1. SAR Imagery Algorithms
Simulated data
Real data
Conclusion and Future Work
Contribution of the polarimetric information in
order to discriminate target from interferences
subspaces. Application to FOPEN detection
with SAR processing 1
F.Briguia , L.Thirion-Lefevreb , G.Ginolhacc and P.Forsterc
a ISAE/University of Toulouse
b SONDRA/SUPELEC
c SATIE, Ens Cachan
1
Funded by the DGA
1/24 IGARSS 2011 July 2011

2. SAR Imagery Algorithms
Simulated data
Real data
Conclusion and Future Work
Context
Objective
Detection of a target embedded in a complex environment using SAR system
SAR (Synthetic Aperture Radar)
◮ airborne antenna
◮ monostatic configuration (“stop
◮ scene seen under different angles
and go“)
◮ synthetic antenna
2/24 IGARSS 2011 July 2011

3. SAR Imagery Algorithms
Simulated data
Real data
Conclusion and Future Work
Application
FoPen Detection (Foliage Penetration)
◮ Man-Made Target (MMT) located u200
z
in a forest u100
y
◮ P/L band: canopy is “transparent” m
u2
10 m
0.5
u1 0
Scattering attenuation but target u0
-10 m
detection still possible 95 m 115 m
x
Modeling
◮ Scatterers of interest
◮ Target → Deterministic scattering
◮ Tree trunks (interferences) → Deterministic scattering
◮ Others scatterers
◮ Branches, foliage → Random scattering
3/24 IGARSS 2011 July 2011

4. SAR Imagery Algorithms
Simulated data
Real data
Conclusion and Future Work
FoPen Detection
Classical SAR
No prior-knowledge on the scatterers → isotropic and white point scatterer model
Real data in VV of a truck and a trihedral in the Nezer
Simulated data in VV of a box in a forest of trunks
forest
Results
◮ Low response of the target → Target not detected
◮ High response of the forest → Many false alarms
4/24 IGARSS 2011 July 2011

5. SAR Imagery Algorithms
Simulated data
Real data
Conclusion and Future Work
FoPen Detection
Classical SAR
No prior-knowledge on the scatterers → isotropic and white point scatterer model
Real data in VV of a truck and a trihedral in the Nezer
Simulated data in VV of a box in a forest of trunks
forest
Results
◮ Low response of the target → Target not detected
◮ High response of the forest → Many false alarms
4/24 IGARSS 2011 July 2011

6. SAR Imagery Algorithms
Simulated data
Real data
Conclusion and Future Work
FoPen Detection
Classical SAR
No prior-knowledge on the scatterers → isotropic and white point scatterer model
Real data in VV of a truck and a trihedral in the Nezer
Simulated data in VV of a box in a forest of trunks
forest
Results
◮ Low response of the target → Target not detected
◮ High response of the forest → Many false alarms
4/24 IGARSS 2011 July 2011

7. SAR Imagery Algorithms
Simulated data
Real data
Conclusion and Future Work
New SAR processors
Approach
◮ To reconsider the SAR image generation by including prior-knowledge on the
scatterers of interest
◮ To generate one single SAR image
→ Use of subspace methods
Awareness of the scattering and polarimetric properties:
1. Of the target → To increase its detection
2. Of the interferences → To reduce false alarms
→
Only possible if the target and the interferences scattering have different properties
5/24 IGARSS 2011 July 2011

8. SAR Imagery Algorithms
Simulated data
Real data
Conclusion and Future Work
Outline
SAR Imagery Algorithms
FoPen Simulated data
Real data
Conclusion and Future Work
6/24 IGARSS 2011 July 2011

9. SAR Algorithms
SAR Imagery Algorithms
CSAR
Simulated data
SSDSAR
Real data
OBSAR
Conclusion and Future Work
OSISDSAR
Outline
SAR Imagery Algorithms
SAR Algorithms
Classical SAR (CSAR)
SSDSAR
OBSAR
OSISDSAR
FoPen Simulated data
Real data
Conclusion and Future Work
7/24 IGARSS 2011 July 2011

10. SAR Algorithms
SAR Imagery Algorithms
CSAR
Simulated data
SSDSAR
Real data
OBSAR
Conclusion and Future Work
OSISDSAR
SAR data configuration
SAR signal
Single Polarization p
Double Polarization
SAR signal zp ∈ CNK
SAR signal z ∈ C2NK
 
 
p  . 
z = . 
 .   . 
 . 

