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Polarimetric Scattering Feature Estimation For Accurate Wetland Boundary Classification

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Polarimetric Scattering Feature Estimation For Accurate Wetland Boundary Classification

  1. 1. IGARSS 2011, July 24-29, 2011, Vancouver, Canada<br />Polarimetric Scattering Feature Estimation For Accurate WetlandBoundary Classification<br />Ryoichi SATO*, Yoshio YAMAGUCHI, <br />and Hiroyoshi YAMADA<br />Niigata University, Japan<br />
  2. 2. Introduction<br />Progress of Global warming<br />- Unusual weather (Climate change)<br />- Natural disasters<br />(Flooding, Water shortage)<br />Monitoring of “Natural resources”<br /> (Forests, wetlands, etc.)<br />Winter<br />Lake “Sakata”<br /> and surrounding wetland<br />Copyright © 2001-2004 Niigata City. All rights reserved.<br />
  3. 3. Introduction<br />“PolSARimage analysis” is a useful tool <br />for continuous wetland monitoring<br />ALOS/PALSAR<br />Pi-SAR<br />http://www.alos-restec.jp/aboutalos1.html<br />Satellite PolSAR<br />http://www.das.co.jp/new_html/service/05.html<br />Airborne PolSAR<br />Summer<br />Copyright © 2001-2004 Niigata City. All rights reserved.<br />So far, <br />Accurate and “complex” wetland classification method <br />
  4. 4. Objective<br />``Simple’’ water area classification marker <br />for water-emergent boundary<br />1. PolSAR image analysis around wetland area <br />Validity of some polarimetric indices as useful markers <br /> for water-emergent boundary classification<br />2. FDTD polarimetric scattering analysis<br /> for a simple water-emergent boundary model<br />Verification of the generating mechanism of specific <br />polarimetric scattering feature at the boundary<br />
  5. 5. Candidates for wetland boundary classification<br />1. HH-VV phase difference:<br />[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. <br />Reed<br />Ground<br />Water<br />Looks like <br />Dihedral reflector<br />
  6. 6. TRUE Water area<br />Candidates for wetland boundary classification<br />Double-bounce <br /> scattering<br />Surface scattering<br />Volume scattering<br />Reed<br />Ground<br />Water<br />Looks like <br />Dihedral reflector<br />2. Double-bounce scattering: <br />Pd<br />Ps<br />Pv<br />Pc<br />[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. <br />[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. <br />
  7. 7. Candidates for wetland boundary classification<br />3. LL-RR correlation coefficient:<br />[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. <br />[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. . <br />
  8. 8. PolSAR image analysis<br />1. HH-VV phase difference<br />2. Double-bounce scattering (4-component model)<br />3. Correlation coefficient in LR basis<br />
  9. 9. PolSAR data description<br />L-band 1.27GHz (l=0.236m)<br />Quad. polarimetric data take function<br />Lake “SAKATA”<br />Mode: Quad.Pol. HH+HV+VH+VV<br />Pi-SAR & ALOS/PALSAR<br />Winter<br />Summer<br />Autumn<br />* Acquired by JAXA, Japan<br />**Acquired by JAXA, Japan<br />
  10. 10. PolSAR image analysis<br />1. HH-VV phase difference<br />2. Double-bounce scattering (4-component model)<br />3. Correlation coefficient in LR basis<br />
  11. 11. PolSAR image analysis<br />Candidate 1: <br />L-band<br />Pi-SAR<br />Lake “SAKATA”<br />Feb.<br />illumination<br />Winter<br />+pi<br />Aug.<br />Summer<br />0<br />Nov.<br />Autumn<br />
  12. 12. PolSAR image analysis<br />1. HH-VV phase difference<br />2. Double-bounce scattering (4-component model)<br />3. Correlation coefficient in LR basis<br />
