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
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
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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
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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
27. This work is motivated to find the decision parameters automatically compromising the performance and the complexitySURF algorithm Selection of GCP and 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
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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
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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
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
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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
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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
76. Need to adopt separate transform functions within the imageAfter correction After correction Coast line area Mountain area Mountain area fusion Coast line fusion
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78. Need to divide blocks and adopt modified transform functions separatelyCoast line Mountain
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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
![Motivation ,[object Object], * the unique feature of the radar imaging becomes prominent and the task of image fusion with optical image becomes complicated, * the number of pixels increases and the amount of resources for calculation such as memory and time consumption escalates exponentially. To relieve the burden of the work and make it done in real time. Efficient image matching in both rural and urban regions Simple approach to the SAR image registration and fusion Let the machine do the rest of the job in almost real time One click](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)