 . 

 
z=



 
 . 
. 
 
.

8/24 IGARSS 2011 July 2011

11. SAR Algorithms
SAR Imagery Algorithms
CSAR
Simulated data
SSDSAR
Real data
OBSAR
Conclusion and Future Work
OSISDSAR
SAR data configuration
◮ K time samples
SAR signal
Single Polarization p
Double Polarization
SAR signal zp ∈ CNK
SAR signal z ∈ C2NK
p
 
z1  
 
p
z =
 . 
.   . 
 .   . 

 . 

 
z=



 
 . 
. 
 
.

8/24 IGARSS 2011 July 2011

12. SAR Algorithms
SAR Imagery Algorithms
CSAR
Simulated data
SSDSAR
Real data
OBSAR
Conclusion and Future Work
OSISDSAR
SAR data configuration
◮ K time samples
◮ N antenna positions ui
SAR signal
Single Polarization p
Double Polarization
SAR signal zp ∈ CNK
SAR signal z ∈ C2NK
 p 
z1
 
. 
 
p
z =

.  .
. 
 
  . 
p
zN

 . 

 
z=



 
 . 
. 
 
.

8/24 IGARSS 2011 July 2011

13. SAR Algorithms
SAR Imagery Algorithms
CSAR
Simulated data
SSDSAR
Real data
OBSAR
Conclusion and Future Work
OSISDSAR
SAR data configuration
◮ K time samples
◮ N antenna positions ui
◮ Polarization: single VV (or HH) or
SAR signal
Single Polarization p
Double Polarization
SAR signal zp ∈ CNK
SAR signal z ∈ C2NK
 p 
z1
zHH
 
p

. 
 1
z =

.   . 
 .  
.

p .
 
zN 
 HH


 z
z= N


 
 
 . 
 . 
 . 
8/24 IGARSS 2011 July 2011

14. SAR Algorithms
SAR Imagery Algorithms
CSAR
Simulated data
SSDSAR
Real data
OBSAR
Conclusion and Future Work
OSISDSAR
SAR data configuration
◮ K time samples
◮ N antenna positions ui
◮ Polarization: single VV (or HH) or double (HH and VV)
SAR signal
Single Polarization p
Double Polarization
SAR signal zp ∈ CNK SAR signal z ∈ C2NK
 p 
z1
zHH
 
1
. 
 
p
z = .

. 
 
.  .
 

.
 
p  
zN  HH
 z


z= N

VV

 z1


 
 . 
 . 
 . 
zVV
N
8/24 IGARSS 2011 July 2011

15. SAR Algorithms
SAR Imagery Algorithms
CSAR
Simulated data
SSDSAR
Real data
OBSAR
Conclusion and Future Work
OSISDSAR
Image generation principle
For each pixel (x, y)
Computation of the SAR response of the model
Classical model
◮ White isotropic point scatterer response
Subspace models
◮ Canonical element responses for all its orientations
◮ Generation of the subspace
9/24 IGARSS 2011 July 2011

16. SAR Algorithms
SAR Imagery Algorithms
CSAR
Simulated data
SSDSAR
Real data
OBSAR
Conclusion and Future Work
OSISDSAR
Image generation principle
For each pixel (x, y)
Computation of the SAR response of the model
Classical model
◮ White isotropic point scatterer response
Subspace models
◮ Canonical element responses for all its orientations
◮ Generation of the subspace
Computation of the complex amplitude coefficient (or the coordinate vector)
◮ Least square estimation
9/24 IGARSS 2011 July 2011

