1. An Efficient Automatic Geo-registration Technique for High Resolution Spaceborne SAR Image Fusion IGARSS 2011 28/July 2011 Woo-Kyung Lee and A.R. Kim Korea Aerospace University wklee@kau.ac.kr

4. Radar images suffer from unrealistic distortions

5. Non-linear distortions along range, Shortening from shadow region

6. Inaccurate Doppler parameter estimation leads to geocoding errors

7. Unstability in internal system clock and orbit parametersSystem error Side-looking Observation SAR vs. optics images Image acquisition

8. SAR SensorandGeometricCharacteristic Effect of Error Error Source SAR sensor - Electronic Time Delay - Slant Range Error - Incidence Angle Estimation - PRF Fluctuation Effect of Error - Range Location - Range Scale - Azimuth Scale Error correction method - Geometric Calibration - Deskew - Ground Projection - Image Rotation - Terrain Correction Earth - Azimuth Skew - Range Non-Linearity - Foreshortening, Layover, Shadowing Earth - Earth Rotation - Side-looking - Target Height Source of SAR geocoding errors Platform - Image Orientation Error - Squint Angle - Doppler Centroid Platform - Inclination Angle - Yaw Angel Error - Pitch Angle Error

12. Multiple GCP(Ground Center Point)s are selected and directly applied to individual position error calculation and correction i

13. Based on the selected GCPs, image transforml function is characterized that best describes the discrepancy between the images

14. Original image is re-sampled and re-arranged by the generated transform function

15. To perform geometrical calibration and restore distortion, the GCPs in the SAR images would be re-arranged into the true ground positions

18. A human work of manual GCP selection is never reliable

19. The number of available points are case-sensitive and still limited by the existence of the distinctive features

20. The precision of the GCP location is not fully guaranteed and the error variance may increase in coarse resolution images. Difficulty of GCP choice SAR image GCP Optical image GCP

22. Scale, rotation and illumination-invariant feature descriptor.

24. The size of the constructed Hessian matix can be varied and can be increased to multiple dimensions as desired

25. The number of dimensions is limited by the complexity, time consumption and precision of the image matching.- case sensitive

26. Parameters required for the decision algorithms is set intuitively

27. This work is motivated to find the decision parameters automatically compromising the performance and the complexitySURF algorithm Selection of GCP and matching

28. Block diagram for GCP pair selection

30. The size is variable depending on the scale and complexity of the image

31. Simple summations of intensity levels are performed over two dimensional domain: A +B +C + D

33. The image scale is varied and the simplified Hessian matrix is obtained for each scale space

34. Harr-wavelet responses are calculated and the feature descriptor is generated

35. The polarity of the image intensity variation is investigated and storedHarr wavelet X, Y direction X direction Y direction

37. One-by-one comparison is straightforward but time-consuming and does not guarantee successful matching due to increased ambiguity

38. Construct a look-up table for the feature descriptor

39. Each feature descriptor is indexed depending on their size, variation rate, orientation

40. For a given GCP , a “search process” is performed within other look-up table generated from reference image and the best matching pair is selected

41. Nearest neighbor search is adopted to find the correct matching pairPrinciple

43. Distance multiplication is performed

44. The number of orientation can be increased in order to reduce ambiguity and avoid wrong decision.

45. Appropriate threshold level is required to compare with the distance multiplication and make a decision

46. The GCP match is confirmed when the distance multiplication is less than the threshold level

47. Image Projective Transform function is deduced from the matching GCPsDefine threshold level

48. Overall procedure diagram for image matching

50. GCP candidates are extracted from both images using the same Hessian matrix structure

51. The number of GCP points appear to be close to each other despite the gap in the image qualityGCP extraction from SAR images (a) Stripmap image (b) Scan image

53. Strip mode SAR image over Vancouver, Canada is geo-registered using the reference image in Radarsat-1 SSG format

54. The threshold level is set to be zero for convenience 881 557 Time consumption vs Th. Level GCP # vs. Threshold level GCP selection for raw image Raw Reference 912 544 Time consumption vs Th. Level GCP # vs. Threshold level GCP selection for reference image GCP selection

58. The number of GCP increases consistently when the level of correlation between the two images are highAs the similarity of the images are high, the GCP increases consistently as the “Threshold Level” decreases 1.72 1595 0.81 252 Original Reference 3.21 2680 GCP variation rate 1.47 404 GCP selection

59. Image Matching Demonstration After fusion

60. Mismatch Error Estimate Corrected Reference The average position error is less than one pixel The performance of the matched GCP selection is affected by the image resolution Mismatch error is reduced as the image resolution improves

62. GCPs from the two images are distinguished - The matching GCPs are easily identified by the nearest search algorithm (b) LANDSAT EO image (a) JERS SAR image

66. This procedure is replaced by compute search algorithm, where the threshold level is traced to find the turning point

67. Total elapsed time is within several minutes and will be further reduced by adaptive search algorithmOriginal Reference Corrected GCP selection and matching Fusion

70. There is non-linear discrepancy between slant and ground ranges

71. Generate Errors in geometrical coordinate

72. Need external references to retrieve broken information and correct errors in ground range allocations

73. foreshortening, layover, shadowingLimited information Shadowing Layover Foreshortening

75. Mountain areas are severely distorted from the EO case

76. Need to adopt separate transform functions within the imageAfter correction After correction Coast line area Mountain area Mountain area fusion Coast line fusion

78. Need to divide blocks and adopt modified transform functions separatelyCoast line Mountain

81. A choice of threshold level is required to perform efficient of GCP matching and it can be automated by tracing its variation curve

82. The image matching algorithm works with various SAR and EO images and the average RMSE is measured to be around 1 pixel.

83. Image blocks containing mountain areas need separate GCP matching and transform function to compensate for image distortion