fabrizio lombardini federico viviani - esa · 2014-06-13 · fabrizio lombardini. federico viviani....
TRANSCRIPT
Fabrizio LombardiniFederico Viviani
DIFF-TOMO OPENING OF THE URBAN SAR PIXEL: SINGLE-LOOK 4D AND NON-UNIFORM MOTION
“5D”
EXTENSIONS
Part of this work has been supported by ASI project n. I/065/09/0
University of Pisa
Recall
of
Tomographic
SAR techniques-
3D SAR Tomography-
4D Differential
Tomography
(3D +time)
3D /4D/”5D”
Tomographic
advances
for
urban
areas:
Full horizontal
resolution
Tomography-
Single-look
adaptive
Tomography, results
with
ERS data-
Tests
of
single-look model-based
Tomography
Full resolution
processing extensions
of
Differential
Tomography-
Adaptive
processing, first model-based
processing test-
First test of
adaptive
Diff-Tomo
for
non-uniform
motions
(5D)
First Tomographic
trial with
C-S data; Differential
Tomography
extensions
for
forest
applications
Conclusions
and future work
Outline
2
3D SAR TomographyFrom 2.5D imaging (InSAR) to full 3D imaging…Multibaseline (MB) technique for imaging (z profiling) of elevation-distributed scatterers (multiple “garbled” scatterers, semitransparent volume scattering layers)
MB array response to backscattering from height h
Spatial frequency related to the scatterer normal height s
Height profiling by elevation beam-steeringHowever…The limited
and irregular
baseline
distribution
causes
anomalous
height
sidelobe behaviour
and limited
resolution
Many solutions have been proposed to counteract these problems…
• Array interpolation (gap filling)+ Fourier processing (IBF) [DLR ‘00]
• Regularized inversion approach [CNR-IREA ‘02]
• Adaptive superresolution tomography, model-based superresolution
• Volumetric coherence-based inversion approach [Cloude ’07]
• Sector Interpolation [UniPi ’07]
• Compressive sensing [Univ. Naples ‘09]
Ground range
Height, hNormal height, s
Slant range
12
KBK
3
a
[Lombardini, TGARS’05]
Define a velocity-dependent temporal frequency: (D-InSAR)
2-D Fourier relationSpatial-temporal spectral estimation
Multitemporal multibaseline cmplx data
Cmplx amplitudeelevation-velocity
distribution
Sparse 2D: multi-ant. air./sat. cluster
& multi-pass (multistatic)
“1D” Curvilinear: plain multi-baseline by
multi-pass, airborne/sat. (monostatic)
2D support in 2D baseline-time plane deeply
exploited
2D elev.-vel. sidelobe challenge; handling by advanced spectral estimators
D-InSAR concept Tomo-SAR concept Conv. acquisition New processing
DiffDiff--TomoTomo**, it , it ““opensopens”” the SAR pixelthe SAR pixel extractingjoint height and dynamical information of superimposed moving
scatterers(“4D imaging”, 3D+time)
New output!
Define an elevation-dependent spatial frequency: (Tomo-SAR)
Differential SAR TomographyFrom 3.5D imaging (D-InSAR) to full 4D imaging…
* patent pending4
Advances of 3D Adaptive Tomography Adaptive Tomo: good superresolution/sidelobe reduction capabilities,
yet it requires coherent multilookingMoving from multilook to single-look processing…Full horizontal resolution is more desirable in urban remote sensing applications
SectorInterpolation
y
SOI
1 lookPreconditioner
Spatial filter
can operate at full horizontal resolution deterministic version for uniform data
Tomo image
Sidelobe reduction and height superresolutionat full range-azimuth resolution
No increased computational burden!
