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IGARSS-MI-Pritt.pptx
IGARSS-MI-Pritt.pptx
IGARSS-MI-Pritt.pptx
IGARSS-MI-Pritt.pptx
IGARSS-MI-Pritt.pptx
IGARSS-MI-Pritt.pptx
IGARSS-MI-Pritt.pptx
IGARSS-MI-Pritt.pptx
IGARSS-MI-Pritt.pptx
IGARSS-MI-Pritt.pptx
IGARSS-MI-Pritt.pptx
IGARSS-MI-Pritt.pptx
IGARSS-MI-Pritt.pptx
IGARSS-MI-Pritt.pptx
IGARSS-MI-Pritt.pptx
IGARSS-MI-Pritt.pptx
IGARSS-MI-Pritt.pptx
IGARSS-MI-Pritt.pptx
IGARSS-MI-Pritt.pptx
IGARSS-MI-Pritt.pptx
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IGARSS-MI-Pritt.pptx

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  • 1. Stabilization and Georegistration of Aerial Video Over MountainTerrain by Means of 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: Georegistration Georegistration is the assignment of 3-D geographic coordinates to the pixels of an image. It is required for many geospatial applications:  Fusion of imagery with other sensor data  Alignment of imagery with GIS and map graphics  Accurate 3-D geolocation Inaccurate georegistration can be a major problem: Correctly aligned Misaligned GIS 2
  • 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 10-meter 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 3
  • 4. Methods Create predicted images from the DEM, illumination conditions, sensor model estimates and actual images. Register the images while refining the sensor model. Iterate. Aerial Video Sensor Illumination Occlusion Predicted Shadow Images Scene 4
  • 5. Methods (cont) The algorithm identifies tie points between the predicted and the actual images by means of NCC Predicted (normalized cross Image correlation) with RANSAC from DEM outlier removal. Predicted Registration Image from Tie Point Aerial Image Detections 5
  • 6. Methods (cont) The algorithm uses the refined sensor model as the initial guess for the next video frame: Initial Register Refine Next Iterate Finish Camera Frame • Estimate • Predict • Compose • Register to • Iterate for • Trajectory camera images from registration previous each video • Propagate model DEM and fcn & camera frame frame geo data • Use camera camera • LS fit for • Compose from DEM focal length • Register better cam with cam of • Resample & platform images with estimate prev. frame images for GPS if avail. NCC • Iterate for init. cam orthomosaic estimate The refined sensor model enables georegistration.  Exterior orientation: Platform position and rotation angles  Interior orientation: Focal length, pixel aspect ratio, principal point and radial distortion 6
  • 7. Example 1: Aerial Motion Imagery Inputs: Aerial Motion Imagery over 1/3 Arc-second Arizona, U.S. USGS DEM Area: 64 km2 Post Spacing: 10 m 16 Mpix, 3.3 fps, panchromatic 7
  • 8. Example 1 (cont) Problem: Too shaky to find moving objects Zoomed to full resolution (1 m) 8
  • 9. Example 1: Results Outputs:  Sensor camera models  Images georegistered to DEM  Platform trajectory 9
  • 10. Example 1 Results (cont) ATV Vehicle Human Pickup Video is now Truck stabilized, and as a result, moving objects are easily detected. 10
  • 11. Example 2: Oblique Motion Imagery Inputs: Oblique Motion Imagery Over LIDAR DEM Arizona, U.S. Area: 24 km2 Post Spacing: 1 m 16 Mpix, 3.4 fps, pan 11
  • 12. Example 2: Results Target Tracking Stabilized Map Video Inset coordinates Aligned Map Graphics Orthorectified Video Background LIDAR DEM Aligned Map Graphics 12
  • 13. Example 2 Results (cont) How fast does the algorithm converge? IMAGE 1 Camera Iteration The initial error Tie Point Residuals 1 2 3 is high, but it 20 Num tie points: 319 318 282 18 16 RMSE decreases after Image Pixels 14 RMSE: 17.4 4.8 2.9 12 mean only several 10 sigma Mean Δx: 1.4 -0.7 0.1 8 6 iterations. 4 Mean Δy: -3.8 -0.1 0 2 0 Sigma Δx: 15.8 4 2.5 1 2 3 Sigma Δy: 6 2.6 1.5 Camera Iteration Subsequent IMAGE 591 Camera Iteration Tie Point Residuals frames have 1 2 3 3 better initial Num tie 681 687 681 2.5 RMSE sensor model points Image Pixels 2 mean RMSE 2.7 0.6 0.3 1.5 sigma estimates and Mean Δx 1 0 0 1 require only 2 Mean Δy 0.9 0 0 0.5 iterations. 0 Sigma Δx 2.1 0.5 0.3 1 2 3 Sigma Δy 0.9 0.2 0.1 Camera Iteration 13
  • 14. Example 3: Aerial Video Inputs: Aerial Video Over LIDAR DEM Arizona, U.S. Area: 24 km2 720 x 480 Color 30 fps Post Spacing: 1 m 14
  • 15. Example 3: Results Background Map Image coordinates Draped Over DEM Orthorectified Video Aligned Map Graphics 15
  • 16. Example 3 Results (cont) Map Graphics Stay Aligned with Features in Video 16
  • 17. Example 4: Thermal Infrared Video Inputs: Commercial MWIR Video Over White Tank Mountains in Arizona LIDAR DEM Post Spacing: 2 m 1 Mpix, 3.3 fps 17
  • 18. Example 4: Results Video Mosaic Georegistered and Draped Over Mountains in Google Earth Video MosaicBackground Inset:LIDAR DEM Original Video with Map Graphics Overlay 18
  • 19. Demo Click picture to play video 19
  • 20. Conclusion We have introduced a new method for aerial video georegistration and stabilization. It registers images to high-resolution DEMs by:  Generating predicted images from the DEM and sensor model;  Registering these predicted images to the actual images;  Correcting the sensor model estimates with the registration results. Processing speed is 1 sec per 16-Mpix image on a PC. Absolute geospatial accuracy is about 1-2 meters.  We are developing a rigorous error propagation model to quantify the accuracy. Applications:  Video stabilization and mosacs  Cross-sensor registration  Alignment with GIS map graphics 20

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