1. Model-based Polarimetric Decomposition using PolInSAR Coherence Si-Wei Chen, Motoyuki Sato Tohoku University, Japan chensw@cneas.tohoku.ac.jp sato@cneas.tohoku.ac.jp

3. Current model-based decompositions

4. Limitations

5. PolInSAR Coherence

6. Estimation and optimization

7. Proposed Decomposition

8. Adaptive volume scattering model

9. Comparative experiments

11. Full polarimetric information

12. Covariance matrix

13. Model-based decomposition

14. Better understanding the scattering mechanisms3 Color-code ALOS/PALSAR East Japan earthquake and tsunami After-event Pre-event Optical Image Decomposition ﹢ ﹢... = ﹢ Double Bounce Volume Scattering Single Bounce (PolSARpro tutorials)

16. ImprovementsHelix component Reflection symmetry assumption Nonnegative eigenvalues Negative power Deorientation Scattering mechanism ambiguity General volume model Inadaptive (A. Freeman, Y. Yamaguchi, W.M. Boerner, J. J. Van Zyl, J.S. Lee, Y. Q. Jin, M. Neumann, M. Arii and et al.)

18. For YamaguchiPauli Image Table I Averaged Backscattered Power (In dB)

20. Adaptive volume scattering model

21. Indirect modification of double- and single-bounce models

22. Balance the inputs and outputsUtilization of both Polarimetric and Interferometric information! 6

24. Current model-based decompositions

25. Limitations

26. PolInSAR Coherence

27. Estimation and optimization

28. Proposed Decomposition

29. Adaptive volume scattering model

30. Comparative experiments

32. Combination of PolSAR and InSAR

33. Covariance matrix

34. Coherence magnitude

35. Optimization8 (T. Xiong) Polarimetric Dependence PolInSAR

37. Signal-to-noise decorrelation

38. Baseline decorrelation

39. Processing decorrelation

40. Temporal decorrelation

41. Volume decorrelation

43. Close relationship to forest structures Potentially, the volume scattering can be modeled from it!

44. 10 PolInSAR Coherence Optical image Optimal 1 Optimal 2 Optimal 3

45. 11 PolInSAR Coherence Optimal 1 Optimal 2 Optimal 3

47. Current model-based decompositions

48. Limitations

49. PolInSAR Coherence

50. Estimation and optimization

51. Proposed Decomposition

52. Adaptive volume scattering model

53. Comparative experiments

59. Current model-based decompositions

60. Limitations

61. PolInSAR Coherence

62. Estimation and optimization

63. Proposed Decomposition

64. Adaptive volume scattering model

65. Comparative experiments

67. Test site: Oberpfaffenhofen, Germany

68. L-band

69. Data size : 1300×1200E-SAR (PolSARpro tutorials) Azimuth Optical image PolInSAR coherence RGB image HH, HV, VV Master track Pauli image HH-VV, HV, HH+VV

73. Pd Decomposition _ After deorientation Ps Pv Color-code Freeman-Durden Yamaguchi Proposed Optical Image More sensitive and better fit for diverse forest terrains! 22

75. Current model-based decompositions

76. Limitations

77. PolInSAR Coherence

78. Estimation and optimization

79. Proposed Decomposition

80. Adaptive volume scattering model

81. Comparative experiments

83. Thank you for your attention !

85. For YamaguchiPauli Image Table I Averaged Backscattering Power (In dB)

86. 27 PolInSAR Coherence Optical image HH-HH VV-VV HV-HV

87. 28 PolInSAR Coherence HH-HH VV-VV HV-HV

88. 29 Model Parameters Optical Image Optimal 3 Coherence

93. PolInSAR Coherence Optimal 1 Optimal 2 Optimal 3

94. PolInSAR Coherence Optimal 1 Optimal 2 Optimal 3

95. Decomposition _ Volume scattering contribution Optical Image Freeman-Durden Yamaguchi Proposed

96. 37 ALOS/PALSAR datasets Range Pauli Image ALOS/PALSAR Optical Image Azimuth Spatial baseline: 299m Temporal baseline: 46 days 2007-4-02 2007-05-18

97. 38 PolInSAR coherence _ H-V HH-HH VV-VV HV-HV

98. 39 PolInSAR coherence _ Optimal Opt 1 Opt 2 Opt 3

99. 40 PolInSAR coherence _ Histogram

100. 41 Decomposition _ After deorientation Pd Ps Pv Color-code Freeman-Durden Yamaguchi Proposed

101. 42 Pd Built-up region - I Ps Pv Color-code Optical image Freeman-Durden Proposed Yamaguchi

102. 43 Pd Built-up region - II Ps Pv Color-code Optical image Freeman-Durden Proposed Yamaguchi