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DIFF-TOMO OPPORTUNITIES FOR LONG
WAVELENGTH SATELLITE SYSTEMS
Fabrizio Lombardini1,2,Federico Viviani1,2
1
University of Pisa, Dept. of Information Engineering, Pisa, Italy
2 CNIT-RaSS National Laboratory, Pisa, Italy
Outline
 Introduction to Differential SAR Tomography (4D imaging)
 4D Diff-Tomo for urban areas
 Tomographic open issues in forest scenarios
 Multidimensional (4 to 5D) imaging of forest areas:
simulated L-band, real P-band E-SAR, and new quick Diff-Tomo analyses and results
• Diff-Tomo space-time signatures of decorrelating forest scatterers
• Tomography robust to temporal decorrelation through Diff-Tomo, baseline-time patterns
• Vertical separation of long-term temporal decorrelation mechanisms, large scale
• First short-term height-varying temporal decorrelation direct radar measurements
 Conclusions
1
4D Differential SAR Tomography
From 3D Tomo to 4D imaging…
 D-InSAR concept
 Tomo-SAR concept
 Conv. MB/Mpass acquisition
 New processing
[Lombardini, TGARS’05]
Diff-Tomo*,
“opens” the SAR pixel extracting
joint height and dynamical information of
superimposed moving scatterers
(“4D imaging”, 3D+time)
* UniPI Italian Patent,
European Patent pending
2
4D Differential SAR Tomography
From 3D Tomo to 4D imaging…
 D-InSAR concept
 Tomo-SAR concept
 Conv. MB/Mpass acquisition
 New processing
This is like a (spaceborne)
“dynamic radar scanner”
for Earth Observation
[Lombardini, TGARS’05]
F. Lombardini, “Differential Tomography: A New Framework for SAR
Interferometry”, IEEE TGARS, 43(1), 2005.
2
4D Differential SAR Tomography
From 3D Tomo to 4D imaging…
 D-InSAR concept
 Tomo-SAR concept
 Conv. MB/Mpass acquisition
 New processing
This is like a (spaceborne)
“dynamic radar scanner”
for Earth Observation
Define an elevation-dependent spatial frequency:
(Tomo-SAR)
Define a velocity-dependent temporal frequency:
(D-InSAR)
New output!
Acquisition patterns
2-D Fourier relation
Spatial-temporal spectral estimation
Multitemporal
multibaseline cmplx
data
Cmplx amplitude
elevation-velocity
distribution
Double scatterers
Multistatic
(SAOCOM CS,
Tandem-L)
Monostatic
(Sentinel-1,
BIOMASS,
NISAR, etc.)
ERS-1/2 data
F. Lombardini, M. Pardini, “Superresolution Differential Tomography:
Experiments on Identification of Multiple Scatterers in Spaceborne
SAR Data”, IEEE TGARS, 50(4), 2012.
2
4D Differential SAR Tomography
From 3D Tomo to 4D imaging…
 D-InSAR concept
 Tomo-SAR concept
 Conv. MB/Mpass acquisition
 New processing
This is like a (spaceborne)
“dynamic radar scanner”
for Earth Observation
Define an elevation-dependent spatial frequency:
(Tomo-SAR)
Define a velocity-dependent temporal frequency:
(D-InSAR)
New output!
Acquisition patterns
2-D Fourier relation
Spatial-temporal spectral estimation
Multitemporal
multibaseline cmplx
data
Cmplx amplitude
elevation-velocity
distribution
 2D support in 2D baseline-time plane deeply
exploited
Multistatic
(SAOCOM CS,
Tandem-L)
Monostatic
(Sentinel-1,
BIOMASS,
NISAR, etc.)
 2D elev.-vel. sidelobe
challenge; handling by
advanced spectral estimators
2
Multidimensional Diff-Tomo for urban applications
Single-look light burden 4D adaptive processing
Double Scatterers (dominant)
Single Scatterers
• C-band ERS 1/2 dataset
• K=43 tracks
• total baseline 1500 m
• time span 5 years
• Rayleigh height res. 6.4 m
• Fourier velocity res. 5.5 mm/yr
• Velocities are zero steered
for reference
Tuscolana quarter
σ = 1 mm/yr (single)
σ = 1.3 mm/yr (double)
+40% additional info
4D model-based superresolution Diff-Tomo heights
CSK data provided
by ASI
• COSMO-SkyMed dataset
ASI project n.
I/065/09/0
• K =28 tracks
• total baseline 1200 m
• Rayleigh height res. 6 m
• time span 1 year
Full
axonometric
reconstruction
of San Paolo
Stadium,
Naples
4D urban
monitoring
investigation
worth for
SAOCOM
F. Lombardini, F. Cai, D. Pasculli, “Spaceborne 3-D SAR Tomography
for Analyzing Garbled Urban Scenarios: Single-look Superresolution
Advances and Experiments," IEEE JSTARS, 6(2), 2013.
3
Tomographic open issues in forest scenarios
• Elevation blurring problems from temporal decorrelation in Tomo-SAR and Pol-InSAR
Ideal MB acquisition
Temporal decorrelating scenario
Acquisition Time
NASA-JPL, ESA and DLR recognized this as a possible limiting factor for the operational development of SAR Tomography
(forest scatterers and spaceborne monostatic acquisitions)
Also companion satellites can be impacted for second-long lag time
• Studies of Tomo-SAR blurring and investigation of robust processing solutions
• Stratified temporal coherence analysis necessary, blurring origins are local
Phenomenological investigations important of both height-varying long-term and
short-term (companion satellites) forest decorrelation
4
4D space-time signatures of forest decorrelation
Temporal perturbations
of a scattering component
temporal harmonic distribution
Temp. freq. does not merely code velocity anymore
Diff-Tomo
processing
Temporal spectrum
signatures of temporal decorrelation
can be detected!
New vision in SAR interferometry…
This also allows avoiding
misinterpretation of temporal
perturbations in the spatial
spectral estimation