17. SAR Algorithms
SAR Imagery Algorithms
CSAR
Simulated data
SSDSAR
Real data
OBSAR
Conclusion and Future Work
OSISDSAR
Image generation principle
For each pixel (x, y)
Computation of the SAR response of the model
Classical model
◮ White isotropic point scatterer response
Subspace models
◮ Canonical element responses for all its orientations
◮ Generation of the subspace
Computation of the complex amplitude coefficient (or the coordinate vector)
◮ Least square estimation
Intensity
◮ Square norm of the complex amplitude
9/24 IGARSS 2011 July 2011

18. SAR Algorithms
SAR Imagery Algorithms
CSAR
Simulated data
SSDSAR
Real data
OBSAR
Conclusion and Future Work
OSISDSAR
CSAR (Classical SAR)
Modeling
No prior knowledge on scatterers of interest.
White Isotropic point model rxy
SAR signal modeling
z = axy rxy + n
axy unknown complex amplitude, n complex white Gaussian noise of variance σ 2
Double polarization: 2 possible models
◮ trihedral scattering: rxy = r+
xy
◮ dihedral scattering: rxy = r−
xy
CSAR image intensity
Equivalence with images generated with
classical SAR processors (TDCA,
± r±† z
xy
2
Backprojection, RMA)
IC (x, y ) =
σ2
10/24 IGARSS 2011 July 2011

19. SAR Algorithms
SAR Imagery Algorithms
CSAR
Simulated data
SSDSAR
Real data
OBSAR
Conclusion and Future Work
OSISDSAR
SSDSAR (Signal Subspace Detector SAR)
Target modeling
Prior-knowledge: Target is made of a Set of Plates.
Target model: Low Rank Subspace Hxy generated from PC plates.
z z
z’ z’
α z"
β
y"=y’
y’
O α β
O y
y
α
β
x (b) x’ (c)
(a) x=x’
x"
Hxy : orthonormal basis of Hxy , λxy
unknown amplitude coordinate vector.
Signal SAR modeling Double polarization:
2 possible target subspaces
z = Hxy λxy + n ◮ trihedral scattering: Hxy = H+
xy
◮ dihedral scattering: Hxy = H−
xy
11/24 IGARSS 2011 July 2011

20. SAR Algorithms
SAR Imagery Algorithms
CSAR
Simulated data
SSDSAR
Real data
OBSAR
Conclusion and Future Work
OSISDSAR
SSDSAR (Signal Subspace Detector SAR)
`
R. Durand, G. Ginolhac, L. Thirion-Lefevre, and P. Forster, “New SAR processor based on matched subspace
detectors,” IEEE TAES, Jan 2009.
`
F. Brigui, L. Thirion-Lefevre, G. Ginolhac and P. Forster, “New polarimetric signal subspace detectors for SAR
processors,” CR Phys, Jan 2010.
z
Goal: Improvment of target detection.
PHz
SSDSAR image intensity <H>
H† z 2
xy
IS (x, y ) =
σ2
†
PHxy = Hxy Hxy : orthogonal projector into Hxy .
<J>
11/24 IGARSS 2011 July 2011

21. SAR Algorithms
SAR Imagery Algorithms
CSAR
Simulated data
SSDSAR
Real data
OBSAR
Conclusion and Future Work
OSISDSAR
OBSAR (Oblique SAR)
Interference modeling (Trunks)
Prior-knowledge: Trunks are dielectric cylinders lying over the ground.
Interference model: Low Rank Subspace Jxy generated from dielectric cylinders lying
over the ground.
z’=z
z z" γ
δ
O y’ O y"=y’
O δ γ
y γ
δ
x x’ x"
(a) (b) (c)
Signal SAR modeling
z = Hxy λxy + Jxy µxy + n
Jxy : orthonormal basis of Jxy , µxy unknown amplitude coordinate vector.
Double polarization: 1 possible interference subspace
12/24 IGARSS 2011 July 2011

