Compact Polarimetry Potentials.ppt

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
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°
•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)
1
2
11 1
2 2
HH HV HH HV
VH VV VH VV
S S S jSk
k
S S S jSk j
-æ ö æ öæ ö æ ö
= = = ç ÷ ç ÷ç ÷ ç ÷
--è øè ø è ø è ø

6. IGARSS’11
"p/4-mode potentials: reconstruction of the PolSAR information (1)
–Iterative algorithm based on:
•Reflection symmetry
•Coherence between co-polarized channels
"p/2-mode potentials: avoid Faraday rotation in transmission (2)
–Transmit a circular polarized wave
–Show results about the reconstruction of the PolSAR information from p/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-d method based on Stokes parameters
• J-C. Souyris, P. Imbo, R. FjØrtoft, S. Mingot and J-S. Lee, Compact Polarimetry Based on Symmetry Properties of Geophysical
Media: The p/4 Mode, IEEE Transactions on Geoscience and Remote Sensing, vol. 43, no. 3, March 2005.
• 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.
• 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.
• 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.
( ),
j
R T
M A r e D R SR D N
j
q
W W
= +
( ) N
fSS
SS
f
erAM
VVHV
VHHHj
+
÷
÷
ø
ö
ç
ç
è
æ
÷
÷
ø
ö
ç
ç
è
æ
WW-
WW
÷
÷
ø
ö
ç
ç
è
æ
÷
÷
ø
ö
ç
ç
è
æ
WW-
WW
÷
÷
ø
ö
ç
ç
è
æ
=
24
3
11
2 1
cossin
sincos
cossin
sincos1
,
d
d
d
d
q
j
1 1 1 1
R T
S R D MD R
- - - -
W W
=
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 d
1
, d
2
, W, f
1
( )
11
,
2
j
R T
M A r e D R SR D N
j
j
q
W W
æ ö
= +
ç ÷
-è ø
( )
11
,
2
j
R
M A r e D R SR N
j
j
q
W W
æ ö
= +
ç ÷
-è ø
1 1
11
2
R T
R D M SR D
j
- -
W W
æ ö
=
ç ÷
-è ø
% %

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
( ) ( )
( ) ( )
( )
( )
÷
÷
ø
ö
ç
ç
è
æ
+-
+-
+
÷
÷
ø
ö
ç
ç
è
æ
W+W-W-W
W+W-W-W
@
W-
12
1
1212
11
cossinsincos
cossinsincos
2
1
fjS
jS
Ae
fjSfS
jSS
eAeM
HV
HVj
VVHH
VVHHjj
d
d
dd
dd
jj
00
0 0
1
ln 2
2
D DD T
RV RVRH
D DD D
RHRV RH
M MMj A
j j
A MM M
-
æ ö
æ ö
ç ÷
ç ÷W = - -
ç ÷ ç ÷
ç ÷
è ø
è ø
0
0
2 *
2 1 1 1 1
D
RH
D
RV
M
f f jf
M
d d= - -
0 0
0 01
2
D D D D
RV RV RV RH
D DD D
RH RVRH RH
M M M Mj
M MM M
d
æ ö
ç ÷= -
ç ÷
è ø
1
2
T D
RH RH
T D
RV RV
j
f
M M
M M
=
-
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
HVHHRH jSSM -=
Processed data
Raw data
Process 1
rawHV
S
pro
HV
S
raw
HH
S
Processing
(corrections,
antenna beam, etc.)
Processing
(corrections,
antenna beam, etc.)
Calibration:
M
RHpro
raw
HH
S
proHH
S
÷
÷
ø
ö
ç
ç
è
æ
-=
propropro HVHHRH
S
HHA
HVA
jSHHAk
_
_
_
Hilbert
transform
Processing
(corrections,
antenna beam, etc.)
Calibration:
M
RH
Process 2
raw
HVS
rawrawraw HVHHRH S
HHA
HVA
jSk
_
_
-=
raw
RHRH
kHHAk ´=_
rawHVjS-
rawHH
S

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
}
proRHrawRHMMr
0 1Coherence between both images

15. IGARSS’11
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. 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
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
RMS error = 2.6 tons/ha (HV vs HV)

19. IGARSS’11
Biomass map – Nezer forest
0.1274
205.8
HV
HV
B e
s
=
0.1465
178.01
HV
HV
B e
s
=
0.1626
53.142
RR
RR
B e
s
=
120 tons/ha
0

20. IGARSS’11
Biomass map – Nezer forest
B
HV
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