Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis N. G ökhan  K asapoğlu Dept . of  Electronics and Communic...
Outline <ul><li>Introduction </li></ul><ul><li>0D-Var SAR Data Assimilation   </li></ul><ul><li>SAR Features </li></ul><ul...
SAR Data Assimilation R2 Dual  Polarized SCWA SAR DATA Forecast Analysis Forecast SAR Feature  Extraction Assimilated Obse...
Variational data assimilation <ul><li>Basic idea: Bayes theorem is used to update pdf of model forecast ( x ) using all av...
Variational method of solution <ul><li>Use an iterative descent algorithm to find the analysis increment  ∆x = x-x b  that...
0D-Var SAR Data Assimilation R2 Dual  Polarized SCWA SAR DATA SAR Feature  Extraction Assimilated Observations   SAR Forwa...
Data Assimilation Formulation <ul><li>y   : the observed SAR features </li></ul><ul><li>H  : the forward model,  H(x,  ) ...
RadarSAT-2 ScanSAR data <ul><li>RadarSAT-2 SCWA Dual-polarized data </li></ul><ul><ul><li>C Band, 5.6 GHz </li></ul></ul><...
SAR Features r2_20090226_215200 <ul><li>RadarSAT-2 SCWA SGF Product – HH channel </li></ul>r2_20090301_220402 RADARSAT-2 D...
SAR Features <ul><li>RadarSAT-2 SCWA SGF Product – HV channel </li></ul>r2_20090226_215200 r2_20090301_220402 RADARSAT-2 D...
SAR Features N.G. K asapoğlu ,  IGARSS 2011, Vancouver,Canada   July .  29 , 20 11   Synthetic Aperture Radar Data Assimil...
SAR Features Observed HH-Variance from  r 2_20090226_215200 Observed HH-Dissimilarity from  r 2_20090301_220402 <ul><li>Te...
SAR Features Observed HV-Lee Filtered Image from  R2_20090226_215200 Observed HV-Entropy from  R2_20090301_220402 <ul><li>...
Forward Model IT h   =  ice thickness (from CIS image analysis chart) IC  =  ice concentration (from CIS image analysis ch...
Simulated SAR Features Predicted  HH  from obs  operator and image analysis Observed H H  from  RadarSAT-2 image <ul><li>r...
Simulated SAR Features Predicted  HV-Entropy  from obs  operator and image analysis HV-Entropy  from  RadarSAT-2 image <ul...
Sea - Ice Open Water Discrimination Mean and Standard deviation of SAR feature (  0  HH  Mean ) for   Sea ice (Red)   wit...
Sea - Ice Open Water Discrimination Mean and Standard deviation of SAR feature (  0  HV) for   Sea ice (Red)   with IC>%9...
Sea - Ice Open Water Discrimination Mean and Standard deviation of SAR feature (  0  HV Entropy) for   Sea ice (Red)   wi...
Sea - Ice Open Water Discrimination Mean and Standard deviation of SAR feature (  0  HV  Data Range ) for   Sea ice (Red)...
Simple Forward Model H  : Observation operator (forward model operator) IC : Ice Concentration   i   : Incidence Angle  ...
0D-Var Analysis Results Background_Bias   Background_Std   Analysis_Bias Analysis_std   - 0.074 4   0.19 9 -0.0667 0.194 F...
0D-Var Analysis Results Analysis Increment:   x R2_20090226_215200 R: HH Lee-Filtered Image G: HH Variance B: HV Mean  N....
0D-Var Analysis Results Analysis Increment:   x R2_20090226_215200 N.G. K asapoğlu ,  IGARSS 2011, Vancouver,Canada   Jul...
0D-Var Analysis Results Background_Bias Background_Std   Analysis_Bias Analysis_std   0.1471 0.3057     0.088 9 0.264 5   ...
0D-Var Analysis Results R2_201002 21 _10 3028 Analysis Increment:   x HH H V N.G. K asapoğlu ,  IGARSS 2011, Vancouver,Ca...
0D-Var Analysis Results N.G. K asapoğlu ,  IGARSS 2011, Vancouver,Canada   July .  29 , 20 11   Synthetic Aperture Radar D...
Optimal SAR Feature Selection <ul><li>Statistical ly  independent features   </li></ul><ul><li>Forward model generalizati...
Separability Measures and Discrimination Analysis N.G. K asapoğlu ,  IGARSS 2011, Vancouver,Canada   July .  29 , 20 11   ...
SAR Feature Selection N.G. K asapoğlu ,  IGARSS 2011, Vancouver,Canada   July .  29 , 20 11   Synthetic Aperture Radar Dat...
Feature Selection for Incidence Angle Intervals N.G. K asapoğlu ,  IGARSS 2011, Vancouver,Canada   July .  29 , 20 11   Sy...
Acknowledgements <ul><li>Canadian Ice Service (CIS) </li></ul>N.G. K asapoğlu ,  IGARSS 2011, Vancouver,Canada   July .  2...
Synthetic Aperture Radar Data Assimilation  for Sea Ice Analysis N. G ökhan  K asapoğlu [email_address] Thank you for your...
Upcoming SlideShare
Loading in...5
×

