TU2.L09 - POTENTIALS OF A COMPACT POLARIMETRIC SAR SYSTEM
Upcoming SlideShare
Loading in...5
×
 

TU2.L09 - POTENTIALS OF A COMPACT POLARIMETRIC SAR SYSTEM

on

  • 1,306 views

 

Statistics

Views

Total Views
1,306
Views on SlideShare
1,304
Embed Views
2

Actions

Likes
0
Downloads
123
Comments
0

1 Embed 2

http://www.grss-ieee.org 2

Accessibility

Categories

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment
  • These thresholds have been chosen and validated over several scenes.

TU2.L09 - POTENTIALS OF A COMPACT POLARIMETRIC SAR SYSTEM TU2.L09 - POTENTIALS OF A COMPACT POLARIMETRIC SAR SYSTEM Presentation Transcript

  • POTENTIALS OF A COMPACT POLARIMETRIC SAR SYSTEM My-Linh Truong-Loï, ONERA/CNES/IETR Pascale Dubois-Fernandez, ONERA Eric Pottier, IETR – UMR CNRS 6164 Anthony Freeman, JPL Jean-Claude Souyris, CNES
  • Objectives Missions dedicated to global environmental change monitoring Low frequency radar Ionosphere interaction  Faraday rotation Compact polarimetry Transmitting a circular polarized wave No depolarization anymore Receiving two orthogonal polarized waves Estimate Faraday rotation High power of penetration Dual-pol system Large swath Dual-pol system
  • Outline
    • Background – compact polarimetry
    • Faraday rotation estimate
      • Bare soil surfaces selection
      • Faraday rotation estimate
    • PolSAR applications
      • Soil moisture estimate
      • Biomass estimate
    • PolInSAR applications
      • Forest height estimate
    • Calibration
  • Background - Compact Polarimetry 1/2
    • π /4 mode: one transmission at 45° and two orthogonal receptions (linear H & V, circular right & left,…)
    • π /2 mode: one circular transmission and two orthogonal receptions (linear H & V, circular right & left,…)
    • Hybrid polarity : particular case of π /2 : one circular transmission and two linear receptions (H&V)
    • π /4-mode potentials: reconstruction of the PolSAR information (1)
      • Iterative algorithm based on:
        • Reflection symmetry
        • Coherence between co-polarized channels
    • π /2-mode potentials: avoid Faraday rotation in transmission (2)
      • Transmit a circular polarized wave
      • Adaptation of the reconstruction of the PolSAR information is possible
    • Hybrid polarity potentials: decomposition of natural targets (3)
      • m - δ method based on Stokes parameters
    Background - Compact Polarimetry 2/2 (1) J-C. Souyris, P. Imbo, R. FjØrtoft, S. Mingot and J-S. Lee, Compact Polarimetry Based on Symmetry Properties of Geophysical Media : The π/4 Mode , IEEE Transactions on Geoscience and Remote Sensing, vol. 43, no. 3, Mars 2005. (2) P. C. Dubois-Fernandez, J-C. Souyris, S. Angelliaume et F. Garestier, The Compact Polarimetry Alternative for Spaceborne SAR at Low Frequency , IEEE Transactions on Geoscience and Remote Sensing, vol. 46, no. 10, Octobre 2008. (3) R. K. Raney, Hybrid-Polarity SAR Architecture , IEEE Transactions on Geoscience and Remote Sensing, vol. 45, no. 11, Novembre 2007.
  • Conclusion
      • π /2 mode is required for low frequency spaceborne radar
      • How to estimate/correct for Faraday rotation in reception ?
        •  Bare soil surfaces are first required
  • The conformity coefficient Double-bounce Volume Surface S HV ~ 0 S HH , S VV correlated Arg<S HH S VV *> ≈ 180° -1< μ < t 2 S HV is significant S HH , S VV less correlated t 2 < μ <t 1 S HV ~ 0 S HH , S VV correlated Arg<S HH S VV *> ≈ 0 t 1 < μ <1
    • This coefficient
      • Can be shown to be FR independent
      • can be used with CP data as well as FP data
      • discriminates 3 different types of scattering