  13. 13. PolSAR image analysis<br />Candidate 2: <br />L-band<br />Pi-SAR<br />Lake “SAKATA”<br />Feb.<br />illumination<br />Winter<br />Pd<br />Aug.<br />Summer<br />Ps<br />Pv<br />Nov.<br />Autumn<br />
  14. 14. B<br />A<br />B<br />A<br />B<br />A<br />PolSAR image analysis<br />Candidate 2: <br />L-band<br />Pi-SAR<br />Lake “SAKATA”<br />Feb.<br />illumination<br />Winter<br />Pd<br />Aug.<br />Summer<br />Ps<br />Pv<br />Nov.<br />Autumn<br />
  15. 15. Surface scattering<br />Surface scattering<br />Volume scattering<br />Reed<br />Double-bounce <br /> scattering<br />TRUE Water area<br />Water<br />Ground<br />Double-bounce <br /> scattering<br />Surface scattering<br />Double-bounce <br /> scattering<br />Volume scattering<br />Reed<br />Ground<br />Water<br />PolSAR image analysis<br />Candidate 2: <br />L-band<br />Emergent<br />(Reeds)<br />Pi-SAR<br />Water<br />Winter<br />Summer<br />Autumn<br />Ps(Surface scattering)<br />Pd(Double-bounce scattering)<br />Pv(Volume scattering)<br />
  16. 16. PolSAR image analysis<br />Candidate 2: <br />L-band<br />Pi-SAR<br />Lake “SAKATA”<br />Feb.<br />illumination<br />Winter<br />Pd<br />Aug.<br />Summer<br />Ps<br />Pv<br />Nov.<br />Autumn<br />
  17. 17. PolSAR image analysis<br />1. HH-VV phase difference<br />2. Double-bounce scattering (4-component model)<br />3. Correlation coefficient in LR basis<br />
  18. 18. PolSAR image analysis<br />Candidate 3: <br />L-band<br />Pi-SAR<br />Lake “SAKATA”<br />Feb.<br />illumination<br />Winter<br />1.0<br />Aug.<br />Summer<br />0.0<br />Nov.<br />Autumn<br />
  19. 19. PolSAR image analysis<br />Candidate 3: <br />L-band<br />Pi-SAR<br />Lake “SAKATA”<br />Feb.<br />illumination<br />Winter<br />+pi<br />Aug.<br />Summer<br />-pi<br />Nov.<br />Autumn<br />
  20. 20. PolSAR image analysis<br />1. HH-VV phase difference<br />2. Double-bounce scattering (4-component model)<br />3. Correlation coefficient in LR basis<br />
  21. 21. Polarimetric FDTD analysis<br />1. HH-VV phase difference<br />2. Double-bounce scattering (4-component model)<br />3. Correlation coefficient in LR basis<br />
  22. 22. Polarimetric FDTD analysis<br />Polarimetric scattering analysis for simple boundary model<br />by using the FDTD method<br />Dielectric pillars<br />(vertical stems of the emergent plants)<br />High water level case<br />Dielectric plate (Water)<br /> Vertical thin dielectric pillarson a dielectric plate<br />(Vertical stems of emerged-plants on water surfacewhen the water level is relatively high. )<br />A<br />where<br />is added to reduce unnecessary back scattering from the horizontal front edge. <br />
  23. 23. Polarimetric FDTD analysis<br />High water level case<br />To determine the relative permittivity <br />for the dielectric base plate or water <br />in the model, <br />the actual relative permittivity of the water <br />in “SAKATA” is measured <br />by a dielectric probe kit (Agilent 85070C). <br />er= 82.78 +i 8.01<br />at 1.2GHz<br />
  24. 24. Polarimetric FDTD analysis<br />Parameters in the FDTD analysis<br />1cm<br />er= 2.0 + i0.05<br />1cm<br />at 1.2GHz<br />f=f0=0o<br />q=q0=45o<br />Each dielectric pillar<br />L=9.6l(2.40m), H1=5.6l(1.40m), <br />D1=2.4l(0.60m), D2=3.40l(0.85m) at 1.2GHz<br />Other parameters in the FDTD simulation<br />Analytical region<br />1200 X 1200 X 1000 cells<br />Cubic cell size D<br />0.0025m<br />Time step Dt<br />4.8125 X 10-12s<br />Incident pulse<br />Lowpass Gaussian pulse<br />Absorbing boundary condition<br />PML (8 layers)<br />