… and to single-look adaptive Differential Tomographic processing…the proposed solution can be extended to the framework of Differential Tomography 5
Real data results Single - look processing
Tomographic profiles from adaptive and model-based tomography
superresolutionmain lobe width gain=43.5
• ERS 1/2 dataset • K=43 tracks• total baseline 1500 m• time span 5 years• Rayleigh height res. 6.4 m
Rome, Cinecittà
Model based Tomography
Adaptive Tomography
Clipped amplitudes
6
( Fourier )
Superresolution separation gain up to 4
Real data results
Single - look processing
Superresolution capabilities of model-based tomography
Tomographic profiles from double scatterer cells(separation –wise ordered and aligned)
Rayleigh resolution limit (6.4m)
Δh=1.7 mΔh=10 m
• Identification of double scatterers with separation 3.7 times below the Rayleigh resolution limit
7
Single-look 4D Adaptive Differential Tomography
The new method extended to the framework of Diff-Tomo
Spatial-temporalInterpolation
PreconditionerSpatial-temporal filter
Diff-Tomo image
1 look
y
SOI
Cinecittà dataset : Height – Velocity PSF
Adaptive Diff-TomoFourier Processing
PSF (PSF)Amplitudes 8
Adaptive Diff-Tomo
Double scatterer cell
Adaptive Diff-Tomo
Single scatterer cell
Single-look Adaptive Differential Tomography
Double scatterer cell Double scatterer cell
Adaptive Diff-Tomo Adaptive Diff-Tomo
Elevation-velocity power distribution (3-th and 4-th dimensions)
Cinecittàdataset
First results …
9
• Identification of double scatterers below the Rayleigh resolution limit
Double scatterer cell
Model-Based Diff-Tomo
Single scatterer cell
Single-look Adaptive Differential Tomography
Model-Based Diff-Tomo
Elevation-velocity power distribution (3-th and 4-th dimensions)
Cinecittàdataset
First results …
9
“5D”
Diff-Tomo for non-uniform motionsExtension of adaptive Diff-Tomo
to extract different (average) accelerations of multiple scatterers Semi-parametric adaptive Diff-TomoExploits a model for non-uniform motions
Simulated analysis Cinecittà baseline pattern 1 steady scatterer 1 moving scatterer with linear uniform motion
- Initial velocity=2.8 mm/year- Velocity increment=5.6 mm/year
Semi-parametric Diff-Tomo output(after optimization over accelearation parameter)
Estimated (average) acceleration (5-th dimension)
“5D” focusing achieved!
the output is in the joint range-azimuth-elevation-
velocity-acceleration domain
Recovery of power loss due to non-uniform motion
Velocity Increment (m
m/year)
10
“5D”
Diff-Tomo for non-uniform motionsExtension of adaptive Diff-Tomo
to extract different (average) accelerations of multiple scatterers Semi-parametric adaptive Diff-TomoExploits a model for non-uniform motions
Simulated analysis Cinecittà baseline pattern 1 steady scatterer 1 moving scatterer with linear uniform motion
- Initial velocity=2.8 mm/year- Velocity increment=5.6 mm/year
Semi-parametric Diff-Tomo output(after optimization over accelearation parameter)
Estimated (average) acceleration (5-th dimension)
Velocity Increment (m
m/year)
10
“5D” focusing achieved!
