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Transcript of "Contribution_of_the_polarimetric_information.pdf"

  1. 1. SAR Imagery Algorithms Simulated data Real data Conclusion and Future Work Contribution of the polarimetric information in order to discriminate target from interferences subspaces. Application to FOPEN detection with SAR processing 1 F.Briguia , L.Thirion-Lefevreb , G.Ginolhacc and P.Forsterc a ISAE/University of Toulouse b SONDRA/SUPELEC c SATIE, Ens Cachan 1 Funded by the DGA1/24 IGARSS 2011 July 2011
  2. 2. SAR Imagery Algorithms Simulated data Real data Conclusion and Future Work Context Objective Detection of a target embedded in a complex environment using SAR system SAR (Synthetic Aperture Radar) ◮ airborne antenna ◮ monostatic configuration (“stop ◮ scene seen under different angles and go“) ◮ synthetic antenna2/24 IGARSS 2011 July 2011
  3. 3. SAR Imagery Algorithms Simulated data Real data Conclusion and Future Work Application FoPen Detection (Foliage Penetration) ◮ Man-Made Target (MMT) located u200 z in a forest u100 y ◮ P/L band: canopy is “transparent” m u2 10 m 0.5 u1 0 Scattering attenuation but target u0 -10 m detection still possible 95 m 115 m x Modeling ◮ Scatterers of interest ◮ Target → Deterministic scattering ◮ Tree trunks (interferences) → Deterministic scattering ◮ Others scatterers ◮ Branches, foliage → Random scattering3/24 IGARSS 2011 July 2011
  4. 4. SAR Imagery Algorithms Simulated data Real data Conclusion and Future Work FoPen Detection Classical SAR No prior-knowledge on the scatterers → isotropic and white point scatterer model Real data in VV of a truck and a trihedral in the Nezer Simulated data in VV of a box in a forest of trunks forest Results ◮ Low response of the target → Target not detected ◮ High response of the forest → Many false alarms4/24 IGARSS 2011 July 2011
  5. 5. SAR Imagery Algorithms Simulated data Real data Conclusion and Future Work FoPen Detection Classical SAR No prior-knowledge on the scatterers → isotropic and white point scatterer model Real data in VV of a truck and a trihedral in the Nezer Simulated data in VV of a box in a forest of trunks forest Results ◮ Low response of the target → Target not detected ◮ High response of the forest → Many false alarms4/24 IGARSS 2011 July 2011
  6. 6. SAR Imagery Algorithms Simulated data Real data Conclusion and Future Work FoPen Detection Classical SAR No prior-knowledge on the scatterers → isotropic and white point scatterer model Real data in VV of a truck and a trihedral in the Nezer Simulated data in VV of a box in a forest of trunks forest Results ◮ Low response of the target → Target not detected ◮ High response of the forest → Many false alarms4/24 IGARSS 2011 July 2011
  7. 7. SAR Imagery Algorithms Simulated data Real data Conclusion and Future Work New SAR processors Approach ◮ To reconsider the SAR image generation by including prior-knowledge on the scatterers of interest ◮ To generate one single SAR image → Use of subspace methods Awareness of the scattering and polarimetric properties: 1. Of the target → To increase its detection 2. Of the interferences → To reduce false alarms → Only possible if the target and the interferences scattering have different properties5/24 IGARSS 2011 July 2011
  8. 8. SAR Imagery Algorithms Simulated data Real data Conclusion and Future Work Outline SAR Imagery Algorithms FoPen Simulated data Real data Conclusion and Future Work6/24 IGARSS 2011 July 2011
  9. 9. SAR Algorithms SAR Imagery Algorithms CSAR Simulated data SSDSAR Real data OBSAR Conclusion and Future Work OSISDSAR Outline SAR Imagery Algorithms SAR Algorithms Classical SAR (CSAR) SSDSAR OBSAR OSISDSAR FoPen Simulated data Real data Conclusion and Future Work7/24 IGARSS 2011 July 2011