    M-L. Truong-Loï, A. Freeman, P. C. Dubois-Fernandez and E. Pottier, Estimation of Soil Moisture and Faraday Rotation From Bare Surfaces Using Compact Polarimetry , IEEE Transactions on Geoscience and Remote Sensing, vol. 47, no. 11, Novembre 2009.
    • Conformity coefficient versus Cloude-Pottier
    • Confusion matrices
    Conformity coefficient thresholds S V DB -1 -0.2 0.32 1
  • Conformity coefficient vs Cloude-Pottier and Freeman-Durden classifications, RAMSES P-band data over St Germain d’Esteuil, France S. R. Cloude et E. Pottier, A Review of Target Decomposition Theorems in Radar Polarimetry , IEEE Transactions on Geoscience and Remote Sensing, vol. 34, no. 2, Mars 1996. A. Freeman et S. L. Durden, A Three-Component Scattering Model for Polarimetric SAR Data , IEEE Transactions on Geoscience and Remote Sensing, vol. 36, no. 3, Mai 1998.
    • P v > 0.6 P d & P v > 0.3 P s
    • P s >P d
    • P d
    Conformity coefficient Cloude-Pottier classification Freeman-Durden classification Volume Double-bounce Surface
  • Conclusion
      • π /2 mode is required for low frequency spaceborne radar
      • Introduction of a parameter : the conformity coefficient
        • Is FR-independent
        • Allows to distinguish 3 types of scattering
        • We can select bare soil surfaces with compact-pol data
        •  Is it now possible to correct for FR with compact-pol data ?
  • Flow diagram of the process   e FP calibrated data (Ramses, PALSAR) Select bare surfaces (by using the conformity coefficient) Estimate the Faraday rotation Synthesized CP data (add Faraday rotation)
    • (2)
    • (3)
  • Faraday rotation estimate over PALSAR L-band data Full polarimetric data Over bare surfaces μ >0.2  Compact polarimetric data Thanks to the ASF for providing the data.
  • Conclusion
      • π /2 mode is required for low frequency spaceborne radar
      • Introduction of a parameter : the conformity coefficient
        • Is FR-independent
        • Allows to distinguish 3 types of scattering
      • Estimation of FR over bare surfaces identified by the conformity coefficient
      • Now FR is corrected
      •  Can we use this type of data ?
          • Let’s see PolSAR applications
            • 1) soil moisture estimate
  • FP vs CP signatures S HV ~ 0 over bare soil surfaces Window size : 7x7 σ Hh / σ Rh σ Vv / σ Rv Stdv: <2dB σ Hh (dB) σ Rh (dB) 10 10 -30 -30 10 10 σ Vv (dB) σ Rv (dB)
  • FP vs CP soil moisture – Dubois et al. algorithm Stdv : 4% CP/FP soil moisture Estimated soil moisture CP & FP vs ground truth Stdv : 2% RAMSES L-band data, Le Moulin du Fâ, France AIRSAR/WASHITA L-band data over Chickasha area P. C. Dubois, J. van Zyl et T. Engman, Measuring Soil Moisture with Imaging Radars, IEEE Transactions on Geoscience and Remote Sensing, vol. 33, no. 4, pp. 915-926, Juillet 1995. FP soil moisture 0 100% 100% CP soil moisture
  • Conclusion
      • π /2 mode is required for low frequency spaceborne radar
      • Introduction of a parameter : the conformity coefficient
        • Is FR-independent
        • Allows to distinguish 3 types of scattering
      • Estimation of FR over bare surfaces identified by the conformity coefficient
      • Compact PolSAR applications:
        • Estimate of soil moisture is possible over bare surfaces selected by μ and using Dubois et al. Algorithm – RMS error of 2% over AIRSAR L-band data
      •  Can we use this type of data ?
          • Let’s see PolSAR applications
            • 1) soil moisture estimate
            • 2) biomass estimate (FP, FP reconstructed from CP and CP)
  • Backscattering coefficients vs measured biomass – RAMSES P-band data over Nezer forest (HV) (RR) (RH)
  • Biomass estimate vs in situ measurements – Nezer forest RMS error=6.25 tons/ha RMS error=6.63 tons/ha RMS error=12.2 tons/ha RMS error=5.8 tons/ha