22. SAR Algorithms
SAR Imagery Algorithms
CSAR
Simulated data
SSDSAR
Real data
OBSAR
Conclusion and Future Work
OSISDSAR
OBSAR (Oblique SAR)
`
F. Brigui, G. Ginolhac, L. Thirion-Lefevre, and P. Forster, “New SAR Algorithm based on Oblique Projection for
Interference Reduction,” IEEE TAES, submitted.
Goals:
◮ Increase of target detection.
◮ Reduce false alarms due to deterministic interferences.
z
OBSAR image intensity
EHSz
H† EHxy Jxy z
xy
2 <H>
IOB (x, y ) =
σ2
† †
EHxy Jxy = PHxy (I − Jxy (Jxy P⊥ Jxy )−1 Jxy P⊥ ):
H H
xy xy
oblique projector into Hxy along the direction
described by Jxy .
Oblique projection of z into Hxy <J>
12/24 IGARSS 2011 July 2011

23. SAR Algorithms
SAR Imagery Algorithms
CSAR
Simulated data
SSDSAR
Real data
OBSAR
Conclusion and Future Work
OSISDSAR
OSISDSAR (Orthogonal Interference Subspace Detector Processor)
Intensity IS Intensity II⊥
H† z 2 J′† z
xy
2
xy II⊥ (x, y ) =
IS (x, y ) = σ2
σ2
′† † †
Jxy = (Jxy P⊥ Jxy )−1 Jxy P⊥
H H
xy xy
z
z
PHz
<H>
<H>
T
J P Hz
<J>
<J>
13/24 IGARSS 2011 July 2011

24. SAR Algorithms
SAR Imagery Algorithms
CSAR
Simulated data
SSDSAR
Real data
OBSAR
Conclusion and Future Work
OSISDSAR
OSISDSAR (Orthogonal Interference Subspace Detector Processor)
`
F. Brigui, G. Ginolhac, L. Thirion-Lefevre, and P. Forster, “New SAR Algorithm based on Signal and Interference
Subspace Models,” IEEE GRS, To submit.
Goals:
◮ Increase of target detection.
◮ Reduce false alarms due to deterministic interferences.
OSISDSAR image intensity
IS (x, y ) I (x, y )
ISI⊥ (x, y ) = − I⊥
ES EI
ES = xy IS (x, y) and EI = xy II⊥ (x, y): normalization parameters
13/24 IGARSS 2011 July 2011

25. SAR Imagery Algorithms
Configuration
Simulated data
Single Polarization
Real data
Double Polarization
Conclusion and Future Work
Outline
SAR Imagery Algorithms
FoPen Simulated data
Configuration
Single Polarization (VV)
Double Polarization
Real data
Conclusion and Future Work
14/24 IGARSS 2011 July 2011

26. SAR Imagery Algorithms
Configuration
Simulated data
Single Polarization
Real data
Double Polarization
Conclusion and Future Work
Configuration
Radar parameters
u200
◮ 200 positions ui
z
y ◮ chirp with frequency bandwidth
u100
B = 100Mhz with f0 = 400MHz
m
u2
10 m
(P band)
0.5
u1 0
u0
-10 m Target and Interference
x ◮ target: metallic box (2m x 1.5m x
95 m 115 m
1) over a PC ground (Feko)
◮ interferences: tree trunks
(COSMO)
Interference subspaces
Signal subspaces
◮ Canonical element: dielectric
◮ Canonical element: PC plate
cylinder (11m × 20cm) over the
(2m × 1m)
ground
◮ Ranks: 10
◮ Ranks: 10
15/24 IGARSS 2011 July 2011

27. SAR Imagery Algorithms
Configuration
Simulated data
Single Polarization
Real data
Double Polarization
Conclusion and Future Work
VV polarization
SSDSAR (ρ = 3.5 dB)
cible
Imax
ρ = 10 log( interf
)
Imax
16/24 IGARSS 2011 July 2011

