1. Automated Registration of
Synthetic Aperture Radar
Imagery to LIDAR
IGARSS 2011, Vancouver, Canada
July 24-29, 2011
Mark Pritt, PhD Kevin LaTourette
Lockheed Martin Lockheed Martin
Gaithersburg, Maryland Goodyear, Arizona
mark.pritt@lmco.com kevin.j.latourette@lmco.com

2. Problem: SAR Image Registration
Registration of SAR and optical imagery is difficult.
 Features appear different.
 Different viewpoints and illumination conditions cause difficulties:
 SAR layover does not match optical foreshortening.
 Shadows do not match.
Conventional techniques rely on linear features.
 But these features can be rare and noisy in SAR imagery.
MSI
image
SAR
image
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3. Solution
Our solution is image registration to a high-resolution
digital elevation model (DEM):
 A DEM post spacing of 1 or 2 meters yields good results.
 It also works with coarser post spacing.
Works with terrain data derived from many sources:
 LIDAR: BuckEye, ALIRT, Commercial
 Stereo Photogrammetry: Socet Set® DSM
 SAR: Stereo and Interferometry
 USGS DEMs
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4. Methods
Create a predicted image from the DEM, illumination
conditions and sensor model estimate.
Register the predicted and the actual images.
Refine the sensor model.
Predicted SAR Image SAR Image
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5. Methods (cont)
The same approach works for SAR and optical sensors.
 Projection into the imaging plane is similar.
 Layover in SAR images is similar to occlusion in optical images.
 Radar shadow is similar to optical shadow.
SAR Optical
Sensor Sensor
Layover Scene Shadow Occlusion Scene Shadow
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6. Methods (cont)
To register SAR and optical images, use the DEM as
the “bridge”.
 Generate a predicted “DEM” image for each SAR and optical image.
 Register the predicted images to the actual images.
 This neatly bypasses the problem of direct SAR-optical registration.
SAR Image DEM MSI Image
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7. Example 1: SAR-LIDAR Registration
COSMO-SkyMed SAR
Image of Mosul, Iraq BuckEye LIDAR DEM
Post Spacing: 1 meter
Area: 100 km2 Absolute Accuracy: 1.5 m (CE90)
21,000 x 20,000 pixels
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8. Results
COSMO-SkyMed SAR Image
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9. Results (cont)
Predicted SAR Image from DEM and
Estimated SAR Camera Model
Flicker with previous slide
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10. Results (cont)
Normalized Cross-Correlation Image
Between Predicted and Actual Images
Flicker with previous slide
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11. Results: Zoom
Note the
SAR layover
COSMO-SkyMed SAR Image and shadow
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12. Zoom (cont)
Note the
SAR layover
Predicted SAR Image from DEM and shadow
Flicker with previous slide
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13. Zoom (cont)
Cross Correlation
Flicker with previous slide
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14. Registration Accuracy
NCC Registration Tie Points
Best shift:
Δx = 16.76m
Δy = 4.27m
After least-squares fit to shift-only registration function
with RANSAC outlier removal, 4572 tie points remained.
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15. Registration Accuracy (cont)
Error Propagation
Statistic x y
Mean Residual 0 pixels 0 pixels
Sigma Residual 0.948 pixels 0.981 pixels
RMSE 1.364 pixels
Circular Error
Propagated to DEM 1.48 m (CE90)
This includes
Circular Error the geospatial
Propagated to Ground 2.1 m (CE90) errors in the
DEM and the
registration.
CE90 = circular error 90%
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16. Results: SAR-MSI Registration
SAR Image: COSMO-SkyMed, Date: Oct 2008, GSD: 1 m
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17. SAR-MSI Registration (cont)
MSI Image: IKONOS, Date: Oct 2010, GSD: 2.2 m
Flicker with previous slide 17

18. SAR-MSI Registration (cont)
SAR Image: COSMO-SkyMed, Date: Oct 2008, GSD: 1 m
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19. SAR-MSI Registration (cont)
MSI Image: IKONOS, Date: Oct 2010, GSD: 2.2 m
Flicker with previous slide 19

20. SAR-MSI Registration (cont)
SAR Image: COSMO-SkyMed, Date: Oct 2008, GSD: 1 m
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21. SAR-MSI Registration (cont)
MSI Image: IKONOS, Date: Oct 2010, GSD: 2.2 m
Flicker with previous slide 21

22. Example 2: SAR-MSI-LIDAR Fusion
Waterton, COSMO-
Colorado SkyMed
SAR
Ikonos
MSI
BuckEye
LIDAR DEM
BuckEye Lidar: March 2003 (4.1 x 5.2 km, 0.75-m post spacing)
Ikonos: July 9, 2001 (1-m GSD). COSMO SkyMed SAR: Oct 31, 2008 (0.5-m GSD)
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23. Results: EO Image Draped Over DEM
Note alignment
of features
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24. Results: SAR Image Draped Over DEM
Note alignment
of features
Flicker with previous slide 24

25. Results: MSI Image Draped Over DEM
Note alignment
of features
Flicker with previous slide 25

26. Results: Fly-Through
Click picture above to play movie
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27. Conclusion
We have introduced a new method for registering
SAR images with other sensor data:
 LIDAR, Digital Elevation Models, Optical Images, MSI
It works by image registration to a high-resolution
DEM.
 It does this by generating a predicted image from the DEM and
sensor model estimate.
 It then registers the predicted and actual images and refines the
sensor model estimate.
Accuracy: 1-2 m CE90
Our approach also extends to the case where no DEM
is available:
 DEM can be generated from stereo EO or interferometric SAR.
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28. Conclusion (cont.)
For an extension to Video Geo-registration:
 Pritt, M & LaTourette, K., Stabilization and Georegistration of Aerial Video
Over Mountain Terrain by Means of LIDAR.
 FR1.T08.4
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