  • Biomass map – Nezer forest Measured biomass 0 120 tons/ha
  • ROI biomass map – Nezer forest Measured biomass B HV B HV’ B RR
  • Conclusion
      • π /2 mode is required for low frequency spaceborne radar
      • Introduction of a parameter : the conformity coefficient
        • Is FR-independent
        • Allows to distinguish 3 types of scattering
      • Estimation of FR over bare surfaces identified by the conformity coefficient
      • Compact PolSAR applications:
        • Estimate of soil moisture is possible over bare surfaces selected by μ and using Dubois et al. Algorithm – RMS error of 2% over AIRSAR L-band data
        • Quantify biomass with HV’ (RMS error : 6,25 tons/ha) and RR (6,63 tons/ha) instead of HV (5,8 tons/ha)
      • Add interferometry concept  compact-PolInSAR potentials
  • Compact-PolInSAR vs full-PolInSAR
    • Compact-PolInSAR (C-PolInSAR) information is represented by a 4x4 matrix
    • Full-PolInSAR (F-PolInSAR) information is represented by a 6x6 matrix
  • PolInSAR applications – vegetation height estimate
    • Flynn et al. algorithm
    T. Flynn, M. Tabb et R. Carande, Estimation of Coherence Region Shapes in Polarimetric SAR Interferometry , AIRSAR Workshop, Mars 2002.
  • Phase centers height (FP & CP) Phase centers height FP Phase centers height CP Nezer forest, P-band
  • Vegetation height – Nezer forest P-band S.R. Cloude and K.P. Papathanassiou, A 3-Stage Inversion Process for Polarimetric SAR Interferometry , IEE Proceedings Radar, Sonar & Navigation, vol. 150, no. 3, June 2003. RMS : 1m, Bias : 0,4m (FP) RMS : 1,1m, Bias : 0,4m (CP) RMS : 2,45m, Bias : -1,8m (FP) RMS : 2,5m, Bias : -1,78m (CP)
  • Conclusion
      • π /2 mode is required for low frequency spaceborne radar
      • Introduction of a parameter : the conformity coefficient
        • Is FR-independent
        • Allows to distinguish 3 types of scattering
      • Estimation of FR over bare surfaces identified by the conformity coefficient
      • Compact PolSAR applications:
        • Estimate of soil moisture is possible over bare surfaces selected by μ and using Dubois et al. Algorithm – RMS error of 2% over AIRSAR L-band data
        • Quantify biomass with HV’ (RMS error : 6,25 tons/ha) and RR (6,63 tons/ha) instead of HV (5,8 tons/ha)
      • Compact PolInSAR applications:
        • C-PolInSAR coherence region < F-PolInSAR coherence region
        • C-PolInSAR coherence region F-PolInSAR coherence region
      • System implications  Calibration
  • Calibration of a CP system
    • CP system:
    • System has to be perfect before transmission because it is not possible to correct afterwards
    • With a perfect transmission  4 unknowns δ 1 , δ 2 , Ω , f 1
    α ~ 1 β ~ -jδ f 2 ~1
  • Calibration of a CP system
    • Dihedral at 0°
    • Dihedral at 45°
    • Expansion of
    • Trihedral at 0°
  • Conclusion
      • π /2 mode is required for low frequency spaceborne radar
      • Introduction of a parameter : the conformity coefficient
        • Is FR-independent
        • Allows to distinguish 3 types of scattering
      • Estimation of FR over bare surfaces identified by the conformity coefficient
      • Compact PolSAR applications:
        • Estimate of soil moisture is possible over bare surfaces selected by μ and using Dubois et al. Algorithm – RMS error of 2% over AIRSAR L-band data
        • Quantify biomass with HV’ (RMS error : 6,25 tons/ha) and RR (6,63 tons/ha) instead of HV (5,8 tons/ha)
      • Compact PolInSAR applications:
        • C-PolInSAR coherence region < F-PolInSAR coherence region
        • C-PolInSAR coherence region F-PolInSAR coherence region
      • Calibration
        • Require a perfect transmission
        • Suggest 3 external targets to calibrate a CP system