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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
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],N.G. K asapoğlu ,  IGARSS 2011, Vancouver,Canada   July .  29 , 20 11   Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
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
Variational data assimilation ,[object Object],[object Object],J obs J b ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],N.G. K asapoğlu ,  IGARSS 2011, Vancouver,Canada   July .  29 , 20 11   Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
Variational method of solution ,[object Object],[object Object],[object Object],[object Object],[object Object],-> N.G. K asapoğlu ,  IGARSS 2011, Vancouver,Canada   July .  29 , 20 11   Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis J Analysis Variable
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
Data Assimilation Formulation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],N.G. K asapoğlu ,  IGARSS 2011, Vancouver,Canada   July .  29 , 20 11   Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
RadarSAT-2 ScanSAR data ,[object Object],[object Object],[object Object],[object Object],[object Object],N.G. K asapoğlu ,  IGARSS 2011, Vancouver,Canada   July .  29 , 20 11   Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
SAR Features r2_20090226_215200 ,[object Object],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
SAR Features ,[object Object],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
SAR Features N.G. K asapoğlu ,  IGARSS 2011, Vancouver,Canada   July .  29 , 20 11   Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
SAR Features Observed HH-Variance from  r 2_20090226_215200 Observed HH-Dissimilarity from  r 2_20090301_220402 ,[object Object],N.G. K asapoğlu ,  IGARSS 2011, Vancouver,Canada   July .  29 , 20 11   Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
SAR Features Observed HV-Lee Filtered Image from  R2_20090226_215200 Observed HV-Entropy from  R2_20090301_220402 ,[object Object],N.G. K asapoğlu ,  IGARSS 2011, Vancouver,Canada   July .  29 , 20 11   Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
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
Simulated SAR Features Predicted  HH  from obs  operator and image analysis Observed H H  from  RadarSAT-2 image ,[object Object],N.G. K asapoğlu ,  IGARSS 2011, Vancouver,Canada   July .  29 , 20 11   Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
Simulated SAR Features Predicted  HV-Entropy  from obs  operator and image analysis HV-Entropy  from  RadarSAT-2 image ,[object Object],N.G. K asapoğlu ,  IGARSS 2011, Vancouver,Canada   July .  29 , 20 11   Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
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
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
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
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
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
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
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
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
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
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
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
Optimal SAR Feature Selection ,[object Object],[object Object],[object Object],[object Object],[object Object],N.G. K asapoğlu ,  IGARSS 2011, Vancouver,Canada   July .  29 , 20 11   Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
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
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
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
Acknowledgements ,[object Object],N.G. K asapoğlu ,  IGARSS 2011, Vancouver,Canada   July .  29 , 20 11   Synthetic Aperture Radar Data Assimilation for Sea Ice Analysis
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

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1_kasapoglu_igarss11.ppt

  • 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.
  • 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.
  • 5.
  • 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.
  • 8.
  • 9.
  • 10.
  • 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.
  • 13.
  • 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.
  • 16.
  • 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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.
  • 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. 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. 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.
  • 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