  10. 10. SAR Algorithms SAR Imagery Algorithms CSAR Simulated data SSDSAR Real data OBSAR Conclusion and Future Work OSISDSAR SAR data configuration SAR signal Single Polarization p Double Polarization SAR signal zp ∈ CNK SAR signal z ∈ C2NK     p  .  z = .   .   .   .    .     z=       .  .    . 8/24 IGARSS 2011 July 2011
  11. 11. SAR Algorithms SAR Imagery Algorithms CSAR Simulated data SSDSAR Real data OBSAR Conclusion and Future Work OSISDSAR SAR data configuration ◮ K time samples SAR signal Single Polarization p Double Polarization SAR signal zp ∈ CNK SAR signal z ∈ C2NK p   z1     p z =  .  .   .   .   .    .     z=       .  .    . 8/24 IGARSS 2011 July 2011
  12. 12. SAR Algorithms SAR Imagery Algorithms CSAR Simulated data SSDSAR Real data OBSAR Conclusion and Future Work OSISDSAR SAR data configuration ◮ K time samples ◮ N antenna positions ui SAR signal Single Polarization p Double Polarization SAR signal zp ∈ CNK SAR signal z ∈ C2NK  p  z1   .    p z =  .  . .      .  p zN   .     z=       .  .    . 8/24 IGARSS 2011 July 2011
  13. 13. SAR Algorithms SAR Imagery Algorithms CSAR Simulated data SSDSAR Real data OBSAR Conclusion and Future Work OSISDSAR SAR data configuration ◮ K time samples ◮ N antenna positions ui ◮ Polarization: single VV (or HH) or SAR signal Single Polarization p Double Polarization SAR signal zp ∈ CNK SAR signal z ∈ C2NK  p  z1 zHH   p  .   1 z =  .   .   .   .  p .   zN   HH    z z= N        .   .   . 8/24 IGARSS 2011 July 2011
  14. 14. SAR Algorithms SAR Imagery Algorithms CSAR Simulated data SSDSAR Real data OBSAR Conclusion and Future Work OSISDSAR SAR data configuration ◮ K time samples ◮ N antenna positions ui ◮ Polarization: single VV (or HH) or double (HH and VV) SAR signal Single Polarization p Double Polarization SAR signal zp ∈ CNK SAR signal z ∈ C2NK  p  z1 zHH   1 .    p z = .  .    .  .    .   p   zN  HH  z   z= N  VV   z1      .   .   .  zVV N8/24 IGARSS 2011 July 2011
  15. 15. SAR Algorithms SAR Imagery Algorithms CSAR Simulated data SSDSAR Real data OBSAR Conclusion and Future Work OSISDSAR Image generation principle For each pixel (x, y) Computation of the SAR response of the model Classical model ◮ White isotropic point scatterer response Subspace models ◮ Canonical element responses for all its orientations ◮ Generation of the subspace9/24 IGARSS 2011 July 2011
  16. 16. SAR Algorithms SAR Imagery Algorithms CSAR Simulated data SSDSAR Real data OBSAR Conclusion and Future Work OSISDSAR Image generation principle For each pixel (x, y) Computation of the SAR response of the model Classical model ◮ White isotropic point scatterer response Subspace models ◮ Canonical element responses for all its orientations ◮ Generation of the subspace Computation of the complex amplitude coefficient (or the coordinate vector) ◮ Least square estimation9/24 IGARSS 2011 July 2011
  17. 17. SAR Algorithms SAR Imagery Algorithms CSAR Simulated data SSDSAR Real data OBSAR Conclusion and Future Work OSISDSAR Image generation principle For each pixel (x, y) Computation of the SAR response of the model Classical model ◮ White isotropic point scatterer response Subspace models ◮ Canonical element responses for all its orientations ◮ Generation of the subspace Computation of the complex amplitude coefficient (or the coordinate vector) ◮ Least square estimation Intensity ◮ Square norm of the complex amplitude9/24 IGARSS 2011 July 2011
  18. 18. SAR Algorithms SAR Imagery Algorithms CSAR Simulated data SSDSAR Real data OBSAR Conclusion and Future Work OSISDSAR CSAR (Classical SAR) Modeling No prior knowledge on scatterers of interest. White Isotropic point model rxy SAR signal modeling z = axy rxy + n axy unknown complex amplitude, n complex white Gaussian noise of variance σ 2 Double polarization: 2 possible models ◮ trihedral scattering: rxy = r+ xy ◮ dihedral scattering: rxy = r− xy CSAR image intensity Equivalence with images generated with classical SAR processors (TDCA, ± r±† z xy 2 Backprojection, RMA) IC (x, y ) = σ210/24 IGARSS 2011 July 2011
