Motivation Introduction Algorithm Validation Conclusion




               Iterative Calibration of Relative Platform
    ...
Motivation Introduction Algorithm Validation Conclusion


Outline

      1    Motivation

      2    Introduction
        ...
Motivation Introduction Algorithm Validation Conclusion


Motivation


      We have already know
             Baseline pr...
Motivation Introduction Algorithm Validation Conclusion   Concept Baseline Calibration Expand


Outline

      1    Motiva...
Motivation Introduction Algorithm Validation Conclusion   Concept Baseline Calibration Expand


Concept



      Baseline ...
Motivation Introduction Algorithm Validation Conclusion   Concept Baseline Calibration Expand




      Baseline Error
   ...
Motivation Introduction Algorithm Validation Conclusion   Concept Baseline Calibration Expand




      Geometrical Constr...
Motivation Introduction Algorithm Validation Conclusion   Concept Baseline Calibration Expand




      Baseline Calibrati...
Motivation Introduction Algorithm Validation Conclusion   Concept Baseline Calibration Expand


Expand




      From base...
Motivation Introduction Algorithm Validation Conclusion   Concept Baseline Calibration Expand


Expand




      Because t...
Motivation Introduction Algorithm Validation Conclusion   Coordinate System Iteration


Outline

      1    Motivation

  ...
Motivation Introduction Algorithm Validation Conclusion   Coordinate System Iteration


Coordinate System


      Requirem...
Motivation Introduction Algorithm Validation Conclusion   Coordinate System Iteration




      Transfer Equation: Bji    ...
Motivation Introduction Algorithm Validation Conclusion    Coordinate System Iteration




      System Error
            ...
Motivation Introduction Algorithm Validation Conclusion   Coordinate System Iteration




      Iteration: Starting Point
...
Motivation Introduction Algorithm Validation Conclusion   Coordinate System Iteration


Iteration Steps

             Take...
Motivation Introduction Algorithm Validation Conclusion       Coordinate System Iteration


Iteration Steps

             ...
Motivation Introduction Algorithm Validation Conclusion       Coordinate System Iteration


Iteration Steps

             ...
Motivation Introduction Algorithm Validation Conclusion       Coordinate System Iteration


Iteration Steps

             ...
Motivation Introduction Algorithm Validation Conclusion       Coordinate System Iteration


Iteration Steps

             ...
Motivation Introduction Algorithm Validation Conclusion       Coordinate System Iteration


Iteration Steps

             ...
Motivation Introduction Algorithm Validation Conclusion       Coordinate System Iteration


Iteration Steps

             ...
Motivation Introduction Algorithm Validation Conclusion


Outline

      1    Motivation

      2    Introduction
        ...
Motivation Introduction Algorithm Validation Conclusion




      Data Over Singapore
             8 passes of PALSAR over...
Motivation Introduction Algorithm Validation Conclusion


Results:Relative Position Iteration
                            ...
Motivation Introduction Algorithm Validation Conclusion


Results:Displacement plotting without weight
coefficient


      ...
Motivation Introduction Algorithm Validation Conclusion


Results:Displacement plotting with weight coefficient



        ...
Motivation Introduction Algorithm Validation Conclusion


Results:Differential interferogram after calibration




       ...
Motivation Introduction Algorithm Validation Conclusion


Outline

      1    Motivation

      2    Introduction
        ...
Motivation Introduction Algorithm Validation Conclusion


Conclusion




                                                 ...
Motivation Introduction Algorithm Validation Conclusion


Conclusion


      Concept
             Satellite platform posit...
Motivation Introduction Algorithm Validation Conclusion


Conclusion


      Concept
             Satellite platform posit...
Motivation Introduction Algorithm Validation Conclusion


Conclusion


      Concept
             Satellite platform posit...
Motivation Introduction Algorithm Validation Conclusion


Conclusion




      Possible Application
             Orbit refi...
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Iterative calibration of relative platform position a new_method for_baseline_estimation

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Iterative calibration of relative platform position a new_method for_baseline_estimation

