Polarimetric Scattering Feature Estimation For Accurate Wetland Boundary Classification

  • 617 views
Uploaded on

 

More in: Technology
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
No Downloads

Views

Total Views
617
On Slideshare
0
From Embeds
0
Number of Embeds
0

Actions

Shares
Downloads
16
Comments
0
Likes
1

Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide

Transcript

  • 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!