1_kasapoglu_igarss11.ppt

633

Published on

Published in: Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
633
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
15
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

1_kasapoglu_igarss11.ppt

  1. 1. Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis N. G ökhan K asapoğlu Dept . of Electronics and Communication Engineering İ stanbul Technical University , Turkey
  2. 2. Outline <ul><li>Introduction </li></ul><ul><li>0D-Var SAR Data Assimilation </li></ul><ul><li>SAR Features </li></ul><ul><li>SAR Forward Model </li></ul><ul><li>Sea Ice Open Water Discrimination </li></ul><ul><li>A Simple Forward Model </li></ul><ul><li>SAR 0D-Var Analysis Results </li></ul><ul><li>Optimal SAR Feature Selection </li></ul>N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  3. 3. SAR Data Assimilation R2 Dual Polarized SCWA SAR DATA Forecast Analysis Forecast SAR Feature Extraction Assimilated Observations SAR Forward Model DATA Assimilation Mode l N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  4. 4. Variational data assimilation <ul><li>Basic idea: Bayes theorem is used to update pdf of model forecast ( x ) using all available observations ( y ) </li></ul><ul><li>If all pdfs Gaussian, maximum likelihood estimate minimizes the function: </li></ul>J obs J b <ul><li>where: </li></ul><ul><ul><li>x b is the short-term ice-ocean model forecast used as the background state </li></ul></ul><ul><ul><li>B is the background error (forecast error) covariance matrix </li></ul></ul><ul><ul><li>y is the vector of observations </li></ul></ul><ul><ul><li>R is the observation error covariance matrix </li></ul></ul><ul><ul><li>H maps the analysis vector into observation space </li></ul></ul>N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  5. 5. Variational method of solution <ul><li>Use an iterative descent algorithm to find the analysis increment ∆x = x-x b that minimizes the cost function J ( requires gradient of J ) </li></ul><ul><li>If obs operator is linear , then J is quadratic and possible to easily find unique minimum </li></ul><ul><li>Otherwise, obs operators can be periodically re-linearized during minimization </li></ul><ul><li>Can be difficult to find the global minimum of a non-quadratic cost function (when H highly nonlinear) </li></ul><ul><li>Grey is original J with nonlinear H , Red curves are from linearizing H </li></ul>-> N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis J Analysis Variable
  6. 6. 0D-Var SAR Data Assimilation R2 Dual Polarized SCWA SAR DATA SAR Feature Extraction Assimilated Observations SAR Forward Model H(x,  ) DATA Assimilation (0D-Var), J(x) Analysis N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  7. 7. Data Assimilation Formulation <ul><li>y : the observed SAR features </li></ul><ul><li>H : the forward model, H(x,  ) is the predicted SAR feature </li></ul><ul><li>x : ice concentration </li></ul><ul><li> : incidence angle </li></ul><ul><li>R : observation error covariance matrix </li></ul><ul><li>B : background error covariance matrix </li></ul>N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  8. 8. RadarSAT-2 ScanSAR data <ul><li>RadarSAT-2 SCWA Dual-polarized data </li></ul><ul><ul><li>C Band, 5.6 GHz </li></ul></ul><ul><ul><li>HH and HV channels </li></ul></ul><ul><ul><li>500 km 2 coverage </li></ul></ul><ul><ul><li>100 m pixel spacing </li></ul></ul>N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  9. 9. SAR Features r2_20090226_215200 <ul><li>RadarSAT-2 SCWA SGF Product – HH channel </li></ul>r2_20090301_220402 RADARSAT-2 Data and Products © MacDonald, Dettwiler and Associates Ltd. (2009) - All Rights Reserved. N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  10. 10. SAR Features <ul><li>RadarSAT-2 SCWA SGF Product – HV channel </li></ul>r2_20090226_215200 r2_20090301_220402 RADARSAT-2 Data and Products © MacDonald, Dettwiler and Associates Ltd. (2009) - All Rights Reserved. N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  11. 11. SAR Features N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  12. 12. SAR Features Observed HH-Variance from r 2_20090226_215200 Observed HH-Dissimilarity from r 2_20090301_220402 <ul><li>Texture Feature Examples </li></ul>N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  13. 13. SAR Features Observed HV-Lee Filtered Image from R2_20090226_215200 Observed HV-Entropy from R2_20090301_220402 <ul><li>Texture Feature Examples </li></ul>N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  14. 