1) The document presents a new method for automatically registering synthetic aperture radar (SAR) imagery to light detection and ranging (LIDAR) data and optical images using digital elevation models (DEMs). 2) The method works by generating a predicted SAR image from the DEM and sensor model, registering this predicted image to the actual SAR image, and refining the sensor model. 3) The results show accurate registration of SAR imagery to LIDAR DEMs and multispectral imagery, with registration errors of 1-2 meters.

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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