  1. 1. Motivation Introduction Algorithm Validation Conclusion Iterative Calibration of Relative Platform Position: A New Method for Baseline Estimation Tiangang Yin1 , Emmanuel Christophe1 , Soo Chin Liew1 , Sim Heng Ong2 1 C ENTRE FOR R EMOTE I MAGING , S ENSING AND P ROCESSING 2 D EPT. OF E LECTRICAL AND C OMPUTER E NGINEERING , N ATIONAL U NIVERSITY OF S INGAPORE IGARSS 2010, Honolulu
  2. 2. Motivation Introduction Algorithm Validation Conclusion Outline 1 Motivation 2 Introduction Concept Baseline Calibration Expand 3 Algorithm Coordinate System Iteration 4 Validation 5 Conclusion IGARSS 2010, Honolulu
  3. 3. Motivation Introduction Algorithm Validation Conclusion Motivation We have already know Baseline precision is significant to the interferometric accuracy Precise estimation is required Idea Interferometric result can provide information on baseline Concept can be extended under multiple passes condition, from baseline to individual sensor position Iteration and Constraint IGARSS 2010, Honolulu
  4. 4. Motivation Introduction Algorithm Validation Conclusion Concept Baseline Calibration Expand Outline 1 Motivation 2 Introduction Concept Baseline Calibration Expand 3 Algorithm Coordinate System Iteration 4 Validation 5 Conclusion IGARSS 2010, Honolulu
  5. 5. Motivation Introduction Algorithm Validation Conclusion Concept Baseline Calibration Expand Concept Baseline Concept Refer to the relative distance between two sensors Highlight “relative” depends on the chosen master image as coordinate origin build a coordinate system base on master image position, normally described using “parallel” and “perpendicular” Initially estimated using orbital information, interpolated from platform position vector IGARSS 2010, Honolulu
  6. 6. Motivation Introduction Algorithm Validation Conclusion Concept Baseline Calibration Expand Baseline Error The root of baseline estimation error is the inaccurate platform position from orbit data It can happen on any of the interferometric pair All the interferograms will be wrong with the same inaccurate path IGARSS 2010, Honolulu
  7. 7. Motivation Introduction Algorithm Validation Conclusion Concept Baseline Calibration Expand Geometrical Constraint The geometric representation of multiple platform positions can be constructed as polygon(2D) or polyhedron(3D) Using the orbit estimated baseline, this geometric representation can be constructed IGARSS 2010, Honolulu
  8. 8. Motivation Introduction Algorithm Validation Conclusion Concept Baseline Calibration Expand Baseline Calibration In the past method, error of perpendicular baseline can be reduced by using GCP or reference DEM However, the correction is only on the relative distance. No guarantee for the corrected baseline. IGARSS 2010, Honolulu
  9. 9. Motivation Introduction Algorithm Validation Conclusion Concept Baseline Calibration Expand Expand From baseline to relative position When more information on platform position can be interpreted from data, global constraint of platform position is needed. Without constraint, the geometry of platform positions will break. IGARSS 2010, Honolulu
  10. 10. Motivation Introduction Algorithm Validation Conclusion Concept Baseline Calibration Expand Expand Because the problem will become very complicated in 3D when more passes are used An iterative optimization method will be provided under geometry constraint Global baseline calibration Detection and quantitative calibration of any pass with inaccurate orbit information IGARSS 2010, Honolulu
  11. 11. Motivation Introduction Algorithm Validation Conclusion Coordinate System Iteration Outline 1 Motivation 2 Introduction Concept Baseline Calibration Expand 3 Algorithm Coordinate System Iteration 4 Validation 5 Conclusion IGARSS 2010, Honolulu
  12. 12. Motivation Introduction Algorithm Validation Conclusion Coordinate System Iteration Coordinate System Requirement easy to transfer system from one master image to another error is small enough TCN (Track, Cross-track and Normal) coordinates is chosen −P ˆ n×V ˆ n= ˆ c= ˆ= c×n t ˆ ˆ (1) |P| ˆ |n×V | IGARSS 2010, Honolulu
