This document presents an efficient automatic geo-registration technique for fusing high resolution SAR and optical images. It uses Speeded-Up Robust Features (SURF) to extract scale and rotation invariant features from the images to select ground control points (GCPs) for geometric correction. An automated method determines the optimal threshold for GCP matching by analyzing how the threshold affects the number of matched points. The technique achieves average georegistration errors of less than one pixel and can process images in minutes. It demonstrates potential for real-time image fusion but requires separate geometric transformations when fusing images with diverse terrain.

1. An Efficient Automatic Geo-registration Technique for High Resolution Spaceborne SAR Image FusionIGARSS 201128/July 2011Woo-Kyung Lee and A.R. KimKorea Aerospace Universitywklee@kau.ac.kr

2. Motivation As the resolution level improves, * 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 regionsSimple approach to the SAR image registration and fusionLet the machine do the rest of the job in almost real timeOne click

3. SAR SensorandGeometricCharacteristicImage processing depends on thesurface characteristics and structures

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 ObservationSAR vs. optics imagesImage acquisition

8. SAR SensorandGeometricCharacteristicEffect of ErrorError SourceSAR sensor - Electronic Time Delay - Slant Range Error - Incidence Angle Estimation - PRF FluctuationEffect of Error - Range Location - Range Scale - Azimuth ScaleError correction method- Geometric Calibration- Deskew - Ground Projection - Image Rotation - Terrain CorrectionEarth - Azimuth Skew - Range Non-Linearity - Foreshortening, Layover, ShadowingEarth - Earth Rotation - Side-looking - Target HeightSource of SAR geocoding errors Platform - Image Orientation Error - Squint Angle - Doppler CentroidPlatform - Inclination Angle - Yaw Angel Error - Pitch Angle Error

9. SAR SensorandGeometricCharacteristicMismatch between SAR and Optical imagesOpticsSARGeometrical distortions in SAR images(a) Azimuth Distortion(b) Non linear Range Error(c) Deskew

10. SAR Geo-correction with satellite internal dataSlant range function has non linear scale variationsAzimuthSlant range imageRangeSland basedGround range imageGround projection exampleReference image(EO image)Ground BasedDiscrepancy exist compared with the reference image

11. Distortion between SAR and EO are case-sensitiveGCP based geometry registrationA reference image is chosen to be used for geometric correction or fusion

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

16. It becomes most essential to pick up the best candidates of GCPsBasic PrincipleChoice of GCPIn general, distinctive features such as road, building, bridges, reflectors are chosen that are easily discriminated for convenience

17. Manually? Or Automatically?? Who will chose what points??Selection of GCPs within SAR images Speckle noise inherence in SAR image makes it difficult to guarantee to pinpoint precise positions that correspond to the reference points.

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 choiceSAR image GCPOptical image GCP

21. MethodologySpeeded-Up Robust Features (SURF) is known to have highly robust performance

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

23. Adaptive for noisy environment and mutll-scale images- Only summing operation is involved in producing integral image to match the scale and calculation is speeded up Selection of matching points(GCP) are performed using feature vectors described by Hessian Matrix

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 algorithmSelection of GCP and matching

28. Block diagram for GCP pair selection

29. Integral image generationFor the given image, an integral set of points are summed together

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

32. GCP candidate generationThe Hessian matrix , corresponding to each pixel, is simplified by summation with adjacent cells

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