the output is in the joint range-azimuth-elevation-
velocity-acceleration domain
Recovery of power loss due to non-uniform motion
“5D”
Diff-Tomo for non-uniform motionsExtension of adaptive Diff-Tomo
to extract different (average) accelerations of multiple scatterers Semi-parametric adaptive Diff-TomoExploits a model for non-uniform motions
Simulated analysis Cinecittà baseline pattern 1 steady scatterer 1 moving scatterer with linear uniform motion
- Initial velocity=2.8 mm/year- Velocity increment=5.6 mm/year
Estimated (average) acceleration (5-th dimension)
Velocity Increment (m
m/year)
Statistics
Steady Scatterer Moving Scatterer
Bias Initial Velocity (mm/yr)
0.2 0.1
RMSE Initial Velocity (mm/yr)
0.2 0.7
Bias Velocity Variation (mm/yr)
-1 -0.4
RMSE Velocity Variation (mm/yr)
0.2 0.9
10
Δv=5.4 mm/year
Δv=0.2 mm/year
Real data results
5D Diff-Tomo
1° scatterer
2° scatterer
Velocity Increment (m
m/year)
Cinecittàdataset
11
Double Scatterer cell
4D Diff-Tomo
11
Real data results
5D Diff-Tomo
1° scatterer
2° scatterer
∆v=-3 mm/yr
∆v= -3.3 mm/yr
Velocity Increment (m
m/year)
Cinecittàdataset
Double Scatterer cell
4D Diff-Tomo
First Tomographic trial with COSMO-SkyMed data
12
• COSMO – SkyMed dataset• K=26 tracks• total baseline 1000 m• time span 1 year
Naples
13
The Diff-Tomo framework can also be applied to volumetric scenarios:Novel functionalities for probing 3D forest structure and internal dynamics
Tomography robust to temporal decorrelation
Subcanopy ground subsidence monitoring
Coherence separation
0 0.2 0.4 0.6 0.8 1
0
10
20
30
40
50
Hei
ght [
m]
Normalized
Tomo MUSICDiff-Tomo Gen. MUSIC
Sensible mitigation of blurring effects from temporal
decorrelation
Decoupling of forest volume from ground scatterer:
subcanopy subsidence may be revealed
Different temporal decorrelation mechanisms can be identfied in the
same resolution cell
Robust tomographic profiles
Estimated ground subsidence
BioSAR P-band data set
Diff-Tomo :Temporal decorrelation signature
Height-velocity-temporal bandwidth“5D” functional
[Lombardini-Cai, PolInSAR ‘11]
[Lombardini-Cai, IGARSS ‘08]
13
The Diff-Tomo framework can also be applied to volumetric scenarios:Novel functionalities for probing 3D forest structure and internal dynamics
Tomography robust to temporal decorrelation
Subcanopy ground subsidence monitoring
Coherence separation
0 0.2 0.4 0.6 0.8 1
0
10
20
30
40
50
Hei
ght [
m]
Normalized
Tomo MUSICDiff-Tomo Gen. MUSIC
Sensible mitigation of blurring effects from temporal
decorrelation
Decoupling of forest volume from ground scatterer:
subcanopy subsidence may be revealed
Different temporal decorrelation mechanisms can be identfied in the
same resolution cell
Robust tomographic profiles
Estimated ground subsidence
BioSAR P-band data set
Diff-Tomo :Temporal decorrelation signature
Height-velocity-temporal bandwidth“5D” functional
See the poster this Tuesday afternoon!
[Lombardini-Cai, PolInSAR ‘11]
[Lombardini-Cai, IGARSS ‘08]
Conclusions
• Tomographic/Differential-Tomographic techniques are emerging methodologies for description and monitoring of garbled scattering urban areas
• 3D Multilook Tomography methods have been extended to the single look case
• Height superresolution
at full horizontal resolution has been achieved mantaining the low computational burden of adaptive and model-based Tomography
• This new single look processing has been extended to the framework of 4D Differential Tomography, both adaptive and model-based
• Diff-Tomo accounting for non-uniform motions: first encouraging results achieved of 5D adaptive Differential Tomographic imaging
Future works• Development of these Tomographic techniques for application to the COSMO-SkyMed data
• Expanding 4D/5D processing tests, and comparisons with other single look techniques
• Tuning of the Differential Tomography functionalities for forest applications14
Thanks for your attention…
15
Single-look 4D Adaptive Differential Tomography
Adaptive Diff-Tomo
Double scatterer cell
Adaptive Diff-Tomo
Single scatterer cell
16
Model-based Diff-Tomo
Single scatterer cell
First result of automated identification of scatterers through single-look adaptive differential tomography and data-domain fitting
17
First result of automated identification of scatterers through single-look adaptive differential tomography and data-domain fitting
18
“5D”
Diff-Tomo -
Real data results
5D Diff-Tomo
1° scatterer
2° scatterer
∆v=0.7 mm/yr
∆v= -
0.9 mm/yr
Velocity Increment (m
m/year)
Cinecittàdataset
19
4D Diff-Tomo
Diff-Tomo for non-uniform motions
• Simulated analysis• Two scatterers at same height and initial velocity• Possibility to resolve in the acceleration domain• “FULL” 5D
20