28. SAR Imagery Algorithms
Configuration
Simulated data
Single Polarization
Real data
Double Polarization
Conclusion and Future Work
CSAR (ρ = −2.5 dB) SSDSAR (ρ = 3.5 dB)
OBSAR (ρ = 3.5 dB) OSISDSAR (ρ = 3.5 dB)
16/24 IGARSS 2011 July 2011

29. SAR Imagery Algorithms
Configuration
Simulated data
Single Polarization
Real data
Double Polarization
Conclusion and Future Work
Analysis
◮ H VV et J VV too “close”
◮ Trunks response rejection not
possible
OBSAR (ρ = 3.5 dB) OSISDSAR (ρ = 3.5 dB)
16/24 IGARSS 2011 July 2011

30. SAR Imagery Algorithms
Configuration
Simulated data
Single Polarization
Real data
Double Polarization
Conclusion and Future Work
Double polarization (dihedral case)
CSAR (ρ = −3.5 dB) SSDSAR (ρ = 1.8 dB)
Dihedral case
17/24 IGARSS 2011 July 2011

31. SAR Imagery Algorithms
Configuration
Simulated data
Single Polarization
Real data
Double Polarization
Conclusion and Future Work
CSAR (ρ = −3.5 dB) SSDSAR (ρ = 1.8 dB)
OBSAR (ρ = 3.6 dB) OSISDSAR (ρ = 4.5 dB)
17/24 IGARSS 2011 July 2011

32. SAR Imagery Algorithms
Configuration
Simulated data
Single Polarization
Real data
Double Polarization
Conclusion and Future Work
Analysis
◮ H et J enough “disjoint”
◮ Trunks response rejection
◮ OBSAR: robust to the target
modeling
◮ OSISDSAR: robust to the
interference modeling.
OBSAR (ρ = 3.6 dB) OSISDSAR (ρ = 4.5 dB)
17/24 IGARSS 2011 July 2011

33. SAR Imagery Algorithms
Configuration
Simulated data
Single Polarization
Real data
Double Polarization
Conclusion and Future Work
Outline
SAR Imagery Algorithms
FoPen Simulated data
Real data
Configuration
Single Polarization (VV)
Double Polarization
Conclusion and Future Work
18/24 IGARSS 2011 July 2011

34. SAR Imagery Algorithms
Configuration
Simulated data
Single Polarization
Real data
Double Polarization
Conclusion and Future Work
Configuration Radar parameters
◮ chirp with frequency
bandwidth B = 70Mhz
Pyla 2004 (ONERA) - Nezer forest with f0 = 435MHz
u
un Target and Interference
y
◮ MMT: Truck
u2
Nezer forest ◮ Other target: Trihedral
z 225 m (5520,150)
u1 ◮ Interferences: pine forest
u0
100 m (5584,126)
Interference subspaces
0 5480 m 5620 m x
◮ Canonical element:
Signal subspaces dielectric cylinder
(11m × 20cm) over the
◮ Canonical element: PC plate (4m × 2m) ground
◮ Ranks: 10 ◮ Ranks: 10
19/24 IGARSS 2011 July 2011

35. SAR Imagery Algorithms
Configuration
Simulated data
Single Polarization
Real data
Double Polarization
Conclusion and Future Work
VV polarization
CSAR
SSDSAR (ρc = 0.8 dB / ρt = 1.5 dB)
OBSAR
OSISDSAR
20/24 IGARSS 2011 July 2011

36. SAR Imagery Algorithms
Configuration
Simulated data
Single Polarization
Real data
Double Polarization
Conclusion and Future Work
VV polarization
SSDSAR (ρc = 0.8 dB / ρt = 1.5 dB) CSAR (ρc = 1 dB / ρt = 1.5 dB)
20/24 IGARSS 2011 July 2011

37. SAR Imagery Algorithms
Configuration
Simulated data
Single Polarization
Real data
Double Polarization
Conclusion and Future Work
VV polarization
SSDSAR (ρc = 0.8 dB / ρt = 1.5 dB) OBSAR (ρc = 0.8 dB / ρt = 1.5 dB)
20/24 IGARSS 2011 July 2011