  25. 25. Polarimetric FDTD analysis<br />Statistical evaluation<br />To evaluate statisticalpolarimetric scattering feature <br /> as actual PolSARimage analysis, <br />Vertical pillars are randomly set on dielectric plate <br />Plain view<br />The ensemble average processing is carried out <br />for 6random distributed patterns.<br />
  26. 26. Polarimetric FDTD analysis<br />1. HH-VV phase difference<br />2. Double-bounce scattering (4-component model)<br />3. Correlation coefficient in LR basis<br />
  27. 27. Polarimetric FDTD analysis<br />1. HH-VV phase difference<br />2. Double-bounce scattering (4-component model)<br />3. Correlation coefficient in LR basis<br />
  28. 28. Polarimetric FDTD analysis<br />1. HH-VV phase difference<br />Ave. 141o<br />So so!<br />
  29. 29. Polarimetric FDTD analysis<br />1. HH-VV phase difference<br />2. Double-bounce scattering (4-component model)<br />3. Correlation coefficient in LR basis<br />
  30. 30. Polarimetric FDTD analysis<br />2. Double-bounce scattering (4-component model)<br />The ensemble average processing is carried out <br />for 6random distributed models. <br />
  31. 31. Polarimetric FDTD analysis<br />2. Double-bounce scattering (4-component model)<br />Very useful<br />Pt=Pd+Pv+Ps+Pc<br />Pv/Pt<br />Pd/Pt<br />Ps/Pt<br />Pc/Pt<br />
  32. 32. ``Unitary rotation’’ possible<br />``Unitary rotation’’ of the original coherency matrix<br />Condition for determining the rotation angle<br />So we obtain the rotation angle as<br />
  33. 33. Polarimetric FDTD analysis<br />2. Double-bounce scattering (4-component model)<br />w/o rotation<br />with T33 rotation<br />Pv/Pt<br />Pd/Pt<br />Ps/Pt<br />Pc/Pt<br />
  34. 34. Polarimetric FDTD analysis<br />1. HH-VV phase difference<br />2. Double-bounce scattering (4-component model)<br />3. Correlation coefficient in LR basis<br />
  35. 35. Polarimetric FDTD analysis<br />3. Correlation coefficient in LR basis<br />The ensemble average processing is carried out <br />for 6random distributed models. <br />
  36. 36. Polarimetric FDTD analysis<br />3. Correlation coefficient in LR basis<br />Man-made object :<br />Phase tends to be 0 or 180 deg.<br />Man-made object :<br />Amp. shows large value<br />
  37. 37. Polarimetric FDTD analysis<br />3. Correlation coefficient in LR basis<br />Reflection symmetry<br />i.e.<br />This condition is derived from experimental results. <br />Amplitude<br />Phase<br />0 or p<br />Real<br />
  38. 38. Conclusion<br />To verify three polarimetric indices <br />as simple wetland boundary classification markers<br />PolSAR image analysis and <br />FDTD polarimetric scattering analysis <br />for wetland boundary (water-emergent ) model <br />``qHH-qVV” ,``Pd” and gLL-RRare ALL useful markers, <br />when the water level is relatively high. <br />
  39. 39. Future developments<br />- Comparison with accurate method<br />(Touzi decomposition etc.)<br />- FDTD polarimetric scattering analysis<br />1. Variation of the incident and squint angles<br />2. Variation of the volume density <br />3. Difference between wet and dry conditions<br />Which wetland classes in Touzi decomposition <br />correspond to each boundary feature? <br />Dielectric plate (Water)<br />
  40. 40. Acknowledgments<br />This research was partially supported by <br />- A Scientific Research Grant-In-Aid (22510188) <br /> from JSPS , <br />-Telecom Engineering Center (TELEC)<br />
  41. 41. Thank you!<br />

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