SAR Imagery AlgorithmsSimulated data

Real dataConclusion and Future Work

Contribution of the polarimetric information inorder to discriminate target from interferencessubspaces. Application to FOPEN detection

with SAR processing 1

F.Briguia, L.Thirion-Lefevreb, G.Ginolhacc and P.Forsterc

aISAE/University of Toulouse

bSONDRA/SUPELEC

cSATIE, Ens Cachan

1Funded by the DGA1/24 IGARSS 2011 July 2011

SAR Imagery AlgorithmsSimulated data

Real dataConclusion 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

and go“)◮ synthetic antenna

◮ scene seen under different angles

2/24 IGARSS 2011 July 2011

SAR Imagery AlgorithmsSimulated data

Real dataConclusion and Future Work

Application

FoPen Detection (Foliage Penetration)

◮ Man-Made Target (MMT) locatedin a forest

◮ P/L band: canopy is “transparent”

Scattering attenuation but target

detection still possible

0

zy

x

u0

u1

u100

u200

0.5m

u2

95 m 115 m

-10 m

10 m

Modeling

◮ Scatterers of interest◮ Target → Deterministic scattering◮ Tree trunks (interferences) → Deterministic scattering

◮ Others scatterers◮ Branches, foliage → Random scattering

3/24 IGARSS 2011 July 2011

SAR Imagery AlgorithmsSimulated data

Real dataConclusion and Future Work

FoPen Detection

Classical SAR

No prior-knowledge on the scatterers → isotropic and white point scatterer model

Simulated data in VV of a box in a forest of trunksReal data in VV of a truck and a trihedral in the Nezerforest

Results

◮ Low response of the target → Target not detected◮ High response of the forest → Many false alarms

4/24 IGARSS 2011 July 2011

SAR Imagery AlgorithmsSimulated data

Real dataConclusion and Future Work

FoPen Detection

Classical SAR

No prior-knowledge on the scatterers → isotropic and white point scatterer model

Simulated data in VV of a box in a forest of trunksReal data in VV of a truck and a trihedral in the Nezerforest

Results

◮ Low response of the target → Target not detected◮ High response of the forest → Many false alarms

4/24 IGARSS 2011 July 2011

SAR Imagery AlgorithmsSimulated data

Real dataConclusion and Future Work

FoPen Detection

Classical SAR

No prior-knowledge on the scatterers → isotropic and white point scatterer model

Simulated data in VV of a box in a forest of trunksReal data in VV of a truck and a trihedral in the Nezerforest

Results

◮ Low response of the target → Target not detected◮ High response of the forest → Many false alarms

4/24 IGARSS 2011 July 2011

SAR Imagery AlgorithmsSimulated data

Real dataConclusion and Future Work

New SAR processors

Approach

◮ To reconsider the SAR image generation by including prior-knowledge on thescatterers 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

SAR Imagery AlgorithmsSimulated data

Real dataConclusion and Future Work

Outline

SAR Imagery Algorithms

FoPen Simulated data

Real data

Conclusion and Future Work

6/24 IGARSS 2011 July 2011

SAR Imagery AlgorithmsSimulated data

Real dataConclusion and Future Work

SAR AlgorithmsCSARSSDSAROBSAROSISDSAR

Outline

SAR Imagery AlgorithmsSAR AlgorithmsClassical SAR (CSAR)SSDSAROBSAROSISDSAR

FoPen Simulated data

Real data

Conclusion and Future Work

7/24 IGARSS 2011 July 2011

SAR Imagery AlgorithmsSimulated data

Real dataConclusion and Future Work

SAR AlgorithmsCSARSSDSAROBSAROSISDSAR

SAR data configuration

SAR signal

Single Polarization p

SAR signal zp ∈ CNK

zp=

.

.

.

Double Polarization

SAR signal z ∈ C2NK

z =

.

.

.

.

.

.

8/24 IGARSS 2011 July 2011

SAR Imagery AlgorithmsSimulated data

Real dataConclusion and Future Work

SAR AlgorithmsCSARSSDSAROBSAROSISDSAR

SAR data configuration

◮ K time samples

SAR signal

Single Polarization p

SAR signal zp ∈ CNK

zp=

zp1...

Double Polarization

SAR signal z ∈ C2NK

z =

.

.

.

.

.

.

