contribution_of_the_polarimetric_information.pdf
TRANSCRIPT
![Page 1: Contribution_of_the_polarimetric_information.pdf](https://reader035.vdocument.in/reader035/viewer/2022081404/558588e3d8b42ab2148b509c/html5/thumbnails/1.jpg)
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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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SAR Imagery AlgorithmsSimulated data
Real dataConclusion and Future Work
Thank you for your attention!
Questions?
24/24 IGARSS 2011 July 2011
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SAR Imagery AlgorithmsSimulated data
Real dataConclusion and Future Work
Single polarization HH
CSAR SSDSAR
25/24 IGARSS 2011 July 2011
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SAR Imagery AlgorithmsSimulated data
Real dataConclusion and Future Work
CSAR SSDSAR
OBSAR OSISDSAR
25/24 IGARSS 2011 July 2011
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SAR Imagery AlgorithmsSimulated data
Real dataConclusion and Future Work
Single polarization HH (real data)
SSDSAR
CSAR
OBSAR
OSISDSAR
26/24 IGARSS 2011 July 2011
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SAR Imagery AlgorithmsSimulated data
Real dataConclusion and Future Work
Single polarization HH (real data)
SSDSAR CSAR
26/24 IGARSS 2011 July 2011
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SAR Imagery AlgorithmsSimulated data
Real dataConclusion and Future Work
Single polarization HH (real data)
SSDSAR OBSAR
26/24 IGARSS 2011 July 2011
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SAR Imagery AlgorithmsSimulated data
Real dataConclusion and Future Work
Single polarization HH (real data)
SSDSAR OSISDSAR
26/24 IGARSS 2011 July 2011