Automated Registration of
Synthetic Aperture Radar
Imagery to LIDAR
IGARSS 2011, Vancouver, Canada
July 24-29, 2011
Mark Pritt, PhD
Lockheed Martin
Gaithersburg, Maryland
mark.pritt@lmco.com
Kevin LaTourette
Lockheed Martin
Goodyear, Arizona
kevin.j.latourette@lmco.com
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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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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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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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
Sensor
Layover
Optical
Sensor
Scene
Shadow
Occlusion
Scene
Shadow
5
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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Example 1: SAR-LIDAR Registration
COSMO-SkyMed SAR
Image of Mosul, Iraq
Area: 100 km2
21,000 x 20,000 pixels
BuckEye LIDAR DEM
Post Spacing: 1 meter
Absolute Accuracy: 1.5 m (CE90)
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Results
COSMO-SkyMed SAR Image
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Results (cont)
Predicted SAR Image from DEM and
Estimated SAR Camera Model
Flicker with previous slide
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Results (cont)
Normalized Cross-Correlation Image
Between Predicted and Actual Images
Flicker with previous slide
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Results: Zoom
COSMO-SkyMed SAR Image
Note the
SAR layover
and shadow
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Zoom (cont)
Predicted SAR Image from DEM
Note the
SAR layover
and shadow
Flicker with previous slide
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Zoom (cont)
Cross Correlation
Flicker with previous slide
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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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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)
Circular Error
Propagated to Ground
2.1 m (CE90)
This includes
the geospatial
errors in the
DEM and the
registration.
CE90 = circular error 90%
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Results: SAR-MSI Registration
SAR Image: COSMO-SkyMed, Date: Oct 2008, GSD: 1 m
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SAR-MSI Registration (cont)
MSI Image: IKONOS, Date: Oct 2010, GSD: 2.2 m
Flicker with previous slide
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SAR-MSI Registration (cont)
SAR Image: COSMO-SkyMed, Date: Oct 2008, GSD: 1 m
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SAR-MSI Registration (cont)
MSI Image: IKONOS, Date: Oct 2010, GSD: 2.2 m
Flicker with previous slide
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SAR-MSI Registration (cont)
SAR Image: COSMO-SkyMed, Date: Oct 2008, GSD: 1 m
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SAR-MSI Registration (cont)
MSI Image: IKONOS, Date: Oct 2010, GSD: 2.2 m
Flicker with previous slide
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Example 2: SAR-MSI-LIDAR Fusion
COSMOSkyMed
SAR
Waterton,
Colorado
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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Results: EO Image Draped Over DEM
Note alignment
of features
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Results: SAR Image Draped Over DEM
Note alignment
of features
Flicker with previous slide
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Results: MSI Image Draped Over DEM
Note alignment
of features
Flicker with previous slide
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Results: Fly-Through
Click picture above to play movie
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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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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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