1. New Method for Ship Detection Jian Yang Hongji Zhang Dept. of Electronic Eng., Tsinghua Univ. Yoshio Yamaguchi Dept. of Inform. Eng., Niigata Univ.

2. Outline Background Polarization Entropy and Similarity Parameter GOPCE based ship detection Experiment Results Summary

3. 1. Background Polarimetric Whitening Filter (PWF) Novak, Burl Identity Likelihood Ratio Test DeGraff HH HV VV RR LL Entropy Span Touzi

4. Touzi’s work

5. Optimization of Polarimetric Contrast Enhancement (OPCE) Ioannidis Hammers Kostinski Boerner Yamaguchi Yang

6. Problem Can we employ the OPCE for ship detection? It is easy to get the average Kennaugh matrix of sea clutter, but how can we get or construct the average Kennaugh matrix of ships? Can we extend the OPCE for ship detection?

7. 2 Polarization entropy and similarity parameters

8. Approximate expression From the least square method and Vieta's Theorem The average error:

9. The Formula has a good approximation to the theoretical value of the polarization entropy Eigenvalues and logarithm are unnecessary!!! Running Time by the proposed formula is only 5% of that by the traditional approach J. Yang, Y. Chen, Y. Peng, Y. Yamaguchi, H. Yamada, “New formula of the polarization entropy”, IEICE Trans. Commun., 2006, E89-B(3), 1033-1035

10. Similarity parameter single-look case Multi-look case J. Yang, et al., “Similarity between two scattering matrices,” Electronics Letters, vol.37, no. 3, pp. 193-194, 2001.

11. Similarity between a target and a plate: surface scattering Similarity between a target and a diplane: Double-bounce scattering

12. Generalized OPCE (GOPCE) based ship detection J. Yang, Y. Yamaguchi, W. -M. Boerner, S. M. Lin, “Numerical methods for solving the optimal problem of contrast enhancement,” IEEE Trans. Geosci. Remote Sensing, 2000, 38(2), pp. 965-971

13. GOPCE: Generalized OPCE J. Yang, et al., “Generalized optimization of polarimetric contrast enhancement”, IEEE GRSL., vol.1, no.3, pp.171-174, 2004

14. GOPCE based ship detection For a sea area subject to: subject to:

15. Average Kennaugh matrix of ships the scattering contributions of a ship direct reflection of plates double reflections of diplates of the ship some multi-reflections of the surface of the ship, or some multi-reflections between the ship and the sea surface

16. Experimental results NASA/JPL AirSAR over Sydney coast, Australia. Span image

17. Experiment results

18. Power Image by OPCE

19. Experiment results GP Image by GOPCE

20. Experiment results Filtered result by PWF

21. spanPWF OPCEGOPCE Detection results: false alarm rate 1%

22. 5. Summary (1) OPCE has been developed (2) GOPCE is effective for ship detection

23. Thank you! yangjian_ee@tsinghua.edu.cn

24. Speckle Filtering Speckle phenomenon in SAR/POLSAR Scattering from distributed scatterers Coherent interferences of waves scattered from many randomly distributed scatterers in the resolution cell Granular Noise Speckle Phenomenon Observation Point Surface Roughness 24/28

25. Speckle Filtering Challenge of speckle filtering Detail PreservationSpeckle Reduction Speckle Filtering These two objectives should be achieved simultaneously These two objectives should be achieved simultaneously 25/28

26. Pre-test Approach for Speckle Filtering Classical Methods for Speckle Filtering Boxcar Filter MMSE Lee’s filter with edge detector 3*3 boxcar To-be-filtered pixel Pixel selected for averaging 8-direction edge detectors 26/28

27. Pre-test Approach for Speckle Filtering Selecting homogenous pixelsAveraging homogenous pixels Boxcar local area In neighboring areaUniformly weight LeeIn aligned matching windowBy mmse criteria Pre-testIn non-local areaBy the similarity of patch To-be-filtered pixel Pre-tested pixels Summary of Speckle Filtering : Two-Step Methodology An example of pre-testing homogenous pixels in non-local area by patch patch patch : pixel itself and its local neighboring non-local area : other than neighboring area pre-test : selecting homogenous pixels in non-local area by patch 27/28

28. Pre-test Approach for Speckle Filtering Non-local but homogeneous pixels using proposed method 28/28

29. Pre-test Approach for Speckle Filtering For each pixel - For each pixel in non-local searching area 1. Calculate the similarity test between the patches with and as the center respectivel 2. If test > threshold, accept as the homogenous pixel to, and calculate the weight 3. Average homogenous pixels with their normalized weight to get filtered covariance matrix searching area of Calculate the similarity between 2 patches patch 29/28

30. Pre-test Approach for Speckle Filtering Experimental Results (a)Original(b) Refined Lee(c) Pre-test SAR-Convair 580 C-band Image size : 340*220 Resolution : 6.4m*10m 30/28

31. C-band AirSAR data SAN Francisco area (a)Original (b)Boxcar (c)Refined Lee (d)Pre-test 4 multi-look Image size : 300*300 Resolution : About 10m*10m 31/28