Cai lomver gold2010


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Cai lomver gold2010

  1. 1. 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
  2. 2. Outline <ul><li>3D SAR Tomography concept </li></ul><ul><li>Temporal decorrelation in SAR Tomography </li></ul><ul><li>Blurring effects of temporal decorrelating volume </li></ul><ul><li>scatterers: simulated analysis </li></ul><ul><ul><li>SAR Tomography criticalities </li></ul></ul><ul><ul><li>Indication on conditions critical for SAR Tomography </li></ul></ul><ul><ul><li>in partially coherent scenes </li></ul></ul><ul><li>Ad-hoc solution for decorrelating volume scatterers: the Diff-Tomo concept </li></ul><ul><ul><li>Examples of robust SAR Tomography </li></ul></ul><ul><li>Conclusions and perspectives </li></ul>
  3. 3. 3D SAR Tomography concept flight direction b N b n b 1 s, elevation z y ( b 1 ) y ( b n ) y ( b N ) Range-azimuth cell Azimuth Ground range Signal spatial sample at baseline b n : 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) <ul><li>Applications: </li></ul><ul><li>solving InSAR layover heights and reflectivity misinterpretation in urban areas </li></ul><ul><li>estimation of forest biomass and height </li></ul><ul><li>sub-canopy topography </li></ul><ul><li>soil humidity and ice thickness monitoring </li></ul>[Reigber-Moreira, IEEE-TGARS ’00] Complex amplitude elevation distribution <ul><li>However… </li></ul><ul><li>Limited and sparse baseline distribution, poor Fourier imaging quality </li></ul><ul><li>Proposed solutions: adaptive beamforming, SVD, spatial interpolators (compressed sensing)… </li></ul>[Lombardini-Reigber, IGARSS ‘03] [Fornaro-Serafino-Soldovieri, IEEE-TGARS ’03] [Lombardini-Pardini, IEEE-GRSL ‘08] <ul><li>Elevation blurring problems from scatterers motion and temporal decorrelation ! </li></ul><ul><li>NASA-JPL and ESA recognized this as a major limiting factor (forest scatterers and spaceborne acquisitions) </li></ul>
  4. 4. Tomography with temporal decorrelation Acquisition Time <ul><li>Temporal decorrelation model: </li></ul><ul><li>Short term random movements; ( e.g. action of the wind on canopy ) </li></ul><ul><li>white zero-mean Gaussian displacements </li></ul><ul><li>Long term correlated random movements; ( e.g. seasonal change, tree growth ) </li></ul><ul><li>internal brownian motion model </li></ul>Assumed temporal coherence function Coherence time Brownian motion standard deviation Acquisition 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 . . . . . . b 1 b 2 t 1 t 2 t n b n
  5. 5. 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 <ul><li>Different temporal decorrelation conditions for temporal decorrelating canopy </li></ul>Long term temp. dec.  c = 34 rev. times Analysis of a model based and adaptive BF Tomo SAR methods, useful for critical resolutions <ul><li>Simulated analysis: forest scenario </li></ul><ul><li>Compact scatterer (ground) + volumetric scatterer (canopy) </li></ul><ul><li>Different baseline-time acquisition pattern: monostatic and multistatic </li></ul><ul><li>Height distance: 0.7 Rayleigh res. Units </li></ul><ul><li>g/v = 1/5 (L-band acquisition) </li></ul><ul><li>Total SNR = 15dB </li></ul><ul><li>16 looks </li></ul><ul><li>Different temporal decorrelation processes </li></ul>Weak Strong Satellite cluster
  6. 6. Model-based SAR Tomography <ul><li>Scatterers rarely unresolved </li></ul><ul><li>The positions of the scatterers are correctly located </li></ul>Ideal case Monostatic acquisition pattern Canopy Ground  c = 34 rev. times  c = 3 rev. times Strong temporal decorrelation Weak temporal decorrelation <ul><li>Two peaks not often visible : loss of resolution </li></ul><ul><li>Elevation displacement: loss of accuracy </li></ul>SAR Tomography functionality affected even by a weak temporal decorrelation condition
  7. 7. Model-based SAR Tomography <ul><li>Two peaks not often visible : loss of resolution </li></ul><ul><li>Elevation displacement: loss of accuracy </li></ul>Ideal case Multistatic acquisition pattern SAR tomography functionality worsening present even in more densely sampled acquisition pattern  c = 34 rev. times  c = 3 rev. times Strong temporal decorrelation Weak temporal decorrelation
  8. 8. Adaptive beam SAR Tomography Ideal case <ul><li>Loss of resolution and loss of accuracy </li></ul><ul><li>Adaptive BF Tomo SAR better than MUSIC for a strong decorrelation condition. </li></ul>Multistatic acquisition pattern Temporal signal histories are equivocated with spatial histories, resulting in a heavy resolution loss and in an estimation performance degradation  c = 34rev. times  c = 3 rev. times Strong temporal decorrelation Weak temporal decorrelation
  9. 9. SAR Tomography criticalities Which 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 τ c Acquisition time >≈ τ c 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 τ c Adaptive BF Tomo SAR begins to perform better than model-based
  10. 10. 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 spectrum Temporal frequencies code velocities Example: subsidence in urban layover areas Extended scatterers in height Range of velocities spatial harmonic distribution temporal harmonic distribution Continuous space-time spectrum Temporal 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 framework Joint elevation-velocity resolution of multiple scatterers
  11. 11. Robust SAR Tomography trough Differential SAR Tomography <ul><li>Simulated data </li></ul><ul><li>Multistatic acquisition pattern </li></ul><ul><li>Other parameters as before </li></ul><ul><li>ESA project BIOSAR: </li></ul><ul><li>quasi-multistatic acquisition </li></ul><ul><li>P-band, 3 passes, 9 tracks </li></ul><ul><li>Time span: 2 months, temp. freq. resolution 0.5 phase cycles/month </li></ul><ul><li>Mild temporal decorrelation </li></ul>Spectral signatures from temporal decorrelation of canopy Robust tomographic method Diff-Tomo spectrum Elevation resolution is restored
  12. 12. Conclusions and perspectives <ul><li>Quantification of temporal effects on SAR Tomography for volumetric scatterers </li></ul><ul><li>Model-based Tomo-SAR criticalities for acquisition time span beyond 1/2~1/3 of the long-term decorrelation time </li></ul><ul><li>Adaptive BF Tomography better than model-based Tomography with temporal decorrelation </li></ul><ul><li>Differential-Tomography , accounts for the temporal dimension and improves the MB tomographic information extraction; demonstration of robust SAR Tomography </li></ul><ul><li>Future work: </li></ul><ul><li>Extension of analysis for different acquisition configurations and different g/v </li></ul><ul><li>Possible application of robust sar tomography to new spaceborne SAR systems can be also investigated </li></ul>