MODEL-BASED POLARIMETRIC SAR CALIBRATION METHOD USING FOREST AND SURFACE SCATTERING TARGETS<br />Masanobu Shimada<br />Ear...
POLCAL history<br />Van Zyl (1990): radar reciprocity, scattering reciprocity(HV=VH),reflection symmetry, and CR<br />Queg...
Covariance model for forest<br />Forest data covariance is expressed by double bounce, surface scattering and volume scatt...
Covariance matrix model used for the forest<br />
Assumption of the Proposed method<br />AH<br />RH<br />SAR-P<br />Z<br />S<br />O<br />AV<br />RV<br />DT, DR1<br />DR2<br...
L-band backscattering from the forest consists of the volume scattering and double bounce (polarization dependent).  (refl...
Scattering Reciprocity (HV=VH) for the volume component
Neglect the HV in surface and double bounce
Use a surface scatterer (including CR) for parameter determination.
No Faraday rotation.
Calibrated Antennas used.</li></li></ul><li>Calibration model<br />Two step determinations of 9 parameters (7 complex, and...
First order terms (channel imbalances and forest parameters)<br />Solutions<br />Equations<br />Unknowns:F1,F2,q1,q2,fv,fd...
Second Order Terms (Crosstalk’s of distortion matrices)<br />Then, the solution is given by<br />
Noise estimation: determined by the following iterative operation<br />
Features of the proposed method<br /><ul><li>Determine the channel imbalances using the forest and the surface scatterer (...
Estimate the HH and VV double bounces and use them for estimating the cross talks (Second order calculation).
No ignorance of the higher order terms (needs iteration)
Ease of the Test site selection (In Amazon)</li></li></ul><li>Evaluations of assumptions<br />Amazon PALSAR data calibrate...
Reflection symmetry<br />Correlation coefficient<br />
Application of Freeman Durden model<br />fs << fd<br />
Ignorance of the surface scattering<br />Condition: <br />Freeman-Durden ignores HV of surface scattering<br />	This model...
Conditions for obtaining the solutions<br />Necessity conditions<br />Boarder lines<br />
Determination of the forest covariance using the Amazon data<br />Min.    :   =0.325,     =0.295<br />
Covariance Parameter and scattering model<br />
List of the data acquired at Amazon rainforest site<br />
Definition of the method<br />
Qeagan<br />
Temporal variation of the PolCAL parameters (Method-1”’)<br />
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POLCAL_shimada.pptx

  1. 1. MODEL-BASED POLARIMETRIC SAR CALIBRATION METHOD USING FOREST AND SURFACE SCATTERING TARGETS<br />Masanobu Shimada<br />Earth Observation Research Center (EORC)<br />Japan Aerospace Exploration Agency (JAXA), Sengen 2-1-1, Tsukuba, Ibaraki, Japan, 305-8505, Voice 81-50-3362-4489, Fax: 81-29-868-2961, shimada.masanobu@jaxa.jp<br />July 28, 2011<br />IGARSS2011<br />
  2. 2. POLCAL history<br />Van Zyl (1990): radar reciprocity, scattering reciprocity(HV=VH),reflection symmetry, and CR<br />Quegan (1994): scattering reciprocity, no-radar reciprocity, reflection symmetry, and CR <br />Ainsworth (2006), Kimura(2008):reflection symmetry and use of urban areas<br />Motivation of research:<br />Rain forest : distributed target (diffuse scatterer)<br /> could be usable for the POLCAL<br /> reflection symmetry ? , scattering reciprocity?<br /> >New trial of POLCAL using the natural target<br /> and the surface scatterer (CR)<br />
  3. 3. Covariance model for forest<br />Forest data covariance is expressed by double bounce, surface scattering and volume scattering. <br />(S. R. Cloud, and E. Pottier,”A Review of Target Decomposition Theorems in Radar Polarimetry,” IEEE Trans. on Geoscience and Remote Sensing, Vol. 34, No. 2, March 1996, pp. 498-518)<br />Asummption:<br />
  4. 4. Covariance matrix model used for the forest<br />
  5. 5. Assumption of the Proposed method<br />AH<br />RH<br />SAR-P<br />Z<br />S<br />O<br />AV<br />RV<br />DT, DR1<br />DR2<br /><ul><li>No-radar reciprocity (TR modules used for PALSAR)
  6. 6. L-band backscattering from the forest consists of the volume scattering and double bounce (polarization dependent). (reflection symmetry: no-correlation between like and cross , i.e., <HH*HV*>=0), large data available.
  7. 7. Scattering Reciprocity (HV=VH) for the volume component
  8. 8. Neglect the HV in surface and double bounce
  9. 9. Use a surface scatterer (including CR) for parameter determination.
  10. 10. No Faraday rotation.
