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IGARSS’11 Compact Polarimetry Potentials My-Linh Truong-Loï, Jet Propulsion Laboratory / California Institue of Technology Eric Pottier, IETR, UMR CNRS.

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Presentation on theme: "IGARSS’11 Compact Polarimetry Potentials My-Linh Truong-Loï, Jet Propulsion Laboratory / California Institue of Technology Eric Pottier, IETR, UMR CNRS."— Presentation transcript:

1 IGARSS’11 Compact Polarimetry Potentials My-Linh Truong-Loï, Jet Propulsion Laboratory / California Institue of Technology Eric Pottier, IETR, UMR CNRS 6164 Pascale Dubois-Fernandez, ONERA

2 IGARSS’11 Overview Definition of compact polarimetry mode Calibration of a compact-pol system Simulation of compact-pol data from full-pol raw data Estimation of biomass with compact-pol data

3 IGARSS’11 Compact polarimetry –1 polarization on transmit –2 polarizations on receive What is the best polarization on transmit? What are the best polarizations on receive? How do we analyze the data? –Calibration –Faraday Rotation –Geophysical parameter estimation Issues

4 IGARSS’11 ModeSwathResolution Incidence angle HH70km10m8° ~ 60° HH/HV or VV/VH (dual-pol) 70km20m8° ~ 60° Full polar (quad-pol) 30km30m8° ~ 30° Single polarisation  large swath and larger incidence angle range Full polarisation  added characterisation Compact polarisation  full investigation of the dual-pol alternative Background - Example with ALOS system

5 IGARSS’11 Background - Compact Polarimetry 1/2 π /4 mode: one transmission at 45° and two coherent polarizations in reception (linear H & V, circular right & left,…) π /2 mode: one circular transmission and two coherent polarizations in reception (linear H & V, circular right & left,…) Hybrid polarity : particular case of π /2 : one circular transmission and two coherent linear polarizations in reception (H&V)

6 IGARSS’11  /4-mode potentials: reconstruction of the PolSAR information (1) –Iterative algorithm based on: Reflection symmetry Coherence between co-polarized channels  /2-mode potentials: avoid Faraday rotation in transmission (2) –Transmit a circular polarized wave –Show results about the reconstruction of the PolSAR information from  /2 mode –Applications possible (3) : Faraday rotation estimate Soil moisture estimate Classification using the conformity coefficient Hybrid polarity potentials: decomposition of natural targets (4) –m -  method based on Stokes parameters (1)J-C. Souyris, P. Imbo, R. FjØrtoft, S. Mingot and J-S. Lee, Compact Polarimetry Based on Symmetry Properties of Geophysical Media: The  /4 Mode, IEEE Transactions on Geoscience and Remote Sensing, vol. 43, no. 3, March 2005. (2)P. C. Dubois-Fernandez, J-C. Souyris, S. Angelliaume and F. Garestier, The Compact Polarimetry Alternative for Spaceborne SAR at Low Frequency, IEEE Transactions on Geoscience and Remote Sensing, vol. 46, no. 10, October 2008. (3)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, Nov. 2009. (4)R. K. Raney, Hybrid-Polarity SAR Architecture, IEEE Transactions on Geoscience and Remote Sensing, vol. 45, no. 11, November 2007. Background - Compact Polarimetry 2/2

7 IGARSS’11 Overview Definition of compact polarimetry mode Calibration of a compact-pol system Simulation of compact-pol data from full-pol raw data Estimation of biomass with compact-pol data

8 IGARSS’11 Calibration – Full-pol system Full-pol system calibration : 7 unknowns δ 1, δ 2, δ 3, δ 4, Ω, f 1, f 2 The S matrix can be recovered: Distorsions can be retrieved with measures over known targets: –Trihedral, dihedral, transponder, natural targets, etc. A. Freeman et T. Ainsworth, Calibration of longer wavelength polarimetric SARs, Proceedings of EUSAR 2008, Friedrishafen, Allemagne, June 2008. S. Quegan, A Unified Algorithm for Phase and Cross-Talk Calibration of Polarimetric Data – Theory and Observations, IEEE Transactions on Geoscience and Remote Sensing, vol. 32, no. 1, pp. 89-99, January 1994. J. J. van Zyl, Calibration of Polarimetric Radar Images Using Only Image Parameters and Trihedral Corner Reflector Responses, IEEE Transactions on Geoscience and Remote Sensing, vol. 28, no. 3, pp. 337-348, May 1990.

