Imagerie SAR multi-modale pour la télédétection d'environnements
Laurent Ferro-Famil
Institut d’Electronique et de Télécommunications de Rennes – IETRUMR 6164
Université de Rennes 1
Synthetic Aperture Radar imaging
ESA ENVISAT
ASAR MERIS
Active coherent device
Quasi-independent of weather conditions
High-resolution & large coverage
Moderate revisiting time (improving)
Signal related to EM scattering physics
Wave penetration through volumes
Side-looking geometry
Speckle effect
Very high EM information content
Complex signal handling
Multi-modalCoherent Imaging
M-D Signal & InfoProcessing
QuantitativeRemote sensing
M-D EM & StatisticalModeling/Analysis
Multi-modal SAR remote sensing
POLSARPRO
PolSAR data
Spatial diversity
Spectral diversityTemporal diversity
PolSAR diversity modes
angular : incidence & aspect angles baseline : interferometry (topo,vol) multiple baselines : tomography (3-D)
Change monitoring Slow effects monitoring (DinSAR, PS) Seasonal/cyclic evolution
Multi-carrier (band) Internal SAR spatial frequencies:
- rg & doppler- links with SAR physics
3 channels Scattering mechanisms Physical interpretation
• Single channel SAR applications: cartography, (moving) object detection
• Multi-channel SAR: …
Multi-modal SAR data statistics
R PolSAR acquisitions along a diversity axis
2nd (or higher) order statistics: structured tensor
Multi-modal SAR data statistics
• R PolSAR covariance matrices
• A few looks are required
• Easy to handle
• Covariance & X-correlation matrices
• Large L value required
• More complex processing
• Much more information
Incoherent processing Coherent processing
Incoherent vs Coherent
• Based on expected EM & statistical properties
• Application and data driven
Coherent SAR Time-Frequency analysis
Vsar
Hqo
Ro
O
XR
Y
Coherent T-F analysis
0azaz
rg0rg
Angular behavior
Frequency sensitivity
Local spectral estimation
SAR image Spectrum
Local spectrumT-F SAR image
TF SAR response: highly related to the scene EM and statistical properties
T-F SAR signal characterization
Varying T-F signal model
: varying composite signal
: quasi-deterministic, slowly varying component highly coherent
: random component, potentially non-stationary incoherent scattering
Canonical T-F behaviorsstationarity
coherencelow high
low
highnatural mediaisotropic
strong scatterers
anisotropic artificial structures
numerous anisotropic responses
Full-pol response sampled at R spectral positions:
where
T-F behavior characterization
Stationarity Coherence
Tools: MLRT hypothesis testing, canonical correlation
T-F SAR signal characterization
H A
a a1
0
0
1
p/2
T11 T22 T33
H A
a1a
Complex urban area: PolSAR features
DLR/ESAR L bandcoarse resolutionDresden data set
Non-stationarity
Facing buildings: non-stationary SB, DB components
Oriented buildings: stationary trend
Coherent objects: building edges, point-like scatterers
TF adaptation: non-stationary but coherent behaviour
Coherence
Complex urban area: PolSAR T-F features
TF detection of ships from spaceRS2 pol-scansar images: - small az aperture- small BW
- Unavoidable target-like artefacts - Energetic sidelobes- Mixed natural-urban environment
H
a
RS2 pol-scansar images: - small az aperture- small BW
- Unavoidable target-like artefacts: solved- Energetic sidelobes : solved- Mixed natural-urban environment : globally solved
2-D (rg & az) analysisTF pol coherence
TF detection of ships from space
rTF-Pol
rTF-PolGoogle T11 T22 T33
Wild vs. built-up island discrimination
ALOS/PALSAR L band stripmap image San-Francisco bay
TF detection of ships from space
C. Hu, L. Ferro-Famil, C. Brekke, S. N. Anfinsen, "Ship detection using polarimetric RadarSat-2 data and multi-dimensional coherent Time-Frequency analysis" Proc. POLINSAR 2013
Norwegian coast-guard icebreaker
RadarSat 2 PolSAR stripmap images