14. Forward Model IT h = ice thickness (from CIS image analysis chart) IC = ice concentration (from CIS image analysis chart)  = incidence angle (know) SAT = surface air temperature (from GEM) SD = snow depth (from GEM) WS = wind speed (from GEM)  = angle between instrument angle (know) and wind direction (from GEM)   = “optimal” model coefficients estimated from “training data” N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  15. 15. Simulated SAR Features Predicted HH from obs operator and image analysis Observed H H from RadarSAT-2 image <ul><li>r 2_20090 301 _2 20402 </li></ul>N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  16. 16. Simulated SAR Features Predicted HV-Entropy from obs operator and image analysis HV-Entropy from RadarSAT-2 image <ul><li>r 2_20090 301 _2 20402 </li></ul>N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  17. 17. Sea - Ice Open Water Discrimination Mean and Standard deviation of SAR feature (  0 HH Mean ) for Sea ice (Red) with IC>%95 and Open Water (Blue) versus incidence Angle N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  18. 18. Sea - Ice Open Water Discrimination Mean and Standard deviation of SAR feature (  0 HV) for Sea ice (Red) with IC>%95 and Open Water (Blue) versus incidence Angle N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  19. 19. Sea - Ice Open Water Discrimination Mean and Standard deviation of SAR feature (  0 HV Entropy) for Sea ice (Red) with IC>%95 and Open Water (Blue) versus incidence Angle N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  20. 20. Sea - Ice Open Water Discrimination Mean and Standard deviation of SAR feature (  0 HV Data Range ) for Sea ice (Red) with IC>%95 and Open Water (Blue) versus incidence Angle N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  21. 21. Simple Forward Model H : Observation operator (forward model operator) IC : Ice Concentration  i : Incidence Angle  o = floor (  i )  o : Rounded Incidence Angle  o = 19,20,...,49 for SCWA Number of Incidence Angle quantization level: 31 N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  22. 22. 0D-Var Analysis Results Background_Bias Background_Std Analysis_Bias Analysis_std - 0.074 4 0.19 9 -0.0667 0.194 F_ID: 3_4_7_8_9_11_13_23 X a =X b +  x 0D-Var Analysis Result X b : Background State; PM data only R2_20090226_215200 N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  23. 23. 0D-Var Analysis Results Analysis Increment:  x R2_20090226_215200 R: HH Lee-Filtered Image G: HH Variance B: HV Mean N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  24. 24. 0D-Var Analysis Results Analysis Increment:  x R2_20090226_215200 N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  25. 25. 0D-Var Analysis Results Background_Bias Background_Std Analysis_Bias Analysis_std 0.1471 0.3057 0.088 9 0.264 5 R2_201002 21 _ 10 3028 X a =X b +  x X b : Background State; PM data only N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  26. 26. 0D-Var Analysis Results R2_201002 21 _10 3028 Analysis Increment:  x HH H V N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  27. 27. 0D-Var Analysis Results N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  28. 28. Optimal SAR Feature Selection <ul><li>Statistical ly independent features  </li></ul><ul><li>Forward model generalization capability  </li></ul><ul><li>Sea-Ice and open water tie points distributions  </li></ul><ul><li>Analysis results statistics  </li></ul><ul><li>Weights of factors listed above x </li></ul>N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  29. 29. Separability Measures and Discrimination Analysis N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  30. 30. SAR Feature Selection N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis Best SAR feature combination selection Top-Down & Bottom-Up Strategies Analysis Bias as a selection criteria Feature Selection for Incidence Angle Intervals
  31. 31. Feature Selection for Incidence Angle Intervals N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  32. 32. Acknowledgements <ul><li>Canadian Ice Service (CIS) </li></ul>N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11 Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
  33. 33. Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis N. G ökhan K asapoğlu [email_address] Thank you for your attention! N.G. K asapoğlu , IGARSS 2011, Vancouver,Canada July . 29 , 20 11
  1. A particular slide catching your eye?

    Clipping is a handy way to collect important slides you want to go back to later.

×