  13. 13. Motivation Introduction Algorithm Validation Conclusion Coordinate System Iteration Transfer Equation: Bji −Bij Is it valid? Assumption can be made that all of the platform have the same direction of V Image pixels within one range row will share the same baseline TCN coordinates | Bij · c |2 + | Bij · ˆ |2 ˆ t ∆θ = arctan (2) Ai + R Ai : the platform altitude of image i (691.65 km for ALOS) R: the radius of the earth (6378.1 km) IGARSS 2010, Honolulu
  14. 14. Motivation Introduction Algorithm Validation Conclusion Coordinate System Iteration System Error The baseline component along ˆ is very small t ˆ Therefore, for baseline of 1 km along c , the axis error is 0.0081 ◦ ˆ the baseline error is Bij · c × tan ∆θ 14 cm for this system Conclude: TCN coordinates system will be considered at corresponding point between all passes Bji −Bij (3) IGARSS 2010, Honolulu
  15. 15. Motivation Introduction Algorithm Validation Conclusion Coordinate System Iteration Iteration: Starting Point K + 1 passes over same area Differential interferogram and baseline is generated for all combinations Processed with both baseline vector and baseline changing rate Initialization: Bji = −Bij ˙ ˙ Bji = −Bij (4) IGARSS 2010, Honolulu
  16. 16. Motivation Introduction Algorithm Validation Conclusion Coordinate System Iteration Iteration Steps Take one pass as master image, calculate the baseline error to be corrected IGARSS 2010, Honolulu
  17. 17. Motivation Introduction Algorithm Validation Conclusion Coordinate System Iteration Iteration Steps Take one pass as master image, calculate the baseline error to be corrected (n) 1 Average the result: ∆Pi = K × j=i ∆Bij IGARSS 2010, Honolulu
  18. 18. Motivation Introduction Algorithm Validation Conclusion Coordinate System Iteration Iteration Steps Take one pass as master image, calculate the baseline error to be corrected (n) 1 Average the result: ∆Pi = K × j=i ∆Bij (n) Update all the baseline vectors: Bij = Bij + ∆Pi 1 (n) A weight coefficient n can be added before ∆Pi to slow down the convergence IGARSS 2010, Honolulu
  19. 19. Motivation Introduction Algorithm Validation Conclusion Coordinate System Iteration Iteration Steps Take one pass as master image, calculate the baseline error to be corrected (n) 1 Average the result: ∆Pi = K × j=i ∆Bij (n) Update all the baseline vectors: Bij = Bij + ∆Pi 1 (n) A weight coefficient n can be added before ∆Pi to slow down the convergence Update the reversed baseline Bji IGARSS 2010, Honolulu
  20. 20. Motivation Introduction Algorithm Validation Conclusion Coordinate System Iteration Iteration Steps Take one pass as master image, calculate the baseline error to be corrected (n) 1 Average the result: ∆Pi = K × j=i ∆Bij (n) Update all the baseline vectors: Bij = Bij + ∆Pi 1 (n) A weight coefficient n can be added before ∆Pi to slow down the convergence Update the reversed baseline Bji Change another master image and go back to first step, until all of the images have been taken once as master image IGARSS 2010, Honolulu
  21. 21. Motivation Introduction Algorithm Validation Conclusion Coordinate System Iteration Iteration Steps Take one pass as master image, calculate the baseline error to be corrected (n) 1 Average the result: ∆Pi = K × j=i ∆Bij (n) Update all the baseline vectors: Bij = Bij + ∆Pi 1 (n) A weight coefficient n can be added before ∆Pi to slow down the convergence Update the reversed baseline Bji Change another master image and go back to first step, until all of the images have been taken once as master image Calculate the total displacement of all platform: (n) ∆P (n) = K +1 | ∆Pi | i=1 IGARSS 2010, Honolulu
  22. 22. Motivation Introduction Algorithm Validation Conclusion Coordinate System Iteration Iteration Steps Take one pass as master image, calculate the baseline error to be corrected (n) 1 Average the result: ∆Pi = K × j=i ∆Bij (n) Update all the baseline vectors: Bij = Bij + ∆Pi 1 (n) A weight coefficient n can be added before ∆Pi to slow down the convergence Update the reversed baseline Bji Change another master image and go back to first step, until all of the images have been taken once as master image Calculate the total displacement of all platform: (n) ∆P (n) = K +1 | ∆Pi | i=1 Iteration n finished, Take n = n + 1 and restart IGARSS 2010, Honolulu