F. Lombardini, F. Cai, “Temporal Decorrelation-Robust SAR
Tomography," IEEE TGARS, 52 (9), 2014.
5
4D space-time signatures of forest decorrelation
Remningstorp forest site
Mild temporal decorrelation
• DLR’s E-SAR (ESA project BIOSAR), P-band, 9 tracks
• Baseline span: 80 m, height Rayleigh resolution 28 m
• Time span: 2 months, temp. freq. Fourier resolution 0.5 phase cycles/month
Non-parametric analysis of a forested cell – Real data proof of concept of
space-time decorrelation signatures
Adaptive Diff-Tomo frame
• HV pol.
Normalized adaptive Diff-Tomo frame
Canopy
Ground
5
4D space-time signatures of forest decorrelation
Remningstorp forest site
Mild temporal decorrelation
• DLR’s E-SAR (ESA project BIOSAR), P-band, 9 tracks
• Baseline span: 80 m, height Rayleigh resolution 28 m
• Time span: 2 months, temp. freq. Fourier resolution 0.5 phase cycles/month
Non-parametric analysis of a forested cell – Real data proof of concept of
space-time decorrelation signatures
Adaptive Diff-Tomo frame
• HV pol.
Normalized adaptive Diff-Tomo frame
Canopy
Canopy
Ground
Ground
Canopy scatterer detected with a wider temporal frequency bandwidth w.r.t. ground!
5
Robust Tomography – L-band simulated analysis
Robust extraction of forest height in temporal decorrelating scenarios
Example of robust Tomography capabilities
Parametric 4D+ Diff-Tomo processing vs. model-based 3D
Robust 3D via Diff-Tomo
Misinterpretation avoided of
temporal perturbations in the
spatial spectral estimation
(temporal bandwidths as a
nuisance)
Probability of resolution vs. long-term
canopy decorrelation
∆h = 0.6 Rayleigh r.u.
Probability of resolution vs. long-term
canopy decorrelation
∆h = 0.6 Rayleigh r.u.
τ c  6 ÷ 11 revisit times
Simulated analysis
L-band forest scenario
monostatic monotonic
acquisition pattern
Simulated analysis
L-band forest scenario
monostatic b-t sparse
acquisition pattern
(10 passes)
(10 passes)
F. Lombardini, F. Cai, “Temporal Decorrelation-Robust SAR
Tomography," IEEE TGARS, available in early access, 52 (9), 2014.
6
Robust Tomography – L-band simulated analysis
Robust extraction of forest height in temporal decorrelating scenarios
Example of robust Tomography capabilities
Parametric 4D+ Diff-Tomo processing vs. model-based 3D
Robust 3D via Diff-Tomo
Misinterpretation avoided of
temporal perturbations in the
spatial spectral estimation
(temporal bandwidths as a
nuisance)
Orbital design can be
reconsidered
synergically with
4D decorrelation-robust
method
(e.g. for NISAR)
Probability of resolution vs. long-term
canopy decorrelation
∆h = 0.6 Rayleigh r.u.
Probability of resolution vs. long-term
canopy decorrelation
∆h = 0.6 Rayleigh r.u.
τ c  6 ÷ 11 revisit times
Simulated analysis
L-band forest scenario
monostatic monotonic
acquisition pattern
Simulated analysis
L-band forest scenario
monostatic b-t sparse
acquisition pattern
(10 passes)
(10 passes)
F. Lombardini, F. Cai, “Temporal Decorrelation-Robust SAR
Tomography," IEEE TGARS, available in early access, 52 (9), 2014.
6
P-band Tomography robust to temporal decorrelation
Robust extraction of forest height in decorrelating scenarios through parametric Diff-Tomo
Model-based 3D Tomo-SAR
BIOSAR P-Band data
 Several canopy layer
portions blurred/missed
• HV pol.
Matched model-based 5D Diff-Tomo
 Height resolution
significantly restored,
sidelobes better cleaned,
both canopy and ground
scatterers neatly located
The method may be
investigated for
BIOMASS
5D processing is here in variable model order form,
and “absorbs” also possible trends of collective phase shifts
F. Lombardini, F. Cai, “Temporal Decorrelation-Robust SAR
Tomography," IEEE TGARS, available in early access, 52 (9), 2014.
7
P-band temporal decorrelation stratigraphy
Large scale parametric analysis, long-term
BIOSAR P-Band data
Analysis of stratified temporal decorrelation
mechanisms on boreal forest
[Lombardini-Viviani-Cai-Dini, IGARSS ‘13]
Mild decorrelating scenario, weak canopy scattering
Separated temporal bandwidth maps
Canopy
Ground
Statistical analysis
Canopy
Mean
temporal
bandwidths
(months)
Ground
0.04
0.08
(phasecycles/month)
Mean
coherence
times
• HV pol.
(0.03
with HH
pol)
16.8
9.1
(35.5
with HH
pol)
Estimates achieved for
overall coherence down to
about 0.4
LIDAR and overall coherence
masking
8
P-band temporal decorrelation stratigraphy (2)
Large scale parametric analysis, long-term
BIOSAR P-Band data
Analysis of stratified temporal decorrelation
mechanisms on boreal forest
Mild decorrelating scenario, weak canopy scattering
• HV pol.
Separated temporal bandwidth maps (raw output)
Ground
Canopy
 Very extensive
separations, >500 land hectares analyzed !
(>> than TropiScat and AfriScat towers)
 No special HW required
9
Short-term decorrelation experiments
First set up of a dedicated micro-scale
space-time short-term decorrelation signature experiment with a
tower mini-sensor (X-band, S-band)
Pseudo Diff-Tomo
characterization of short-term
coherence time
Innovative short-term coherence profiling along the height dimension by a special
ground-based miniradar (X-band, HH pol.) is also possible.
1st quick Diff-Tomo
characterization of short-term
coherence time
Scenario