  19. 19. SAR Algorithms SAR Imagery Algorithms CSAR Simulated data SSDSAR Real data OBSAR Conclusion and Future Work OSISDSAR SSDSAR (Signal Subspace Detector SAR) Target modeling Prior-knowledge: Target is made of a Set of Plates. Target model: Low Rank Subspace Hxy generated from PC plates. z z z’ z’ α z" β y"=y’ y’ O α β O y y α β x (b) x’ (c) (a) x=x’ x" Hxy : orthonormal basis of Hxy , λxy unknown amplitude coordinate vector. Signal SAR modeling Double polarization: 2 possible target subspaces z = Hxy λxy + n ◮ trihedral scattering: Hxy = H+ xy ◮ dihedral scattering: Hxy = H− xy11/24 IGARSS 2011 July 2011
  20. 20. SAR Algorithms SAR Imagery Algorithms CSAR Simulated data SSDSAR Real data OBSAR Conclusion and Future Work OSISDSAR SSDSAR (Signal Subspace Detector SAR) ` R. Durand, G. Ginolhac, L. Thirion-Lefevre, and P. Forster, “New SAR processor based on matched subspace detectors,” IEEE TAES, Jan 2009. ` F. Brigui, L. Thirion-Lefevre, G. Ginolhac and P. Forster, “New polarimetric signal subspace detectors for SAR processors,” CR Phys, Jan 2010. z Goal: Improvment of target detection. PHz SSDSAR image intensity <H> H† z 2 xy IS (x, y ) = σ2 † PHxy = Hxy Hxy : orthogonal projector into Hxy . <J>11/24 IGARSS 2011 July 2011
  21. 21. SAR Algorithms SAR Imagery Algorithms CSAR Simulated data SSDSAR Real data OBSAR Conclusion and Future Work OSISDSAR OBSAR (Oblique SAR) Interference modeling (Trunks) Prior-knowledge: Trunks are dielectric cylinders lying over the ground. Interference model: Low Rank Subspace Jxy generated from dielectric cylinders lying over the ground. z’=z z z" γ δ O y’ O y"=y’ O δ γ y γ δ x x’ x" (a) (b) (c) Signal SAR modeling z = Hxy λxy + Jxy µxy + n Jxy : orthonormal basis of Jxy , µxy unknown amplitude coordinate vector. Double polarization: 1 possible interference subspace12/24 IGARSS 2011 July 2011
  22. 22. SAR Algorithms SAR Imagery Algorithms CSAR Simulated data SSDSAR Real data OBSAR Conclusion and Future Work OSISDSAR OBSAR (Oblique SAR) ` F. Brigui, G. Ginolhac, L. Thirion-Lefevre, and P. Forster, “New SAR Algorithm based on Oblique Projection for Interference Reduction,” IEEE TAES, submitted. Goals: ◮ Increase of target detection. ◮ Reduce false alarms due to deterministic interferences. z OBSAR image intensity EHSz H† EHxy Jxy z xy 2 <H> IOB (x, y ) = σ2 † † EHxy Jxy = PHxy (I − Jxy (Jxy P⊥ Jxy )−1 Jxy P⊥ ): H H xy xy oblique projector into Hxy along the direction described by Jxy . Oblique projection of z into Hxy <J>12/24 IGARSS 2011 July 2011
  23. 23. SAR Algorithms SAR Imagery Algorithms CSAR Simulated data SSDSAR Real data OBSAR Conclusion and Future Work OSISDSAR OSISDSAR (Orthogonal Interference Subspace Detector Processor) Intensity IS Intensity II⊥ H† z 2 J′† z xy 2 xy II⊥ (x, y ) = IS (x, y ) = σ2 σ2 ′† † † Jxy = (Jxy P⊥ Jxy )−1 Jxy P⊥ H H xy xy z z PHz <H> <H> T J P Hz <J> <J>13/24 IGARSS 2011 July 2011
  24. 24. SAR Algorithms SAR Imagery Algorithms CSAR Simulated data SSDSAR Real data OBSAR Conclusion and Future Work OSISDSAR OSISDSAR (Orthogonal Interference Subspace Detector Processor) ` F. Brigui, G. Ginolhac, L. Thirion-Lefevre, and P. Forster, “New SAR Algorithm based on Signal and Interference Subspace Models,” IEEE GRS, To submit. Goals: ◮ Increase of target detection. ◮ Reduce false alarms due to deterministic interferences. OSISDSAR image intensity IS (x, y ) I (x, y ) ISI⊥ (x, y ) = − I⊥ ES EI ES = xy IS (x, y) and EI = xy II⊥ (x, y): normalization parameters13/24 IGARSS 2011 July 2011
