1) The document proposes three potential markers for accurate wetland boundary classification using polarimetric synthetic aperture radar (PolSAR) image analysis: HH-VV phase difference, double-bounce scattering from a four-component decomposition model, and correlation coefficient in the LR basis. 2) Polarimetric scattering feature analysis was performed on PolSAR data collected over Lake Sakata using the FDTD method for a simple water-emergent boundary model. 3) The FDTD analysis found that all three proposed markers showed potential for delineating the wetland boundary, with HH-VV phase difference averaging 141 degrees, double-bounce scattering dominating the decomposition, and high correlation coefficients in LR basis.

1. IGARSS 2011, July 24-29, 2011, Vancouver, Canada Polarimetric Scattering Feature Estimation For Accurate WetlandBoundary Classification Ryoichi SATO*, Yoshio YAMAGUCHI, and Hiroyoshi YAMADA Niigata University, Japan

2. Introduction Progress of Global warming - Unusual weather (Climate change) - Natural disasters (Flooding, Water shortage) Monitoring of “Natural resources” (Forests, wetlands, etc.) Winter Lake “Sakata” and surrounding wetland Copyright © 2001-2004 Niigata City. All rights reserved.

3. Introduction “PolSARimage analysis” is a useful tool for continuous wetland monitoring ALOS/PALSAR Pi-SAR http://www.alos-restec.jp/aboutalos1.html Satellite PolSAR http://www.das.co.jp/new_html/service/05.html Airborne PolSAR Summer Copyright © 2001-2004 Niigata City. All rights reserved. So far, Accurate and “complex” wetland classification method

4. Objective ``Simple’’ water area classification marker for water-emergent boundary 1. PolSAR image analysis around wetland area Validity of some polarimetric indices as useful markers for water-emergent boundary classification 2. FDTD polarimetric scattering analysis for a simple water-emergent boundary model Verification of the generating mechanism of specific polarimetric scattering feature at the boundary

5. Candidates for wetland boundary classification 1. HH-VV phase difference: [1] K.O. Pope, et al. ,``Detecting seasonal flooding cycles in marches of the yucatan peninsula with sar-c polarimetric radar imagery,’’ Remote Sensing Environ., vol.59, no.2 pp.157-166, Feb.1997. Reed Ground Water Looks like Dihedral reflector

6. TRUE Water area Candidates for wetland boundary classification Double-bounce scattering Surface scattering Volume scattering Reed Ground Water Looks like Dihedral reflector 2. Double-bounce scattering: Pd Ps Pv Pc [5] A. Freeman and S.L.Durden,``A three-component scattering model for polarimetric SAR data,’’ IEEE Trans. Geosi. Remote Sensiing, vol.36, no.3 pp.963-973, May 1998. [6] Y. Yamaguchi et al, ``Four-component scattering model for polarimetric SAR image decomposition,’’ IEEE Trans. Geosi. Remote Sensiing, vol.43, no.8 pp.1699-1706, Aug. 2005.

7. Candidates for wetland boundary classification 3. LL-RR correlation coefficient: [Kimura 2004] K. Kimura, et al. ,``Circular polarization correlation coefficient for detection of non-natural targets aligned not parallel to SAR flight path in the X-band POLSAR image analysis,’’ vol.E87-B, no.10 pp.3050-3056, Oct.2004. [Schuler 2006] D. Schuler, J.-S. Lee, and G.D.DeGrande, ``Characteristics of polarimetric SAR scattering in urban and natural areas,'' Proc. of EUSAR 2006 (CD-ROM), May 2006. .

8. PolSAR image analysis 1. HH-VV phase difference 2. Double-bounce scattering (4-component model) 3. Correlation coefficient in LR basis

9. PolSAR data description L-band 1.27GHz (l=0.236m) Quad. polarimetric data take function Lake “SAKATA” Mode: Quad.Pol. HH+HV+VH+VV Pi-SAR & ALOS/PALSAR Winter Summer Autumn * Acquired by JAXA, Japan **Acquired by JAXA, Japan

10. PolSAR image analysis 1. HH-VV phase difference 2. Double-bounce scattering (4-component model) 3. Correlation coefficient in LR basis

11. PolSAR image analysis Candidate 1: L-band Pi-SAR Lake “SAKATA” Feb. illumination Winter +pi Aug. Summer 0 Nov. Autumn

12. PolSAR image analysis 1. HH-VV phase difference 2. Double-bounce scattering (4-component model) 3. Correlation coefficient in LR basis

13. PolSAR image analysis Candidate 2: L-band Pi-SAR Lake “SAKATA” Feb. illumination Winter Pd Aug. Summer Ps Pv Nov. Autumn

14. B A B A B A PolSAR image analysis Candidate 2: L-band Pi-SAR Lake “SAKATA” Feb. illumination Winter Pd Aug. Summer Ps Pv Nov. Autumn

15. Surface scattering Surface scattering Volume scattering Reed Double-bounce scattering TRUE Water area Water Ground Double-bounce scattering Surface scattering Double-bounce scattering Volume scattering Reed Ground Water PolSAR image analysis Candidate 2: L-band Emergent (Reeds) Pi-SAR Water Winter Summer Autumn Ps(Surface scattering) Pd(Double-bounce scattering) Pv(Volume scattering)