8/24 IGARSS 2011 July 2011

SAR Imagery AlgorithmsSimulated data

Real dataConclusion and Future Work

SAR AlgorithmsCSARSSDSAROBSAROSISDSAR

SAR data configuration

◮ K time samples◮ N antenna positions ui

SAR signal

Single Polarization p

SAR signal zp ∈ CNK

zp=

zp1...

zpN

Double Polarization

SAR signal z ∈ C2NK

z =

.

.

.

.

.

.

8/24 IGARSS 2011 July 2011

SAR Imagery AlgorithmsSimulated data

Real dataConclusion and Future Work

SAR AlgorithmsCSARSSDSAROBSAROSISDSAR

SAR data configuration

◮ K time samples◮ N antenna positions ui

◮ Polarization: single VV (or HH) or

SAR signal

Single Polarization p

SAR signal zp ∈ CNK

zp=

zp1...

zpN

Double Polarization

SAR signal z ∈ C2NK

z =

zHH1...

zHHN

.

.

.

8/24 IGARSS 2011 July 2011

SAR Imagery AlgorithmsSimulated data

Real dataConclusion and Future Work

SAR AlgorithmsCSARSSDSAROBSAROSISDSAR

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

SAR signal zp ∈ CNK

zp=

zp1...

zpN

Double Polarization

SAR signal z ∈ C2NK

z =

zHH1...

zHHN

zVV1...

zVVN

8/24 IGARSS 2011 July 2011

SAR Imagery AlgorithmsSimulated data

Real dataConclusion and Future Work

SAR AlgorithmsCSARSSDSAROBSAROSISDSAR

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

SAR Imagery AlgorithmsSimulated data

Real dataConclusion and Future Work

SAR AlgorithmsCSARSSDSAROBSAROSISDSAR

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

SAR Imagery AlgorithmsSimulated data

Real dataConclusion and Future Work

SAR AlgorithmsCSARSSDSAROBSAROSISDSAR

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

SAR Imagery AlgorithmsSimulated data

Real dataConclusion and Future Work

SAR AlgorithmsCSARSSDSAROBSAROSISDSAR

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 σ2

Double polarization: 2 possible models◮ trihedral scattering: rxy = r+xy

◮ dihedral scattering: rxy = r−xy

CSAR image intensity

I±C (x , y) =‖r±†

xy z‖2

σ2

Equivalence with images generated withclassical SAR processors (TDCA,Backprojection, RMA)

10/24 IGARSS 2011 July 2011

SAR Imagery AlgorithmsSimulated data

Real dataConclusion and Future Work

SAR AlgorithmsCSARSSDSAROBSAROSISDSAR

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.

x=x’

y’

z’

αy

z

y

z

x

O O

z’

x’

β

x"

z"

y"=y’

α

α

β

β

(c)(b)(a)

Signal SAR modeling

z = Hxy λxy + n

Hxy : orthonormal basis of 〈Hxy 〉, λxyunknown amplitude coordinate vector.Double polarization:2 possible target subspaces

◮ trihedral scattering: Hxy = H+xy

◮ dihedral scattering: Hxy = H−xy

11/24 IGARSS 2011 July 2011

SAR Imagery AlgorithmsSimulated data

Real dataConclusion and Future Work

SAR AlgorithmsCSARSSDSAROBSAROSISDSAR

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.

Goal: Improvment of target detection.

SSDSAR image intensity

IS(x , y) =‖H†

xy z‖2

σ2

PHxy = Hxy H†xy : orthogonal projector into 〈Hxy 〉.

< H >

< J >

P zH

z

11/24 IGARSS 2011 July 2011

SAR Imagery AlgorithmsSimulated data

Real dataConclusion and Future Work

SAR AlgorithmsCSARSSDSAROBSAROSISDSAR

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 lyingover the ground.

x’

y’

z’=z

y

z

x

O

δ

δ

δ γ

γ

γ

x"

z"

y"=y’O O

(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

SAR Imagery AlgorithmsSimulated data

Real dataConclusion and Future Work

SAR AlgorithmsCSARSSDSAROBSAROSISDSAR

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.

OBSAR image intensity

IOB(x , y) =‖H†

xy EHxy Jxy z‖2

σ2

EHxy Jxy = PHxy (I − Jxy (J†xy P⊥Hxy

Jxy )−1J†xy P⊥Hxy

):

oblique projector into 〈Hxy 〉 along the directiondescribed by 〈Jxy 〉.

