G. Vasile , E. Trouvé, I. Petillot, Ph. Bolon, J.-M. Nicolas,

7th CNES/DLR Workshop LISTIC / TSI / GIPSA-lab / MAP-PAGE 1

High Resolution SAR Interferometry: estimation of local

frequencies in the context of Alpine glaciersG. VasileG. Vasile, E. Trouvé, I. Petillot, Ph. Bolon, J.-M. Nicolas,, E. Trouvé, I. Petillot, Ph. Bolon, J.-M. Nicolas,

M. Gay, J. Chanussot, T. Landes and P. Grussenmeyer M. Gay, J. Chanussot, T. Landes and P. Grussenmeyer

[email protected]@univ-savoie.fr


Outlines

Context: InSAR high resolution Local frequencies estimation algorithm Results and discussions

Low Resolution ERS TANDEM data High Resolution simulated TS-X data

Conclusions and perspectives


Low Resolution (LR) vs. High Resolution (HR)

Longitudinal elevation profiles along the Mer-de-glace (m)

LR – 80m LR+HR – 2mMer-de-glace surface

May 2004

• Strong topography -> narrow fringes

• Glacier microreliefmicrorelief -> HR component

Different surface penetration

Different orientations


Need for frequencies estimates - Estimation

Estimation of 2nd order moments : complex correlation

3 directions for preserving the stationarity & ergodicity Spatial support: boxcar, directional, region growing… Appropriate estimator: ML, LLMMSE… Compensation of deterministic phase components

STATIONARITY

ERGODICITY

Trade-off: ergodicity/stationarity – number of samplesTrade-off: ergodicity/stationarity – number of samples ! !


Need for frequencies estimates – 2D unwrapping

Phase ambiguity Wrapped phase: φ = Φ (mod 2π)

Nyquist criterion: | Φ(N) − Φ(M)| < π

Phase difference test for unwrapping:

Phase difference -> phase gradient -> Phase difference -> phase gradient -> local frequencylocal frequency


Outlines

Context: InSAR high resolution Local frequency estimation algorithm Results and discussions




Phase LR+HR model

Analytical phase signal:

: 2D sine-wave estimated on large square windows (*)

: 2D sine-wave

Need of adaptive neighborhood

Need of new estimation technique

nHRLR iiii eeee

(*) E. Trouvé et al. “Improving phase unwrapping techniques by the use of local frequency”, IEEE Transactions on Geoscience and Remote Sensing, 36(6):1963-1972, 1998

LR

HR


Intensity Driven Adaptive Neighborhood (IDAN)

(*) G. Vasile et al. “Intensity-Driven-Adaptive-Neighborhood Technique for Polarimetric and Interferometric SAR Parameters Estimation”. IEEE Transactions on Geoscience and Remote Sensing, 44(5):1609-1621, 2006

2-step region growing technique (*)

Driven simultaneously on all the intensities of the input data set; AN makes it possible to reach the number of pixels necessary

for reliable estimation;

AN preserves the stationarity since most of the sources of phase nonstationaritymost of the sources of phase nonstationarityare revealed by the SAR intensity are revealed by the SAR intensity which is mostly influenced by the local slopewhich is mostly influenced by the local slope.


Estimation of the local frequency

2D phase model:

Estimation technique based on the autocorrelation function:

under stationarity and phase noise iid hypothesis K real

Step 1: estimation of on the Np,q available pixel pairs

Step 2: estimation of the local frequency:

)(2*),(),(),( yx qfpfjKeqlpkslksEqp

),( qp

),()1,(

),(1,,arg

2

1ˆ qpqpNNqp

qpqpf y

),(),1(

),(,1,arg

2

1ˆ qpqpNNqp

qpqpf x


Algorithm implementation

HR - IDANHR - IDANFrequency Frequency EstimationEstimation

2D - LR2D - LRlocal local

frequenciesfrequencies

SAR SAR intensities intensities

LR MUSICLR MUSICFrequencyFrequencyEstimationEstimation

SAR phaseSAR phase LR Freq.LR Freq.

CompensationCompensation

2D - HR2D - HRlocal local

frequenciesfrequencies

Local compensation of LR deterministic geometrical phase component

The resulting phase signal exhibits the local differences between the 2D sine-wave model and the real HR fringe pattern


Outlines

Context: InSAR high resolution Local frequency estimation algorithm Results and discussions




TANDEM ERS data set

masteramplitude phase

LR fringe orientation

HR fringe orientation

LUT

Mer-de-glace glacier [C-band, 5-looks, 768x489 pixels, 20x20 m, ea=45m]


TANDEM ERS data set

masteramplitude phase

LR+HR fringe

orientation

IDANfiltered phase

LUT



TANDEM ERS data set

LR+HRfringe

orientation

phase

IDAN filtered coherence

ROI-PACfiltered

coherence

LUT



TerraSAR-X application

(a) (b)

The Mer-de-glace glacier: (a) Aerotriangulation, (b) DTM 2mx2m.



Slant range sampling of the SAR intensity

Slant range sampling of the elevation (linear interpolation)

Descending pass simulation

1.2x2m, αin=30, H=514km



Simulated HR SAR amplitude: σ2=1

(speckle variance), 1.2x2m

Real LR ERS SAR amplitude: 20x20m



Simulated HR SAR amplitude: σ2=1

(speckle variance), 1.2x2m

Simulated HR SAR phase: ea=10m, uniform phase

noise distribution ±π/4



Simulated HR SAR phase: ea=10m, uniform phase noise distribution ±π/4

LUT

LR map:fringe orientation



LR map:fringe orientation

LUT

LR+HR map:fringe orientation



LR+HR map:fringe orientation

IDAN LR+HR filtered phase

LUT


TerraSAR-X applicationLUT

May 2004 Photo of the simulated TerraSAR-X region on the Mer-de-glace glacier (approximate position of the profile)

50m spatial profile along the surface of the Mer-de-glace glacier:

real altitude resampled in the TerraSAR-X slant range,unwrapped HR+LR estimates

of the local frequencies,unwrapped LR estimates of the local frequencies.



ConclusionsConclusions:: HRHR frequency estimation combined with intensity driven frequency estimation combined with intensity driven

adaptive neighborhood;adaptive neighborhood; estimate local frequenciesestimate local frequencies within HR interferograms; within HR interferograms; measure the measure the local topographic variationslocal topographic variations in in

interferograms with a interferograms with a small altitude of ambiguitysmall altitude of ambiguity..

Future directions:Future directions: Chamonix – Mont Blanc Chamonix – Mont Blanc glacier monitoring glacier monitoring by D-InSAR,by D-InSAR, New context: POL-InSAR airborne data.New context: POL-InSAR airborne data.


E-SAR CampaignE-SAR Campaign

Argentière: Oct./06 & Feb./07Argentière: Oct./06 & Feb./07


Thank you!

This work was supported by the French national project ACI-MEGATOR. The authors wish to thank the European Space Agency for providing the SAR data through the Category 1 proposal No.3525.

G. Vasile , E. Trouvé, I. Petillot, Ph. Bolon, J.-M. Nicolas,

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