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Agricultural land cover from short revisit SAR data Henning Skriver DTU Space Technical University of Denmark IGARSS 2010 ...
Background <ul><li>Visible/NIR sensors  provide land cover information with high accuracy </li></ul><ul><li>But for an ope...
SAR systems <ul><li>Single polarisation SAR systems </li></ul><ul><ul><li>Backscatter coefficients </li></ul></ul><ul><ul>...
AgriSAR data set (2006) <ul><li>Demmin test site in NE-Germany (E-SAR) </li></ul><ul><li>Crop types (140 polygons) </li></...
AgriSAR 2006 <ul><li>L </li></ul>C
AgriSAR06 L-band Multitemporal HH   HV   VV 0607 0621 0705 0419 0511 0516
AgriSAR06 C-band Multitemporal HH   HV   VV 0607 0621 0705 0419 0511 0516
AgriSAR data set (2009) <ul><li>Flevoland test site in the Netherlands (Radarsat-2) </li></ul><ul><li>Crop types (1072 pol...
AgriSAR 2009  4 th  April
AgriSAR 2009  28 th  April
AgriSAR 2009  22 nd  May
AgriSAR 2009  1 st  June
AgriSAR 2009  5 th  July
AgriSAR 2009  19 th  July
AgriSAR 2009  2 nd  August
Classification methods <ul><li>Scattering mechanisms methods </li></ul><ul><ul><li>Cloude and Pottier decomposition – stat...
Spring - Winter crop discrimination
Classification methods <ul><li>Statistical data-driven methods </li></ul><ul><ul><li>Supervised methods, with training set...
<ul><li>Multi-dimensional parameter vector </li></ul><ul><li>…  with multivariate Gaussion pdf </li></ul><ul><li>Maximize ...
Polarimetric SAR - pdf’s Scattering matrix Covariance matrix Complex Gaussian Complex Wishart Gamma
Bayes ML classification for polarimetric data  <ul><li>Covariance matrix </li></ul><ul><li>…  with complex Wishart pdf </l...
Hoekman and Vissers (2003) classifier <ul><li>5 backscatter intensities </li></ul><ul><li>7 backscatter intensities </li><...
<ul><li>Multitemporal single polarisation </li></ul><ul><li>Multitemporal dual polarisation </li></ul><ul><li>Multitempora...
AgriSAR 2006 polygons
Confusion matrix for classification results One field for each class is used for training Pixel-based classification resul...
Training vs. Test set
AgriSAR06  L-band HH  E-SAR
AgriSAR06  L-band XP  E-SAR
AgriSAR06  L-band HH+XP  E-SAR
AgriSAR06  L-band Lee Wishart  E-SAR
AgriSAR06  L-band Hoekman/Vissers 5  E-SAR
AgriSAR06  C-band VV  E-SAR
AgriSAR06  C-band XP  E-SAR
AgriSAR06  C-band VV+XP  E-SAR
AgriSAR09  C-band VV  RADARSAT-2
AgriSAR09  C-band XP  RADARSAT-2
AgriSAR09  C-band HHVV  RADARSAT-2
AgriSAR09  C-band VVXP  RADARSAT-2
AgriSAR09  C-band Lee  RADARSAT-2
AgriSAR09  C-band Hoekman 5  RADARSAT-2
AgriSAR09  C-band  RADARSAT-2
AgriSAR09  C-band XP  RADARSAT-2
AgriSAR09  C-band VVXP  RADARSAT-2
AgriSAR09  C-band Lee  RADARSAT-2
Conclusions <ul><li>Classification accuracies </li></ul><ul><li>Best accuracies </li></ul><ul><ul><li>L-band 06 XP (2,7%) ...
Conclusions <ul><ul><li>Results for AgriSAR06 campaign may be too optimistic because of limited number of crop types </li>...