Oblique projection of z into 〈Hxy 〉

< H >

< J >

z

HSE z

12/24 IGARSS 2011 July 2011

SAR Imagery AlgorithmsSimulated data

Real dataConclusion and Future Work

SAR AlgorithmsCSARSSDSAROBSAROSISDSAR

OSISDSAR (Orthogonal Interference Subspace Detector Processor)

Intensity IS

IS(x , y) =‖H†

xy z‖2

σ2

< H >

< J >

P zH

z

Intensity II⊥

II⊥(x , y) =‖J′†

xy z‖2

σ2

J′†xy = (J†xy P⊥Hxy

Jxy )−1J†xy P⊥Hxy

< H >

< J >

z

J P zH

T

13/24 IGARSS 2011 July 2011

SAR Imagery AlgorithmsSimulated data

Real dataConclusion and Future Work

SAR AlgorithmsCSARSSDSAROBSAROSISDSAR

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

ISI⊥(x , y) =IS(x , y)

ES−

II⊥(x , y)

EI

ES =∑

xy IS(x, y) and EI =∑

xy II⊥(x, y): normalization parameters

13/24 IGARSS 2011 July 2011

SAR Imagery AlgorithmsSimulated data

Real dataConclusion and Future Work

ConfigurationSingle PolarizationDouble Polarization

Outline

SAR Imagery Algorithms

FoPen Simulated dataConfigurationSingle Polarization (VV)Double Polarization

Real data

Conclusion and Future Work

14/24 IGARSS 2011 July 2011

SAR Imagery AlgorithmsSimulated data

Real dataConclusion and Future Work

ConfigurationSingle PolarizationDouble Polarization

Configuration

0

zy

x

u0

u1

u100

u200

0.5m

u2

95 m 115 m

-10 m

10 m

Interference subspaces

◮ Canonical element: dielectriccylinder (11m × 20cm) over theground

◮ Ranks: 10

Radar parameters

◮ 200 positions ui

◮ chirp with frequency bandwidthB = 100Mhz with f0 = 400MHz(P band)

Target and Interference

◮ target: metallic box (2m x 1.5m x1) over a PC ground (Feko)

◮ interferences: tree trunks(COSMO)

Signal subspaces

◮ Canonical element: PC plate(2m × 1m)

◮ Ranks: 10

15/24 IGARSS 2011 July 2011

SAR Imagery AlgorithmsSimulated data

Real dataConclusion and Future Work

ConfigurationSingle PolarizationDouble Polarization

VV polarization

ρ = 10 log(Iciblemax

I interfmax

)

SSDSAR (ρ = 3.5 dB)

16/24 IGARSS 2011 July 2011

SAR Imagery AlgorithmsSimulated data

Real dataConclusion and Future Work

ConfigurationSingle PolarizationDouble Polarization

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

SAR Imagery AlgorithmsSimulated data

Real dataConclusion and Future Work

ConfigurationSingle PolarizationDouble Polarization

Analysis

◮ 〈HVV 〉 et 〈JVV 〉 too “close”◮ Trunks response rejection not

possible

OBSAR (ρ = 3.5 dB) OSISDSAR (ρ = 3.5 dB)

16/24 IGARSS 2011 July 2011

SAR Imagery AlgorithmsSimulated data

Real dataConclusion and Future Work

ConfigurationSingle PolarizationDouble Polarization

Double polarization (dihedral case)

CSAR (ρ = −3.5 dB) SSDSAR (ρ = 1.8 dB)

Dihedral case

17/24 IGARSS 2011 July 2011

SAR Imagery AlgorithmsSimulated data

Real dataConclusion and Future Work

ConfigurationSingle PolarizationDouble Polarization

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

SAR Imagery AlgorithmsSimulated data

Real dataConclusion and Future Work

ConfigurationSingle PolarizationDouble Polarization

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

SAR Imagery AlgorithmsSimulated data

Real dataConclusion and Future Work

ConfigurationSingle PolarizationDouble Polarization

Outline

SAR Imagery Algorithms

FoPen Simulated data

Real dataConfigurationSingle Polarization (VV)Double Polarization

Conclusion and Future Work

18/24 IGARSS 2011 July 2011

SAR Imagery AlgorithmsSimulated data

Real dataConclusion and Future Work

ConfigurationSingle PolarizationDouble Polarization

Configuration

Pyla 2004 (ONERA) - Nezer forest

0

z

y

x

u0

u1

un

u2

5480 m 5620 m

100 m

225 m

Nezer forest

u

(5520,150)

(5584,126)