  11. 11. Calibrated Antennas used.</li></li></ul><li>Calibration model<br />Two step determinations of 9 parameters (7 complex, and 2 real) needs a trihedral corner reflector and forest region.<br />noise(n0)<br />f1 ,f2 ,fd, fv, x(|x| and qx)<br />d1, d2, d3, and d4<br />
  12. 12. First order terms (channel imbalances and forest parameters)<br />Solutions<br />Equations<br />Unknowns:F1,F2,q1,q2,fv,fd,x(x,qx)<br />
  13. 13. Second Order Terms (Crosstalk’s of distortion matrices)<br />Then, the solution is given by<br />
  14. 14. Noise estimation: determined by the following iterative operation<br />
  15. 15. Features of the proposed method<br /><ul><li>Determine the channel imbalances using the forest and the surface scatterer (First order calculation) using a decomposition model with two different volume covariance(gamma and rho).
  16. 16. Estimate the HH and VV double bounces and use them for estimating the cross talks (Second order calculation).
  17. 17. No ignorance of the higher order terms (needs iteration)
  18. 18. Ease of the Test site selection (In Amazon)</li></li></ul><li>Evaluations of assumptions<br />Amazon PALSAR data calibrated by Queagan method<br />
  19. 19. Reflection symmetry<br />Correlation coefficient<br />
  20. 20. Application of Freeman Durden model<br />fs << fd<br />
  21. 21. Ignorance of the surface scattering<br />Condition: <br />Freeman-Durden ignores HV of surface scattering<br /> This models ignores the surface scattering.<br /> Do these ignorance affect the parameter estimation?<br />These ignorance do not affect the parameter estimation.<br />
  22. 22. Conditions for obtaining the solutions<br />Necessity conditions<br />Boarder lines<br />
  23. 23. Determination of the forest covariance using the Amazon data<br />Min. : =0.325, =0.295<br />
  24. 24. Covariance Parameter and scattering model<br />
  25. 25.
  26. 26.
  27. 27. List of the data acquired at Amazon rainforest site<br />
  28. 28. Definition of the method<br />
  29. 29. Qeagan<br />
  30. 30. Temporal variation of the PolCAL parameters (Method-1”’)<br />
  31. 31. Cross talk measurements using CR and natural target<br />
  32. 32. Amplitude ratio and phase difference<br />
  33. 33. Uncal M-1 M-1’ M-1”<br />M-1”’ M-2 M-3 M-4<br />
  34. 34. corr(HH,VV)<br />corr(HV,VH)<br />Arg(HH,VV)<br />Arg(HV,VH)<br />
  35. 35. Fig. 19. Incidence angle dependence of the forest parameters estimated from the proposed PolCAL method. Within a 3-degree incidence angle range, two components, the volume scattering component, and the double-bounce component, are almost independent of the incidence angle. The amplitude ratio of the double-bounce component in HH to that in VV is nearly equal to 2.1.<br />
  36. 36. Comparison of the interferometric coherences in HH and VV<br />Fig. 22. Comparative display of repeat-pass SAR interferometric coherences of HH-HH in a) and VV-VV in b). The master and slave images were acquired on Oct. 20, 2006, and Sept. 6, 2006, respectively as 46 days separated in time and 193m separated in space (e.g., perpendicular baseline). Slightly dark stripes running vertically seem to be interferometric decorrelation due to ionospheric disturbances [29].<br />
  37. 37. Conclusions<br /><ul><li>A new polarimetric calibration method using the forest and surface scatterer (i.e., a corner reflector) was proposed.
  38. 38. 26 datasets for the Amazon test sites showed the good stability of the PolCAL parameters.
  39. 39. The calibrated polarimetric data showed the good polarimetric signature.
  40. 40. While two surface scatterers were compared, CR showed the great performance than the pond.
  41. 41. We will seek the other possible surface scatter.
  42. 42. Reflection reciprocity was confirmed for the forest region.</li></li></ul><li>Covariance matrix (4 x 4) and its notation<br />1) Distributed targets<br />a=1, f<1, c~e~|d|~1/3<br />d,b:complex<br />|b|<c&d&e because b contains double bounce.<br />All components to be subtracted by noise<br />Cross talks<br />2) Surface scatterer<br />Channel imbalances<br />
  43. 43. Surface Scatterer<br />Corner Reflector and Surface scatterer (Pond)<br />
  44. 44. Fig. 15. Temporal variation of the volume scattering component and the double-bounce scattering components of VV and HH. Double-bounce component of HH is nine times larger than that of VV. This calculation is obtained by Method-1”’.<br />
  45. 45.
  46. 46. 1) no-radar reciprocity<br />Problem Description<br />2) Reflection symmetry ?<br />7 unknowns (including )<br />10 measurements<br />4 measurements<br />x2<br />Scattering matrix<br />

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