Imagerie SAR multi-modale pour la télédétection d ... · PDF fileImagerie SAR multi-modale pour la télédétection d'environnements Laurent Ferro-Famil Institut d’Electronique

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

[email protected]

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

Deformation monitoring: M-Pol-DinSAR

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

POLSAR data

POLinSAR data

TF analysis

Complex urban area: T-F classification

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

Where is the ship ?

T11 T22 T33


April 11

T11 T22 T33


April 11


The ship is in A !

T11 T22 T33


Polarimetric SAR Tomography principles


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



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




, algebraic tensor decompositions

PolTomSAR imaging of a tropical forest





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


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


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

Sea ice campaign (with U. Tromso, Nor.)

Kattfjord, Norway, 2013

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


- 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

Conclusions

Modern SAR remote sensing is multi-modal

Signal diversity:Application or medium adapted (a priori)Enhanced separationMost useful: space & time

Statistical signal processing:High dimensionalUses data structure (tensors)May use sparsity

EM model validation/correction: important potential

Documents

Imagerie SAR multi-modale pour la télédétection d ... · PDF fileImagerie SAR multi-modale pour la télédétection d'environnements Laurent Ferro-Famil Institut d’Electronique