16. PolSAR image analysis Candidate 2: L-band Pi-SAR Lake “SAKATA” Feb. illumination Winter Pd Aug. Summer Ps Pv Nov. Autumn

17. PolSAR image analysis 1. HH-VV phase difference 2. Double-bounce scattering (4-component model) 3. Correlation coefficient in LR basis

18. PolSAR image analysis Candidate 3: L-band Pi-SAR Lake “SAKATA” Feb. illumination Winter 1.0 Aug. Summer 0.0 Nov. Autumn

19. PolSAR image analysis Candidate 3: L-band Pi-SAR Lake “SAKATA” Feb. illumination Winter +pi Aug. Summer -pi Nov. Autumn

20. PolSAR image analysis 1. HH-VV phase difference 2. Double-bounce scattering (4-component model) 3. Correlation coefficient in LR basis

21. Polarimetric FDTD analysis 1. HH-VV phase difference 2. Double-bounce scattering (4-component model) 3. Correlation coefficient in LR basis

22. Polarimetric FDTD analysis Polarimetric scattering analysis for simple boundary model by using the FDTD method Dielectric pillars (vertical stems of the emergent plants) High water level case Dielectric plate (Water) Vertical thin dielectric pillarson a dielectric plate (Vertical stems of emerged-plants on water surfacewhen the water level is relatively high. ) A where is added to reduce unnecessary back scattering from the horizontal front edge.

23. Polarimetric FDTD analysis High water level case To determine the relative permittivity for the dielectric base plate or water in the model, the actual relative permittivity of the water in “SAKATA” is measured by a dielectric probe kit (Agilent 85070C). er= 82.78 +i 8.01 at 1.2GHz

24. Polarimetric FDTD analysis Parameters in the FDTD analysis 1cm er= 2.0 + i0.05 1cm at 1.2GHz f=f0=0o q=q0=45o Each dielectric pillar L=9.6l(2.40m), H1=5.6l(1.40m), D1=2.4l(0.60m), D2=3.40l(0.85m) at 1.2GHz Other parameters in the FDTD simulation Analytical region 1200 X 1200 X 1000 cells Cubic cell size D 0.0025m Time step Dt 4.8125 X 10-12s Incident pulse Lowpass Gaussian pulse Absorbing boundary condition PML (8 layers)

25. Polarimetric FDTD analysis Statistical evaluation To evaluate statisticalpolarimetric scattering feature as actual PolSARimage analysis, Vertical pillars are randomly set on dielectric plate Plain view The ensemble average processing is carried out for 6random distributed patterns.

26. Polarimetric FDTD analysis 1. HH-VV phase difference 2. Double-bounce scattering (4-component model) 3. Correlation coefficient in LR basis

27. Polarimetric FDTD analysis 1. HH-VV phase difference 2. Double-bounce scattering (4-component model) 3. Correlation coefficient in LR basis

28. Polarimetric FDTD analysis 1. HH-VV phase difference Ave. 141o So so!

29. Polarimetric FDTD analysis 1. HH-VV phase difference 2. Double-bounce scattering (4-component model) 3. Correlation coefficient in LR basis

30. Polarimetric FDTD analysis 2. Double-bounce scattering (4-component model) The ensemble average processing is carried out for 6random distributed models.

31. Polarimetric FDTD analysis 2. Double-bounce scattering (4-component model) Very useful Pt=Pd+Pv+Ps+Pc Pv/Pt Pd/Pt Ps/Pt Pc/Pt

32. ``Unitary rotation’’ possible ``Unitary rotation’’ of the original coherency matrix Condition for determining the rotation angle So we obtain the rotation angle as

33. Polarimetric FDTD analysis 2. Double-bounce scattering (4-component model) w/o rotation with T33 rotation Pv/Pt Pd/Pt Ps/Pt Pc/Pt

34. Polarimetric FDTD analysis 1. HH-VV phase difference 2. Double-bounce scattering (4-component model) 3. Correlation coefficient in LR basis

35. Polarimetric FDTD analysis 3. Correlation coefficient in LR basis The ensemble average processing is carried out for 6random distributed models.

36. Polarimetric FDTD analysis 3. Correlation coefficient in LR basis Man-made object : Phase tends to be 0 or 180 deg. Man-made object : Amp. shows large value

37. Polarimetric FDTD analysis 3. Correlation coefficient in LR basis Reflection symmetry i.e. This condition is derived from experimental results. Amplitude Phase 0 or p Real

38. Conclusion To verify three polarimetric indices as simple wetland boundary classification markers PolSAR image analysis and FDTD polarimetric scattering analysis for wetland boundary (water-emergent ) model ``qHH-qVV” ,``Pd” and gLL-RRare ALL useful markers, when the water level is relatively high.

39. Future developments - Comparison with accurate method (Touzi decomposition etc.) - FDTD polarimetric scattering analysis 1. Variation of the incident and squint angles 2. Variation of the volume density 3. Difference between wet and dry conditions Which wetland classes in Touzi decomposition correspond to each boundary feature? Dielectric plate (Water)

40. Acknowledgments This research was partially supported by - A Scientific Research Grant-In-Aid (22510188) from JSPS , -Telecom Engineering Center (TELEC)

41. Thank you!