automated registration of synthetic aperture radar imagery to lidar
DESCRIPTION
Automated Registration of Synthetic Aperture Radar Imagery to LIDAR. Mark Pritt, PhD Lockheed Martin Gaithersburg, Maryland [email protected]. IGARSS 2011, Vancouver, Canada July 24-29, 2011. Kevin LaTourette Lockheed Martin Goodyear, Arizona kevin.j.latourette @ lmco.com. - PowerPoint PPT PresentationTRANSCRIPT
Automated Registration of Synthetic Aperture Radar
Imagery to LIDAR
Mark Pritt, PhDLockheed Martin
Gaithersburg, [email protected]
Kevin LaTouretteLockheed MartinGoodyear, [email protected]
IGARSS 2011, Vancouver, CanadaJuly 24-29, 2011
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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.
SAR image
MSI 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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· Create a predicted image from the DEM, illumination conditions and sensor model estimate.
· Register the predicted and the actual images.· Refine the sensor model.
Methods
SAR ImagePredicted SAR Image
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· 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.
Methods (cont)
SAR Sensor
Image Plane
SceneLayover Shadow SceneOcclusion
Optical Sensor
Image Plane
Shadow
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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 BuckEye LIDAR DEM
Area: 100 km2
21,000 x 20,000 pixels
Post Spacing: 1 meterAbsolute 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
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Results (cont)
Normalized Cross-Correlation Image Between Predicted and Actual Images
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Results: Zoom
COSMO-SkyMed SAR ImageNote the
SAR layover and shadow
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Zoom (cont)
Predicted SAR Image from DEM
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Note the SAR layover and shadow
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Zoom (cont)
Cross Correlation
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Registration Accuracy
NCC Registration Tie Points
After least-squares fit to shift-only registration function with RANSAC outlier removal, 4572 tie points remained.
Best shift:Δx = 16.76mΔy = 4.27m
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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 ErrorPropagated to DEM 1.48 m (CE90)
Circular ErrorPropagated 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 RegistrationSAR 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
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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
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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
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Example 2: SAR-MSI-LIDAR Fusion
Waterton, Colorado
IkonosMSI
COSMO- SkyMed
SAR
BuckEyeLIDAR 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 alignmentof features
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Results: SAR Image Draped Over DEM
Note alignmentof features
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Results: MSI Image Draped Over DEM
Note alignmentof features
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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