Svalbard sea-ice test site
TF detection of ships in sea-ice
Cloude, S. & Papathanassiou, K. "Polarimetric SAR interferometry" IEEE TGRS, 1998Papathanassiou, K. & Cloude, S, "Single-baseline polarimetric SAR interferometry," IEEE TGRS 2001Cloude, S. & Papathanassiou, K. "Three-stage inversion process for polarimetric SAR interferometry", IEE RSN, 2003
Polarimetric SAR Tomography principles
Polarimetric SAR Tomography principles
Leading research centers in PolTomSAR: DLR (Ger.), PoliMi (It), IETR (Fra)
Tebaldini, S. "Algebraic Synthesis of Forest Scenarios From Multibaseline PolInSAR Data", IEEE TGRS 2009Tebaldini, S. "Single and Multipolarimetric SAR Tomography of Forested Areas: A Parametric Approach" IEEE TGRS 2010L. Ferro-Famil, Y. Huang, E. Pottier, “Principles and applications of polarimetric SAR tomography for the characterization of complex environments”, IAGS proceedings, 2014
Polarimetric SAR Tomography principles
Huang, Y.; Ferro-Famil, L. & Reigber, A. "Under-Foliage Object Imaging Using SAR Tomography and Polarimetric Spectral Estimators", IEEE TGRS 2011
PolTomSAR imaging of concealed objects
IETR SAR sensors: the PoSAR project
PoSAR-GB (Ground-Based) PoSAR-FX (Fixed)
PoSAR-MC (Multi-Channel), airborne
o VNA basedo frequency bands: 3 GHz to 18 GHz
(C,X,Ku) o Short range (meters)o Very High spatial Resolution (cms) o Box + VNA = 40 Kg
Objectives3-D HR characterization of volumeso Local & structural propertieso Model development and
(un)validationo Space-borne mission concepts
PoSAR-GB: VHR 3-D SAR imager
o Collecting along parallel vertical passes 3D resolution capabilities
3D Imaging
Image Stack
Ground range [m]
Hei
ght
[m]
Intensity
1 2 3 4 5 6 7 8
-1.5
-1
-0.5
0
0.5
Tomogram
PoSAR-GB: VHR 3-D SAR imager
o ESA campaign, (ESA,ENVEO (A), UR1 (Fra), PoliMi (It)): Feb. 2013, Austrian Alps• Snowpit data, GPR, Airborne SAR, GBSAR
o X-Band: 4 GHz @ 10 GHzo Ku-Band: 4 GHz @ 14 GHzo VV Polarizationo dz ≈ 10 cm (Ku-band), 15 cm (X-Band) o dr ≈ 4 cm, dx ≈ 4 cm
LeutaschoNorth of Innsbrucko≈ 1150 m a.s.l.o70 cm snowpack
Rotmoos valleyoItalian bordero≈ 2300 m a.s.l. o140 cm snowpack
The ALPSAR campaign
y [m]
z [m
]
0 1 2 3 4 5 6 7-2
-1.5
-1
-0.5
0
0.5
y [m]
z [m
]
0 1 2 3 4 5 6 7-2
-1.5
-1
-0.5
0
0.5
Averaged vertical section – X-Band [dB]
Averaged vertical section – Ku-Band [dB]
• Visible interfaces• Low volume
contribution • Strongest returns:
bottom layers
Snowpit data:Snow depth= 140 cm !!
Rotmoos X & Ku bands
Real scenario
0 1 2 3 4 5 6 7 8 9-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
ground range [m]
heig
ht [
m]
apparent surface height
c
Resulting vertical section
Apparent surface height for a three-layered scenario
c
Propagation effects
y [m]
z [m
]
0 1 2 3 4 5 6 7-2
-1.5
-1
-0.5
0
0.5
n = 1
0 m
-0.54 m
-1.03 m
-1.40 m
n = 1.25
n = 1.25
n = 1.25
Averaged vertical section – X-Band [dB]
Rotmoos
y [m]
z [m
]
0 1 2 3 4 5 6 7-1.5
-1
-0.5
0
0.5
n = 1
0 m
-0.31 m-0.46 m
-0.70 m
n = 1.5
n = 1.5
n = 1.5
Averaged vertical section – X-Band [dB]
Leutasch
Propagation effects
• Vertical structure : consistent with hand-hardness
new parameter not yet handled by EM scattering models
Characterization
• Vertical structure : consistent with hand-hardness
teta [deg]
dept
h [m
]
Rotmoos Ku Normalized backscattered power [dB] - teta-depth
30 35 40 45 50 55
0
0.5
1
1.5
new parameter not yet handled by EM scattering models
Characterization
VHR GB tomography of sea ice
Sea-ice : large air bubbles embedded in ice
Ice-sea interface: rough surface
Brewster effect at large q : coherent sea response
VHR GB tomography of sea ice
- Null snow response : spherical grain hypothesis verified
- Strong sea-ice return: non spherical air bubbles embedded in ice
- Coherent ice-sea interface response: faceted rough interface
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