poplar and elm trees
light breeze
cross-wind
short-term coherence times of 50 ms
on the average
10
Short-term decorrelation experiments
First set up of a dedicated micro-scale
space-time short-term decorrelation signature experiment with a
tower mini-sensor (X-band, S-band)
Pseudo Diff-Tomo
characterization of short-term
coherence time
Innovative short-term coherence profiling along the height dimension by a special
ground-based miniradar (X-band, HH pol.) is also possible.
1st quick Diff-Tomo
characterization of short-term
coherence time
Scenario



poplar and elm trees
light breeze
cross-wind
Interesting to consider
characterizing the
(height-varying) short-term
decorrelation for the
SAOCOM-CS system
10
Conclusions
The Differential Tomographic (Diff-Tomo) technique is an advanced methodology for
promising 3D sensing and TomoSAR/PolInSAR characterization of
decorrelating forest scatterers, beyond urban applications

Temporal decorrelation-robust Tomography through higher-dimensional Diff-Tomo:
Sparse (non monotonic) baseline-time sampling can be considered jointly with
robust Tomography for monostatic satellite systems

Diff-Tomo separation of different overlayed long-term decorrelation mechanisms:
Coverage exploiting airborne data much larger than TropiScat and AfriScat
No special hardware required
Can be considered also to exploit AfriSAR extending AfriScat analyses

New vertical profiling of short-term temporal decorrelation:
First samples from ground-based quick miniradar experiment (currently at X-band; possible
next extension also at S-band)
Phenomenology can be important in designing companion non-fully simultaneous satellites
Long-wavelength spaceborne missions (BIOMASS, NISAR, SAOCOM-CS, Tandem-L, etc.)
and supporting campaigns may benefit from the application of these Diff-Tomo analyses,
processing, and measurement concepts
11
THANKS FOR YOUR ATTENTION!