  23. 23. Motivation Introduction Algorithm Validation Conclusion Outline 1 Motivation 2 Introduction Concept Baseline Calibration Expand 3 Algorithm Coordinate System Iteration 4 Validation 5 Conclusion IGARSS 2010, Honolulu
  24. 24. Motivation Introduction Algorithm Validation Conclusion Data Over Singapore 8 passes of PALSAR over the Singapore between December 2006 and September 2009 are used SRTM is used as reference DEM GAMMA software is used for the interferograms Python used for programming Starting Point: IGARSS 2010, Honolulu
  25. 25. Motivation Introduction Algorithm Validation Conclusion Results:Relative Position Iteration h a n g e Vi h a n g e Vi XC e XC e F- w F- w PD PD er er ! ! W W O O N N y y bu bu to to k k lic lic C C w w m m w w w w o o .d o .c .d o .c c u -tr a c k c u -tr a c k 250 Relative Normal Corrdinate(m) Before iteration Before iteration After iteration After iteration 160 200 20070623 159.5 150 20070923 Relative Normal Corrdinate(m) 159 100 −95 −94.5 −94 −93.5 −93 Relative Cross−Track Coordinate(m) 50 (b) for 20070923 0 20090928 Relative Normal Corrdinate(m) 16 Before iteration 20081226 20090628 After iteration 15 −50 20061221 14 20090210 13 −100 20081110 12 11 −150 −200 −100 0 100 200 300 72 74 76 78 Relative Cross−Track Coordinate(m) Relative Cross−Track Coordinate(m) (a) Global Relative Position Iteration (c) for 20090928 IGARSS 2010, Honolulu
  26. 26. Motivation Introduction Algorithm Validation Conclusion Results:Displacement plotting without weight coefficient 20 Total Displacement ∆P(n) 18 20081226 16 20061221 20070923 Displacement ∆P(m) 14 20090928 The total 12 20090210 20070623 displacement 10 20081110 20090628 ∆P (n) 8 converges 6 4 2 0 0 1 2 3 4 5 6 7 8 9 10 Interation Number n IGARSS 2010, Honolulu
  27. 27. Motivation Introduction Algorithm Validation Conclusion Results:Displacement plotting with weight coefficient 20 Total Displacement ∆P(n) The 18 20081226 convergence 16 20061221 20070923 is slower but 14 20090928 Displacement ∆P(m) 20090210 result in a 12 20070623 20081110 smaller value 10 20090628 8 Speed can 6 neither be too 4 slow nor too 2 fast 0 0 1 2 3 4 5 6 7 8 9 10 Interation Number n IGARSS 2010, Honolulu
  28. 28. Motivation Introduction Algorithm Validation Conclusion Results:Differential interferogram after calibration Before iteration 16 After iteration Relative Normal Corrdinate(m) 15 14 13 12 11 71 72 73 74 75 76 77 78 79 Relative Cross−Track Coordinate(m) IGARSS 2010, Honolulu
  29. 29. Motivation Introduction Algorithm Validation Conclusion Outline 1 Motivation 2 Introduction Concept Baseline Calibration Expand 3 Algorithm Coordinate System Iteration 4 Validation 5 Conclusion IGARSS 2010, Honolulu
  30. 30. Motivation Introduction Algorithm Validation Conclusion Conclusion IGARSS 2010, Honolulu
  31. 31. Motivation Introduction Algorithm Validation Conclusion Conclusion Concept Satellite platform position can be relatively calibrated from multiple interferograms IGARSS 2010, Honolulu
  32. 32. Motivation Introduction Algorithm Validation Conclusion Conclusion Concept Satellite platform position can be relatively calibrated from multiple interferograms Result The SAR passes which gives inaccurate platform position are successfully detected and calibrated IGARSS 2010, Honolulu
  33. 33. Motivation Introduction Algorithm Validation Conclusion Conclusion Concept Satellite platform position can be relatively calibrated from multiple interferograms Result The SAR passes which gives inaccurate platform position are successfully detected and calibrated Disadvantage Platform position can only be calibrated along perpendicular baseline IGARSS 2010, Honolulu
  34. 34. Motivation Introduction Algorithm Validation Conclusion Conclusion Possible Application Orbit refinement for SAR Baseline problem for deformation monitoring, like earthquake IGARSS 2010, Honolulu

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