Compact Polarimetry Potentials.ppt

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The document discusses compact polarimetry for SAR systems. It defines compact polarimetry as using one transmit polarization and two receive polarizations. This allows for larger swaths compared to full polarimetry. Calibration of a compact polarimetry system requires three reference targets and has fewer unknown parameters than a full polarimetry system. Simulations show compact polarimetry data can be accurately reconstructed from full polarimetry raw data. Analysis of biomass estimation using compact polarimetry data found errors similar to using actual full polarimetry polarization channels.

Technology

Overview ,[object Object],[object Object],[object Object],[object Object]

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[object Object],[object Object],[object Object],Background - Example with ALOS system Mode Swath Resolution Incidence angle HH 70km 10m 8° ~ 60° HH/HV or VV/VH (dual-pol) 70km 20m 8° ~ 60° Full polar (quad-pol) 30km 30m 8° ~ 30°

Background - Compact Polarimetry 1/2 ,[object Object],[object Object],[object Object],preserving f

[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Background - Compact Polarimetry 2/2 ,[object Object],[object Object],[object Object],[object Object]

Calibration – Full-pol system ,[object Object],[object Object],[object Object],[object Object],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.

Calibration – Compact-pol system ,[object Object],[object Object],[object Object],[object Object]

Simulated compact polarimetric data ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Example of raw data, range spectra HH {R;G;B}={HH;HV;VV}, SETHI data, L-band, Garons

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

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

Compact-pol - Process 2 / Process 2 FP data {R;G;B}={<|VV|²>;<|HV|²>;<|HH|²>} FP reconstructed {R;G;B}={<|VV|²>;<|HV|²>;<|HH|²>}

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

Biomass estimate – Nezer forest RMS error = 2.6 tons/ha (HV vs HV ) Polarization RMS error (tons/ha) quadratic regression RMS error (tons/ha) exponential regression HV 5.8 5.7 HV 6.2 6.5 RR 6.6 6.6 RH 12.2 12.8

Biomass map – Nezer forest 120 tons/ha 0

Biomass map – Nezer forest B HV B HV B RR 120 tons/ha 0 Measured biomass

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

Summary: systems implications ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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Compact Polarimetry Potentials.ppt

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

10.

11.

12.

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

14. 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 1 Coherence between both images

15. Compact-pol - Process 2 / Process 2 FP data {R;G;B}={<|VV|²>;<|HV|²>;<|HH|²>} FP reconstructed {R;G;B}={<|VV|²>;<|HV|²>;<|HH|²>}

16.

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

18. Biomass estimate – Nezer forest RMS error = 2.6 tons/ha (HV vs HV ) Polarization RMS error (tons/ha) quadratic regression RMS error (tons/ha) exponential regression HV 5.8 5.7 HV 6.2 6.5 RR 6.6 6.6 RH 12.2 12.8

19. Biomass map – Nezer forest 120 tons/ha 0

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

21. 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.

23. Thank you for your attention

Compact Polarimetry Potentials.ppt

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