38. SAR Imagery Algorithms
Configuration
Simulated data
Single Polarization
Real data
Double Polarization
Conclusion and Future Work
VV polarization
SSDSAR (ρc = 0.8 dB / ρt = 1.5 dB) OSISDSAR (ρc = 1, 3 dB / ρt = 1.3 dB)
20/24 IGARSS 2011 July 2011

39. SAR Imagery Algorithms
Configuration
Simulated data
Single Polarization
Real data
Double Polarization
Conclusion and Future Work
Double polarization (dihedral case)
SSDSAR (ρ = 1.7 dB)
Dihedral case
21/24 IGARSS 2011 July 2011

40. SAR Imagery Algorithms
Configuration
Simulated data
Single Polarization
Real data
Double Polarization
Conclusion and Future Work
Double polarization (dihedral case)
CSAR
SSDSAR (ρ = 1.7 dB)
OBSAR
OSISDSAR
21/24 IGARSS 2011 July 2011

41. SAR Imagery Algorithms
Configuration
Simulated data
Single Polarization
Real data
Double Polarization
Conclusion and Future Work
Double polarization (dihedral case)
SSDSAR (ρ = 1.7 dB) CSAR (ρ = 0.7 dB)
21/24 IGARSS 2011 July 2011

42. SAR Imagery Algorithms
Configuration
Simulated data
Single Polarization
Real data
Double Polarization
Conclusion and Future Work
Double polarization (dihedral case)
SSDSAR (ρ = 1.7 dB) OBSAR (ρ = 2.3 dB)
21/24 IGARSS 2011 July 2011

43. SAR Imagery Algorithms
Configuration
Simulated data
Single Polarization
Real data
Double Polarization
Conclusion and Future Work
Double polarization (dihedral case)
SSDSAR (ρ = 1.7 dB) OSISDSAR (ρ = 3.7 dB)
21/24 IGARSS 2011 July 2011

44. SAR Imagery Algorithms
Simulated data
Real data
Conclusion and Future Work
Outline
SAR Imagery Algorithms
FoPen Simulated data
Real data
Conclusion and Future Work
22/24 IGARSS 2011 July 2011

45. SAR Imagery Algorithms
Simulated data
Real data
Conclusion and Future Work
Conclusion
◮ Subspace Methods: target and interferences scattering taken into account for
the SAR image processing
◮ Double Polarization: reduction on false alarms due to the interferences possible
Future Work
◮ Awardeness of the canopy attenuation effets
◮ Cross-polarization (HV, VH)
23/24 IGARSS 2011 July 2011

46. SAR Imagery Algorithms
Simulated data
Real data
Conclusion and Future Work
Thank you for your attention!
Questions?
24/24 IGARSS 2011 July 2011

47. SAR Imagery Algorithms
Simulated data
Real data
Conclusion and Future Work
Single polarization HH
CSAR SSDSAR
25/24 IGARSS 2011 July 2011

48. SAR Imagery Algorithms
Simulated data
Real data
Conclusion and Future Work
CSAR SSDSAR
OBSAR OSISDSAR
25/24 IGARSS 2011 July 2011

49. SAR Imagery Algorithms
Simulated data
Real data
Conclusion and Future Work
Single polarization HH (real data)
CSAR
SSDSAR
OBSAR
OSISDSAR
26/24 IGARSS 2011 July 2011

50. SAR Imagery Algorithms
Simulated data
Real data
Conclusion and Future Work
Single polarization HH (real data)
SSDSAR CSAR
26/24 IGARSS 2011 July 2011

51. SAR Imagery Algorithms
Simulated data
Real data
Conclusion and Future Work
Single polarization HH (real data)
SSDSAR OBSAR
26/24 IGARSS 2011 July 2011

52. SAR Imagery Algorithms
Simulated data
Real data
Conclusion and Future Work
Single polarization HH (real data)
SSDSAR OSISDSAR
26/24 IGARSS 2011 July 2011