  25. 25. SAR Imagery Algorithms Configuration Simulated data Single Polarization Real data Double Polarization Conclusion and Future Work Outline SAR Imagery Algorithms FoPen Simulated data Configuration Single Polarization (VV) Double Polarization Real data Conclusion and Future Work14/24 IGARSS 2011 July 2011
  26. 26. SAR Imagery Algorithms Configuration Simulated data Single Polarization Real data Double Polarization Conclusion and Future Work Configuration Radar parameters u200 ◮ 200 positions ui z y ◮ chirp with frequency bandwidth u100 B = 100Mhz with f0 = 400MHz m u2 10 m (P band) 0.5 u1 0 u0 -10 m Target and Interference x ◮ target: metallic box (2m x 1.5m x 95 m 115 m 1) over a PC ground (Feko) ◮ interferences: tree trunks (COSMO) Interference subspaces Signal subspaces ◮ Canonical element: dielectric ◮ Canonical element: PC plate cylinder (11m × 20cm) over the (2m × 1m) ground ◮ Ranks: 10 ◮ Ranks: 1015/24 IGARSS 2011 July 2011
  27. 27. SAR Imagery Algorithms Configuration Simulated data Single Polarization Real data Double Polarization Conclusion and Future Work VV polarization SSDSAR (ρ = 3.5 dB) cible Imax ρ = 10 log( interf ) Imax16/24 IGARSS 2011 July 2011
  28. 28. SAR Imagery Algorithms Configuration Simulated data Single Polarization Real data Double Polarization Conclusion and Future Work CSAR (ρ = −2.5 dB) SSDSAR (ρ = 3.5 dB) OBSAR (ρ = 3.5 dB) OSISDSAR (ρ = 3.5 dB)16/24 IGARSS 2011 July 2011
  29. 29. SAR Imagery Algorithms Configuration Simulated data Single Polarization Real data Double Polarization Conclusion and Future Work Analysis ◮ H VV et J VV too “close” ◮ Trunks response rejection not possible OBSAR (ρ = 3.5 dB) OSISDSAR (ρ = 3.5 dB)16/24 IGARSS 2011 July 2011
  30. 30. SAR Imagery Algorithms Configuration Simulated data Single Polarization Real data Double Polarization Conclusion and Future Work Double polarization (dihedral case) CSAR (ρ = −3.5 dB) SSDSAR (ρ = 1.8 dB) Dihedral case17/24 IGARSS 2011 July 2011
  31. 31. SAR Imagery Algorithms Configuration Simulated data Single Polarization Real data Double Polarization Conclusion and Future Work CSAR (ρ = −3.5 dB) SSDSAR (ρ = 1.8 dB) OBSAR (ρ = 3.6 dB) OSISDSAR (ρ = 4.5 dB)17/24 IGARSS 2011 July 2011
  32. 32. SAR Imagery Algorithms Configuration Simulated data Single Polarization Real data Double Polarization Conclusion and Future Work Analysis ◮ H et J enough “disjoint” ◮ Trunks response rejection ◮ OBSAR: robust to the target modeling ◮ OSISDSAR: robust to the interference modeling. OBSAR (ρ = 3.6 dB) OSISDSAR (ρ = 4.5 dB)17/24 IGARSS 2011 July 2011
  33. 33. SAR Imagery Algorithms Configuration Simulated data Single Polarization Real data Double Polarization Conclusion and Future Work Outline SAR Imagery Algorithms FoPen Simulated data Real data Configuration Single Polarization (VV) Double Polarization Conclusion and Future Work18/24 IGARSS 2011 July 2011
  34. 34. SAR Imagery Algorithms Configuration Simulated data Single Polarization Real data Double Polarization Conclusion and Future Work Configuration Radar parameters ◮ chirp with frequency bandwidth B = 70Mhz Pyla 2004 (ONERA) - Nezer forest with f0 = 435MHz u un Target and Interference y ◮ MMT: Truck u2 Nezer forest ◮ Other target: Trihedral z 225 m (5520,150) u1 ◮ Interferences: pine forest u0 100 m (5584,126) Interference subspaces 0 5480 m 5620 m x ◮ Canonical element: Signal subspaces dielectric cylinder (11m × 20cm) over the ◮ Canonical element: PC plate (4m × 2m) ground ◮ Ranks: 10 ◮ Ranks: 1019/24 IGARSS 2011 July 2011
  35. 35. SAR Imagery Algorithms Configuration Simulated data Single Polarization Real data Double Polarization Conclusion and Future Work VV polarization CSAR SSDSAR (ρc = 0.8 dB / ρt = 1.5 dB) OBSAR OSISDSAR20/24 IGARSS 2011 July 2011