Signal subspaces

◮ Canonical element: PC plate (4m × 2m)◮ Ranks: 10

Radar parameters

◮ chirp with frequencybandwidth B = 70Mhzwith f0 = 435MHz

Target and Interference

◮ MMT: Truck◮ Other target: Trihedral◮ Interferences: pine forest

Interference subspaces

◮ Canonical element:dielectric cylinder(11m × 20cm) over theground

◮ Ranks: 10

19/24 IGARSS 2011 July 2011

SAR Imagery AlgorithmsSimulated data

Real dataConclusion and Future Work

ConfigurationSingle PolarizationDouble Polarization

VV polarization

SSDSAR (ρc = 0.8 dB / ρt = 1.5 dB)

CSAR

OBSAR

OSISDSAR

20/24 IGARSS 2011 July 2011

SAR Imagery AlgorithmsSimulated data

Real dataConclusion and Future Work

ConfigurationSingle PolarizationDouble Polarization

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

SAR Imagery AlgorithmsSimulated data

Real dataConclusion and Future Work

ConfigurationSingle PolarizationDouble Polarization

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

SAR Imagery AlgorithmsSimulated data

Real dataConclusion and Future Work

ConfigurationSingle PolarizationDouble Polarization

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

SAR Imagery AlgorithmsSimulated data

Real dataConclusion and Future Work

ConfigurationSingle PolarizationDouble Polarization

Double polarization (dihedral case)

SSDSAR (ρ = 1.7 dB)

Dihedral case

21/24 IGARSS 2011 July 2011

SAR Imagery AlgorithmsSimulated data

Real dataConclusion and Future Work

ConfigurationSingle PolarizationDouble Polarization

Double polarization (dihedral case)

SSDSAR (ρ = 1.7 dB)

CSAR

OBSAR

OSISDSAR

21/24 IGARSS 2011 July 2011

SAR Imagery AlgorithmsSimulated data

Real dataConclusion and Future Work

ConfigurationSingle PolarizationDouble Polarization

Double polarization (dihedral case)

SSDSAR (ρ = 1.7 dB) CSAR (ρ = 0.7 dB)

21/24 IGARSS 2011 July 2011

SAR Imagery AlgorithmsSimulated data

Real dataConclusion and Future Work

ConfigurationSingle PolarizationDouble Polarization

Double polarization (dihedral case)

SSDSAR (ρ = 1.7 dB) OBSAR (ρ = 2.3 dB)

21/24 IGARSS 2011 July 2011

SAR Imagery AlgorithmsSimulated data

Real dataConclusion and Future Work

ConfigurationSingle PolarizationDouble Polarization

Double polarization (dihedral case)

SSDSAR (ρ = 1.7 dB) OSISDSAR (ρ = 3.7 dB)

21/24 IGARSS 2011 July 2011

SAR Imagery AlgorithmsSimulated data

Real dataConclusion and Future Work

Outline

SAR Imagery Algorithms

FoPen Simulated data

Real data

Conclusion and Future Work

22/24 IGARSS 2011 July 2011

SAR Imagery AlgorithmsSimulated data

Real dataConclusion and Future Work

Conclusion

◮ Subspace Methods: target and interferences scattering taken into account forthe 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

SAR Imagery AlgorithmsSimulated data

Real dataConclusion and Future Work

Thank you for your attention!

Questions?

24/24 IGARSS 2011 July 2011

SAR Imagery AlgorithmsSimulated data

Real dataConclusion and Future Work

Single polarization HH

CSAR SSDSAR

25/24 IGARSS 2011 July 2011

SAR Imagery AlgorithmsSimulated data

Real dataConclusion and Future Work

CSAR SSDSAR

OBSAR OSISDSAR

25/24 IGARSS 2011 July 2011

SAR Imagery AlgorithmsSimulated data

Real dataConclusion and Future Work

Single polarization HH (real data)

SSDSAR

CSAR

OBSAR

OSISDSAR

26/24 IGARSS 2011 July 2011

SAR Imagery AlgorithmsSimulated data

Real dataConclusion and Future Work

Single polarization HH (real data)

SSDSAR CSAR

26/24 IGARSS 2011 July 2011

SAR Imagery AlgorithmsSimulated data

Real dataConclusion and Future Work

Single polarization HH (real data)

SSDSAR OBSAR

26/24 IGARSS 2011 July 2011

SAR Imagery AlgorithmsSimulated data

Real dataConclusion and Future Work

Single polarization HH (real data)

SSDSAR OSISDSAR

26/24 IGARSS 2011 July 2011