9 IGARSS’11 Calibration – Compact-pol system Compact polarimetric system: The transmission defects cannot be corrected a posteriori System needs to be of high quality before transmission With a high-quality transmission  4 unknowns  1,  2, , f 1

10 IGARSS’11 Compact polarisation –3 reference targets are necessary Dihedral @ 0° Dihedral @ 45° Trihedral Full polarisation –More unknowns –But less targets are required –Natural targets can be used –Acquisition of both HV and VH Calibration – Compact-pol system

11 IGARSS’11 Overview Definition of compact polarimetry mode Calibration of a compact-pol system Simulation of compact-pol data from full-pol raw data Estimation of biomass with compact-pol data

12 IGARSS’11 Simulated compact polarimetric data Simulation of CP data is necessary How do we proceed? –Two options: From raw data From processed data Comparison between the two approaches {R;G;B}={HH;HV;VV}, SETHI data, L-band, Garons Example of raw data, range spectra HH

13 IGARSS’11 Building compact polarimetric data Processed data Raw data Process 1 Processing (corrections, antenna beam, etc.) Calibration : M RHpro Hilbert transform Processing (corrections, antenna beam, etc.) Calibration: M RH Process 2

14 IGARSS’11 Building CP data - Process 1 / Process 2 Image of CP data from FP raw data, {R ;G;B}={ M Rh +M Rv ;M Rh ;M Rv } Image of CP data from FP processed data, {R ;G ;B}={ M Rh_pro +M Rv_pro ;M Rh_pro ;M Rv_pro } 0 1Coherence between both images

15 IGARSS’11 Compact-pol - Process 2 / Process 2 FP data {R;G;B}={ ; ; } FP reconstructed {R;G;B}={ ; ; }

16 IGARSS’11 Overview Definition of compact polarimetry mode Calibration of a compact-pol system Simulation of compact-pol data from full-pol raw data Estimation of biomass with compact-pol data

17 IGARSS’11 Backscattering coefficients and biomass – RAMSES P- band data over Nezer forest (HV) (RR) (RH) (HV)

18 IGARSS’11 Biomass estimate – Nezer forest PolarizationRMS error (tons/ha) quadratic regression RMS error (tons/ha) exponential regression HV5.85.7 HV6.26.5 RR6.6 RH12.212.8 RMS error = 2.6 tons/ha (HV vs HV)

19 IGARSS’11 Biomass map – Nezer forest 120 tons/ha 0

20 IGARSS’11 Biomass map – Nezer forest B HV B RR 120 tons/ha 0 Measured biomass

21 IGARSS’11 Biomass estimate with HV regression RMS error=20.1 tons/ha Bias=19.5 tons/ha Using the HV regression as a reference, computation of the biomass with HV backscattering coefficient

22 IGARSS’11 Summary: systems implications Compact-pol allows –To acquire larger swath (versus FP) –To access wider incidence angle range (versus FP) –To avoid Faraday rotation in transmission (versus DP) Calibration –A solution with 3 external targets Raw data –Equivalence between CP from FP raw data and from FP processed data Biomass estimate –FP: RMS error for HV: 5.8 tons/ha –CP: RMS error for HV reconstructed: 6.3 tons/ha –CP: RMS error for RR: 6.6 tons/ha

23 IGARSS’11 Thank you for your attention

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