Kuang G Y. 2009. A fast CFAR detection algorithm based on the G0 distribution for SAR images. Journal of National University of Defense Technology, 31(1): 47–51 (
  9. 9.
    Jakeman E and Pusey P. 1976. A model for non-Rayleigh sea echo. IEEE Transactions on Antennas and Propagation, 24(6): 806–814
  10. 10.
    Jao J. 1984. Amplitude distribution of composite terrain radar clutter and the κ-distribution. IEEE Transactions on Antennas and Propagation, 32(10): 1049–1062
  11. 11.
    Khan S and Guida R. 2014. On fractional moments of multilook polarimetric whitening filter for polarimetric SAR data. IEEE Transactions on Geoscience and Remote Sensing, 52(6): 3502–3512
  12. 12.
    Liu G Q, Huang S J, Torre A and Rubertone F. 1998. A new multi-look polarimetric SAR target detector and its performance analysis. Signal Processing, 14(2): 110–116 (
  13. 13.
    Liu G Q, Huang S J, Torre A and Rubertone F. 1998. The multilook polarimetric whitening filter (MPWF) for intensity speckle reduction in polarimetric SAR images. IEEE Transactions on Geoscience and Remote Sensing, 36(3): 1016–1020
  14. 14.
    Liu T, Cui H G and Gao J. 2013. Statistics of the determinant of the Wishart distributed matrix and its application to parameter estimation. Acta Electronica Sinica, 41(6): 1231–1237 (
  15. 15.
    Liu T, Cui H G, Mao T, Xi Z M and Gao J. 2012. Novel estimation of the equivalent number of looks in polari-metric SAR imagery based on log-cumulants of sub-covariance- matrix. Scientia Sinica (Informationis), 42(11): 1459–1470 (
  16. 16.
    Liu T, Cui H G, Xi Z M and Gao J. 2016. Novel estimators of equivalent number of looks in polarimetric SAR imagery based on sub-matrices. Science China Information Sciences, 59(6): 062309
  17. 17.
    Lopes A and Sery F. 1997. Optimal speckle reduction for the product model in multilook polarimetric SAR imagery and the wishart distribution. IEEE Transactions on Geoscience and Remote Sensing, 35(3): 632–647
  18. 18.
    Lu T Z, Zhang J, Ji Y G, Zhang X and Meng J M. 2011. Ship target detection algorithm based on G0 distribution for SAR images under rough sea conditions. Advances in Marine Science, 29(2): 186–195 (
  19. 19.
    Novak L M and Burl M C. 1990. Optimal speckle reduction in polarimetric SAR imagery. IEEE Transactions on Aerospace and Electronic Systems, 26(2): 293–305
  20. 20.
    Novak L M, Burl M C and Irving W W. 1991. Optimal polrimetric procesing for enhanced target detetion//Proceedings of 1991 National Telesystems Conference. Atlanta: 69–75
  21. 21.
    Novak L M, Sechtin M B, Cardullo M J. 1989. Studies of target detection algorithms that use polarimetric radar data. IEEE Transactions on Aerospace and Electronic Systems, 25(2): 150–165
  22. 22.
    Pi Y M, Zou Q and Huang S J. 2002. Speckle reduction of polarimetric SAR—polarimetric whitening filter. Journal of Electronics and Information Technology, 24(5): 597–603 (
  23. 23.
    Stinco P, Greco M and Gini F. 2011. Adaptive detection in compound-Gaussian clutter with inverse-gamma texture//Proceedings of 2011 IEEE CIE International Conference on Radar. Chengdu: IEEE: 434–437 [DOI: 10.1109/CIE-Radar.2011.6159570]
  24. 24.
    Wang N, Shi G T, Lu J and Kuang G Y. 2011. A new polarimetric SAR image CFAR target detecting method. Journal of Electronics and Information Technology, 33(2): 395–400 (
  25. 25.
    Wang N, Liu L, Hu C B, Kuang G Y and Jiang Y M. 2011. A novel polarimetric CFAR target detection method//Proceedings of 2011 International Workshop on Multi-Platform/multi-Sensor Remote Sensing and Mapping. Xiamen: IEEE: 1–6 [DOI: 10.1109/M2RSM.2011.5697368]
  26. 26.
    Wei J J, Li P X, Yang J, Zhang J X and Lang F K. 2014. A new automatic ship detection method using L-band polarimetric SAR imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(4): 1383–1393
  27. 27.
    Weinberg G V. 2013. Coherent CFAR detection in compound Gaussian clutter with inverse gamma texture. EURASIP Journal on Advances in Signal Processing, 2013: 105
  28. 28.
    Witkovský V. 2001. Computing the distribution of a linear combination of inverted gamma variables. Kybernetika, 37(1): 79–90
  29. 29.
    Zhang J F, Zhu B, Zhang P, Wang M C and Liu T. 2018. Polarimetric SAR imagery target CFAR detection analytical algorithm with Wishart distribution. Acta Electronica Sinica, 46(2): 433–439 (
  30. 30.
    Zhao Y N, Pang X Y and Yin B. 2013. Adaptive radar detection for targets in compound-Gaussian clutter with inverse-gamma texture//Proceedings of IET International Radar Conference 2013. Xi’an: IEEE: 1–4 [DOI: 10.1049/cp.2013.0249]

Читать полностью

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website