Temporal decorrelation effects in super-resolution 3D Tomosar

Francesco Cai, Fabrizio Lombardini, Lucio Verrazzani

University of Pisa

Department of Information Engineering

Gold conference 2010 Livorno, April 29 2009

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Outline

3D SAR Tomography concept

Temporal decorrelation in SAR Tomography Blurring effects of temporal decorrelating volume scatterers: simulated analysis

SAR Tomography criticalities Indication on conditions critical for SAR Tomography in partially coherent scenes

Ad-hoc solution for decorrelating volume scatterers: the Diff-Tomo concept

Examples of robust SAR Tomography

Conclusions and perspectives

3D SAR Tomography concept

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flight dire

ction

bN

bn

b1

s, elevationz

y(b1)

y(bn)

y(bN)

Range-azimuth cell

Azimuth

Ground range

Signal spatial sample at baseline bn:

Define an elevation-dependent spatial frequency:

1-D Fourier relation

Tomo-SAR can localize the multiple scatterers through spatial spectral estimation (i.e. elevation beamforming)

Applications:• solving InSAR layover heights and reflectivity misinterpretation in urban areas• estimation of forest biomass and height• sub-canopy topography• soil humidity and ice thickness monitoring

[Reigber-Moreira, IEEE-TGARS ’00]

Complex amplitude elevation distribution

However…• Limited and sparse baseline distribution, poor Fourier imaging qualityProposed solutions: adaptive beamforming, SVD, spatial interpolators (compressed sensing)…

[Lombardini-Reigber, IGARSS ‘03][Fornaro-Serafino-Soldovieri, IEEE-TGARS ’03][Lombardini-Pardini, IEEE-GRSL ‘08]

• Elevation blurring problems from scatterers motion and temporal decorrelation !

NASA-JPL and ESA recognized this as a major limiting factor (forest scatterers and spaceborne acquisitions)

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Tomography with temporal decorrelation

. . .

. . .b1

b2

t1 t2 tn

bn

Acquisition Time

Temporal decorrelation model:

• Short term random movements; (e.g. action of the wind on canopy) white zero-mean Gaussian displacements

• Long term correlated random movements; (e.g. seasonal change, tree growth)internal brownian motion model

Assumed temporal coherence function Coherence time

Brownian motion standard deviationAcquisition time index

Physical changes during the multibaseline acquisition time span can badly affect the spatial spectral estimation

Objective: Analysis and quantification of temporal decorrelation effects on the formation of Tomo profiles from repeat pass multibaseline data; analysis of possible solution

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Tomographic analysis: scenario and methods

Temporal decorrelation model from [Lombardini-Griffiths, IEE-EUREL ’98]

Baseline-time acquisition pattern

Long term temp. dec. c = 3 rev. times

• Different temporal decorrelation conditions for temporal decorrelating canopy

Long term temp. dec. c = 34 rev. times

Analysis of a model based and adaptive BF Tomo SAR methods, useful for critical resolutions

Simulated analysis: forest scenario• Compact scatterer (ground) + volumetric scatterer (canopy)• Different baseline-time acquisition pattern: monostatic and multistatic• Height distance: 0.7 Rayleigh res. Units• g/v = 1/5 (L-band acquisition)• Total SNR = 15dB• 16 looks• Different temporal decorrelation processes

Weak Strong

Satellitecluster

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Model-based SAR Tomography

• Scatterers rarely unresolved

• The positions of the scatterers are correctly located

Ideal case c = 34 rev. times

c = 3 rev. times

Strong temporal decorrelation

Weak temporal decorrelation

Monostatic acquisition pattern

Canopy

Ground

• Two peaks not often visible : loss of resolution

• Elevation displacement: loss of accuracy

SAR Tomography functionality affected even by a weak temporal decorrelation condition

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Model-based SAR Tomography

• Two peaks not often visible : loss of resolution

• Elevation displacement: loss of accuracy

Ideal case c = 34 rev. times

c = 3 rev. times

Strong temporal decorrelation

Weak temporal decorrelation

Multistatic acquisition pattern

SAR tomography functionality worsening present even in more densely sampled acquisition pattern

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Adaptive beam SAR Tomography

Ideal case

• Loss of resolution and loss of accuracy

• Adaptive BF Tomo SAR better than MUSIC for a strong decorrelation condition.

c = 34rev. times

c = 3 rev. times

Strong temporal decorrelation

Weak temporal decorrelation

Multistatic acquisition pattern

Temporal signal histories are equivocated with spatial histories, resulting in a heavy resolution loss and in an estimation performance degradation

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SAR Tomography criticalitiesWhich temporal decorrelation condition is critical for SAR tomography functionality?

Resolution(%), multistatic configuration

Useful indications in the planning of future missions such as ESA-BIOMASS and DLR TanDEM-L.

Acquisition time >≈ ½-⅓ τc

Acquisition time >≈ τc

Criticalities for model-based SAR Tomograpy : strong loss for resolution probability

Adaptive BF method is more robust to temporal decorrelation effects than model –based method

Acquisition time ≈ ⅓ τc Adaptive BF Tomo SAR begins to perform better than model-based

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A new approach:the Differential SAR Tomography framework

Point-like scatterer in height

Uniform motion (l.o.s. direction)

spatial harmonic

temporal harmonic

Discrete space-time spectrumTemporal frequencies code velocitiesExample: subsidence in urban layover areas

Extended scatterers in height

Range of velocities

spatial harmonicdistribution

temporal harmonic distribution

Continuous space-time spectrumTemporal frequencies code velocities Example: a glacier flow (sliding random volume over ground)

Temporal decorrelation of a scattering component

temporal harmonic distribution

Temporal frequencies are signatures of the temporal decorrelation !

[Lombardini-Fornaro, IGARSS’05][Fornaro-Serafino-Reale, IEEE-TGARS’09]

[Lombardini, ESA FRINGE Wrkshp’07]

Diff-Tomo exploits the multibaseline-multitemporal information content to enter the SAR pixel and extract separated information on elevation and velocity of multiple superimposed scatterers

[Lombardini, TGARS Jan. 2005]

“Diff-Tomo” is a new interferometric mode, which avoids the misinterpretation of spatial signal histories (scatterers location) and temporal histories in non-stationary scenarios

Temporal signal histories from decorrelation can be decoupled from the spatial spectral estimation.

D-InSAR and Tomo-SAR crossed in an unified frameworkJoint elevation-velocity resolution of multiple scatterers

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Robust SAR Tomography trough Differential SAR Tomography

Simulated data• Multistatic acquisition pattern• Other parameters as before

ESA project BIOSAR: quasi-multistatic acquisition• P-band, 3 passes, 9 tracks•Time span: 2 months, temp. freq. resolution 0.5 phase cycles/month• Mild temporal decorrelation

Spectral signatures from temporal decorrelation of canopy

Robust tomographic method Diff-Tomo spectrum

Elevation resolution is restored

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Conclusions and perspectives

• Quantification of temporal effects on SAR Tomography for volumetric scatterers

• Model-based Tomo-SAR criticalities for acquisition time span beyond 1/2~1/3 of the long-term

decorrelation time

• Adaptive BF Tomography better than model-based Tomography with temporal decorrelation

• Differential-Tomography, accounts for the temporal dimension and improves the MB

tomographic information extraction; demonstration of robust SAR Tomography

Future work: • Extension of analysis for different acquisition configurations and different g/v

• Possible application of robust sar tomography to new spaceborne SAR systems can be also

investigated