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TH2.L09.2 - AGRICULTURAL LAND COVER FROM SHORT REVISIT SAR DATA – SENTINEL-1 OPERATION SIMULATED BY AIRCRAFT AND SATELLITE SAR DATA

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TH2.L09.2 - AGRICULTURAL LAND COVER FROM SHORT REVISIT SAR DATA – SENTINEL-1 OPERATION SIMULATED BY AIRCRAFT AND SATELLITE SAR DATA

  1. 1. Agricultural land cover from short revisit SAR data Henning Skriver DTU Space Technical University of Denmark IGARSS 2010 Honolulu, Hawaii, 26-30 July 2010
  2. 2. Background <ul><li>Visible/NIR sensors provide land cover information with high accuracy </li></ul><ul><li>But for an operational service, cloud cover , can cause problems, which can be resolved by SAR </li></ul><ul><li>Also, SAR data can provide estimates of other parameters , such as soil moisture, vegetation biomass, vegetation structure, vegetation moisture. </li></ul><ul><li>The basis of using SAR data for land cover classification is its sensitivity to the shape and orientation of vegetation components and their moisture content. </li></ul><ul><li>These vegetation parameters change for crops during the growing season, and hence multitemporal data are important </li></ul>
  3. 3. SAR systems <ul><li>Single polarisation SAR systems </li></ul><ul><ul><li>Backscatter coefficients </li></ul></ul><ul><ul><li>Examples: ERS-2 (C-VV), Radarsat-1 (C-HH), JERS-1 (L-HH) </li></ul></ul><ul><li>Dual polarisation SAR systems </li></ul><ul><ul><li>Also, Ratios of Backscatter coefficients, Correlations </li></ul></ul><ul><ul><li>Examples: Envisat (C-VV, C-HH, C-XP), Sentinel-1 (C-VV, C-HH, C-XP) </li></ul></ul><ul><li>Fully polarimetric SAR systems </li></ul><ul><ul><li>Also, Scattering matrix, Covariance/Coherency matrix, Polarimetric param. </li></ul></ul><ul><ul><li>Examples: Radarsat-2 (C), ALOS (L), TerraSAR-X (X) </li></ul></ul>
  4. 4. AgriSAR data set (2006) <ul><li>Demmin test site in NE-Germany (E-SAR) </li></ul><ul><li>Crop types (140 polygons) </li></ul><ul><ul><li>beets, maize </li></ul></ul><ul><ul><li>wheat, barley, rape </li></ul></ul>
  5. 5. AgriSAR 2006 <ul><li>L </li></ul>C
  6. 6. AgriSAR06 L-band Multitemporal HH HV VV 0607 0621 0705 0419 0511 0516
  7. 7. AgriSAR06 C-band Multitemporal HH HV VV 0607 0621 0705 0419 0511 0516
  8. 8. AgriSAR data set (2009) <ul><li>Flevoland test site in the Netherlands (Radarsat-2) </li></ul><ul><li>Crop types (1072 polygons) </li></ul><ul><ul><li>beets, peas, potatoes, maize, spring barley, onion </li></ul></ul><ul><ul><li>winter wheat, grass </li></ul></ul>
  9. 9. AgriSAR 2009 4 th April
  10. 10. AgriSAR 2009 28 th April
  11. 11. AgriSAR 2009 22 nd May
  12. 12. AgriSAR 2009 1 st June
  13. 13. AgriSAR 2009 5 th July
  14. 14. AgriSAR 2009 19 th July
  15. 15. AgriSAR 2009 2 nd August
  16. 16. Classification methods <ul><li>Scattering mechanisms methods </li></ul><ul><ul><li>Cloude and Pottier decomposition – statistical method </li></ul></ul><ul><ul><li>Freeman and Durden decomposition – model based method </li></ul></ul><ul><ul><li>Difficult to relate to real crop classes </li></ul></ul><ul><ul><li>Good results when few classes relate clearly to scattering mechanisms – e.g. forest/non-forest, flooded/non-flooded. </li></ul></ul><ul><li>Knowledge/Rule–based methods </li></ul><ul><ul><li>Methods adapted to physical scattering mechanisms </li></ul></ul><ul><ul><li>Polarimetric parameters are often used and/or multitemporal variation </li></ul></ul><ul><ul><li>Land cover scheme proposed by Pierce and Dobson et al. </li></ul></ul><ul><ul><li>Crop scheme proposed by Baronti and Ferrazoli et al. </li></ul></ul>
  17. 17. Spring - Winter crop discrimination