  36. 36. SAR Imagery Algorithms Configuration Simulated data Single Polarization Real data Double Polarization Conclusion and Future Work VV polarization SSDSAR (ρc = 0.8 dB / ρt = 1.5 dB) CSAR (ρc = 1 dB / ρt = 1.5 dB)20/24 IGARSS 2011 July 2011
  37. 37. SAR Imagery Algorithms Configuration Simulated data Single Polarization Real data Double Polarization Conclusion and Future Work VV polarization SSDSAR (ρc = 0.8 dB / ρt = 1.5 dB) OBSAR (ρc = 0.8 dB / ρt = 1.5 dB)20/24 IGARSS 2011 July 2011
  38. 38. SAR Imagery Algorithms Configuration Simulated data Single Polarization Real data Double Polarization Conclusion and Future Work VV polarization SSDSAR (ρc = 0.8 dB / ρt = 1.5 dB) OSISDSAR (ρc = 1, 3 dB / ρt = 1.3 dB)20/24 IGARSS 2011 July 2011
  39. 39. SAR Imagery Algorithms Configuration Simulated data Single Polarization Real data Double Polarization Conclusion and Future Work Double polarization (dihedral case) SSDSAR (ρ = 1.7 dB) Dihedral case21/24 IGARSS 2011 July 2011
  40. 40. SAR Imagery Algorithms Configuration Simulated data Single Polarization Real data Double Polarization Conclusion and Future Work Double polarization (dihedral case) CSAR SSDSAR (ρ = 1.7 dB) OBSAR OSISDSAR21/24 IGARSS 2011 July 2011
  41. 41. SAR Imagery Algorithms Configuration Simulated data Single Polarization Real data Double Polarization Conclusion and Future Work Double polarization (dihedral case) SSDSAR (ρ = 1.7 dB) CSAR (ρ = 0.7 dB)21/24 IGARSS 2011 July 2011
  42. 42. SAR Imagery Algorithms Configuration Simulated data Single Polarization Real data Double Polarization Conclusion and Future Work Double polarization (dihedral case) SSDSAR (ρ = 1.7 dB) OBSAR (ρ = 2.3 dB)21/24 IGARSS 2011 July 2011
  43. 43. SAR Imagery Algorithms Configuration Simulated data Single Polarization Real data Double Polarization Conclusion and Future Work Double polarization (dihedral case) SSDSAR (ρ = 1.7 dB) OSISDSAR (ρ = 3.7 dB)21/24 IGARSS 2011 July 2011
  44. 44. SAR Imagery Algorithms Simulated data Real data Conclusion and Future Work Outline SAR Imagery Algorithms FoPen Simulated data Real data Conclusion and Future Work22/24 IGARSS 2011 July 2011
  45. 45. SAR Imagery Algorithms Simulated data Real data Conclusion and Future Work Conclusion ◮ Subspace Methods: target and interferences scattering taken into account for the SAR image processing ◮ Double Polarization: reduction on false alarms due to the interferences possible Future Work ◮ Awardeness of the canopy attenuation effets ◮ Cross-polarization (HV, VH)23/24 IGARSS 2011 July 2011
  46. 46. SAR Imagery Algorithms Simulated data Real data Conclusion and Future Work Thank you for your attention! Questions?24/24 IGARSS 2011 July 2011
  47. 47. SAR Imagery Algorithms Simulated data Real data Conclusion and Future Work Single polarization HH CSAR SSDSAR25/24 IGARSS 2011 July 2011
  48. 48. SAR Imagery Algorithms Simulated data Real data Conclusion and Future Work CSAR SSDSAR OBSAR OSISDSAR25/24 IGARSS 2011 July 2011
  49. 49. SAR Imagery Algorithms Simulated data Real data Conclusion and Future Work Single polarization HH (real data) CSAR SSDSAR OBSAR OSISDSAR26/24 IGARSS 2011 July 2011
  50. 50. SAR Imagery Algorithms Simulated data Real data Conclusion and Future Work Single polarization HH (real data) SSDSAR CSAR26/24 IGARSS 2011 July 2011
  51. 51. SAR Imagery Algorithms Simulated data Real data Conclusion and Future Work Single polarization HH (real data) SSDSAR OBSAR26/24 IGARSS 2011 July 2011
  52. 52. SAR Imagery Algorithms Simulated data Real data Conclusion and Future Work Single polarization HH (real data) SSDSAR OSISDSAR26/24 IGARSS 2011 July 2011
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