volume | PIER Journals

Abstract

An adaptive wavelet packet transform (AWPT) algorithm is proposed to process synthetic aperture radar (SAR) imagery and remove background clutters from target images. Since target features are more efficiently represented using the wavelet packet bases, higher signal-to-clutter ratios (SCR) can be achieved in the wavelet transform domain. Consequently, clutters can be more effectively separated from the desired target features in the transform domain than in the original SAR domain. The processed results based on the MSTAR data set demonstrate the effectiveness of this algorithm for SAR clutter reduction.

1. Wehner, D. R., High Resolution Radar, Artech, Norwood, MA, 1995.

2. Pham, D. H., A. Ezekel, M. T. Campbell, and M. J. T. Smith, "A new end-to-end SAR ATR system," Proceedings of SPIE: Algorithms SAR Imagery VI, Vol. 3721, 292-301, Orlando, Florida, April 1999. Google Scholar

3. Luo, D., Pattern Recognition and Image Processing, Horwood Publishing Limited, Chichester, England, 1998.
doi:10.1533/9780857099761

4. Wickerhauser, M. V., Adapted Wavelet Analysis from Theory to Software, A. K. Peters, Wellesley, Mass., 1994.

5. Chui, C. K., An Introduction to Wavelets, Academic Press, New York, 1992.

6. Daubechies, I., Ten Lectures on Wavelets, SIAM, Philadelphia, Penn., 1992.
doi:10.1137/1.9781611970104

7. Coifman, R. R., Y. Meyer, and M. V. Wickerhauser, "Wavelet analysis and signal processing," Wavelets and Their Applications, 153-178, Jones and Barlett, Boston, 1992. Google Scholar

8. Mallat, S., A Wavelet Tour of Signal Processing, Academic Press, Inc., New York, 1998.

9. Deng, H. and H. Ling, "Fast solution of electromagnetic integral equations using adaptive wavelet packet transform," IEEE Trans. Antennas Propagat., Vol. 47, 674-682, April 1999.
doi:10.1109/8.768807 Google Scholar

10. Donoho, D. L. and I. M. Johnstone, "Ideal spatial adaption by wavelet shrinkage," Biometria, Vol. 81, 425-455, Dec. 1994.
doi:10.1093/biomet/81.3.425 Google Scholar

11. Odegard, J. E., H. Guo, M. Lang, C. S. Burrus, R. O. Wells, Jr., L. M. Novak, and M. Hiett, "Wavelet based SAR speckle reduction and image compression,", Research Report, Comp. Math Lab., Rice Univ., and MIT Lincoln Lab., 1995. Google Scholar

12. MSTAR SAR Data Set, Clutter and Targets, collected by Sandia National Lab, released by DARPA, Apr. 1997.

13. Moulin, P., "A wavelet regularization method for diffuse radartarget imaging and speckle-noise reduction," J. Math. Imag. and Vision, No. 3, 123-134, 1993.
doi:10.1007/BF01248407 Google Scholar

14. Irving, W. W., L. M. Novak, and A. S. Willsky, "A multiresolution approach to discrimination in SAR imagery," IEEE Trans. Aerospace and Elec. Systems, Vol. 33, No. 4, 1157-1168, Oct. 1997.
doi:10.1109/7.625103 Google Scholar

15. Skolnik, M. I., (Ed.), Radar Handbook, 2nd edition, McGraw-Hill, New York, 1990.

16. Van Trees, H. L., Detection, Estimation, and Modulation Theory, Part III, John Wiley & Sons, New York, 1971.

17. Papoulis, A., Probability, Random Variables, and Stochastic Processes, McGraw-Hill, New York, 1965.

18. Coifman, R. R. and M. V. Wickerhauser, "Entropy-based algorithms for best basis selection," IEEE Trans. Info. Theory, Vol. 38, 713-718, March 1992.
doi:10.1109/18.119732 Google Scholar

19. Donoho, D. L., "On minimum entropy segmentation," Wavelets: Theory, Algorithms, and Applications, Academic Press, Inc., New York, 1994. Google Scholar

Dr. H. Deng

Dr. H. Ling