  18. 18. Classification methods <ul><li>Statistical data-driven methods </li></ul><ul><ul><li>Supervised methods, with training set eventually for each data set </li></ul></ul><ul><ul><li>Backscatter coefficients, Ratios, Polarimetric parameters </li></ul></ul><ul><ul><ul><li>Normally speckle reduced data, and hence Gaussian in stead of e.g. Gamma pdf </li></ul></ul></ul><ul><ul><ul><li>Maximum Likelihood classifier for multivariate Gaussian parameter vector </li></ul></ul></ul><ul><ul><li>Polarimetric data </li></ul></ul><ul><ul><ul><li>Maximum Likelihood classifier for complex Wishart covariance/coherency matrix – Lee classifier </li></ul></ul></ul><ul><ul><ul><li>Alternative representation of covariance/coherency matrix, where all elements are backscatter coefficients – Hoekman Vissers classifier. </li></ul></ul></ul>
  19. 19. <ul><li>Multi-dimensional parameter vector </li></ul><ul><li>… with multivariate Gaussion pdf </li></ul><ul><li>Maximize a posteriori propability for an observation u </li></ul><ul><li>… or alternatively minimize the distance function ( d = - ln (p)) </li></ul>Bayes ML classification for parameter vector
  20. 20. Polarimetric SAR - pdf’s Scattering matrix Covariance matrix Complex Gaussian Complex Wishart Gamma
  21. 21. Bayes ML classification for polarimetric data <ul><li>Covariance matrix </li></ul><ul><li>… with complex Wishart pdf </li></ul><ul><li>Maximize a posteriori propability for an observation x </li></ul><ul><li>… or alternatively minimize the distance function ( d = - ln ( p )) </li></ul>
  22. 22. Hoekman and Vissers (2003) classifier <ul><li>5 backscatter intensities </li></ul><ul><li>7 backscatter intensities </li></ul><ul><li>9 backscatter intensities </li></ul>
  23. 23. <ul><li>Multitemporal single polarisation </li></ul><ul><li>Multitemporal dual polarisation </li></ul><ul><li>Multitemporal Lee classifier </li></ul><ul><li>Multitemporal Hoekman & Vissers classifier </li></ul>Classification methodology
  24. 24. AgriSAR 2006 polygons
  25. 25. Confusion matrix for classification results One field for each class is used for training Pixel-based classification results for all polygons
  26. 26. Training vs. Test set
  27. 27. AgriSAR06 L-band HH E-SAR
  28. 28. AgriSAR06 L-band XP E-SAR
  29. 29. AgriSAR06 L-band HH+XP E-SAR
  30. 30. AgriSAR06 L-band Lee Wishart E-SAR
  31. 31. AgriSAR06 L-band Hoekman/Vissers 5 E-SAR
  32. 32. AgriSAR06 C-band VV E-SAR
  33. 33. AgriSAR06 C-band XP E-SAR
  34. 34. AgriSAR06 C-band VV+XP E-SAR
  35. 35. AgriSAR09 C-band VV RADARSAT-2
  36. 36. AgriSAR09 C-band XP RADARSAT-2
  37. 37. AgriSAR09 C-band HHVV RADARSAT-2
  38. 38. AgriSAR09 C-band VVXP RADARSAT-2
  39. 39. AgriSAR09 C-band Lee RADARSAT-2
  40. 40. AgriSAR09 C-band Hoekman 5 RADARSAT-2
  41. 41. AgriSAR09 C-band RADARSAT-2
  42. 42. AgriSAR09 C-band XP RADARSAT-2
  43. 43. AgriSAR09 C-band VVXP RADARSAT-2
  44. 44. AgriSAR09 C-band Lee RADARSAT-2
  45. 45. Conclusions <ul><li>Classification accuracies </li></ul><ul><li>Best accuracies </li></ul><ul><ul><li>L-band 06 XP (2,7%) </li></ul></ul><ul><ul><li>C-band 06 XP (6%) </li></ul></ul><ul><ul><li>C-band 09 Lee (15,7%) </li></ul></ul>
  46. 46. Conclusions <ul><ul><li>Results for AgriSAR06 campaign may be too optimistic because of limited number of crop types </li></ul></ul><ul><li>L-band and C-band provide comparable results for AgriSAR06 </li></ul><ul><li>Multitemporal acquisitions are essential especially for all modes </li></ul><ul><ul><li>Multitemporal acquisitions improve results a lot for both single/dual pol and polarimetric data, and they provide in general very good results </li></ul></ul><ul><ul><li>Polarimetric data at L-band may only need a few acquisitions (AgriSAR06) </li></ul></ul><ul><li>Results support the concept of the ESA Sentinel-1 mission with short revision time </li></ul><ul><li>At C-band an improvement of from 22% to 16% is obtained using polarimetric data compared to dual-pol data </li></ul>

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