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Stabilization and Georegistration of Aerial Video Over Mountain Terrain by Means of LIDAR

Stabilization and Georegistration of Aerial Video Over Mountain Terrain by Means of LIDAR. Mark Pritt, PhD Lockheed Martin Gaithersburg, Maryland mark.pritt@lmco.com. IGARSS 2011, Vancouver, Canada July 24-29, 2011. Kevin LaTourette Lockheed Martin Goodyear, Arizona

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Stabilization and Georegistration of Aerial Video Over Mountain Terrain by Means of LIDAR

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  1. Stabilization and Georegistration of Aerial Video Over Mountain Terrain by Means of LIDAR Mark Pritt, PhD Lockheed Martin Gaithersburg, Maryland mark.pritt@lmco.com IGARSS 2011, Vancouver, Canada July 24-29, 2011 Kevin LaTourette Lockheed Martin Goodyear, Arizona 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

  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

  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 Image Plane Illumination Occlusion Predicted Images Shadow Scene

  5. Methods (cont) The algorithm identifies tie points between the predicted and the actual images by means of NCC (normalized cross correlation) with RANSAC outlier removal. Predicted Image from DEM Predicted Image from Aerial Image Registration Tie Point Detections

  6. Methods (cont) • The algorithm uses the refined sensor model as the initial guess for the next video frame: • 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 Initial Camera Register Refine Next Frame Iterate Finish • Register to previous frame • Compose with cam of prev. frame for init. cam estimate • Iterate for each video frame • Trajectory • Propagate geo data from DEM • Resample images for orthomosaic • Estimate camera model • Use camera focal length & platform GPS if avail. • Predict images from DEM and camera • Register images with NCC • Compose registration fcn & camera • LS fit for better cam estimate • Iterate

  7. Inputs: Example 1: Aerial Motion Imagery Aerial Motion Imagery over Arizona, U.S. 1/3 Arc-second USGS DEM Area: 64 km2 Post Spacing: 10 m 16 Mpix, 3.3 fps, panchromatic

  8. Problem: Too shaky to find moving objects Example 1 (cont) Zoomed to full resolution (1 m)

  9. Example 1: Results • Outputs: • Sensor camera models • Images georegistered to DEM • Platform trajectory

  10. Example 1 Results (cont) ATV Vehicle Human Pickup Truck Video is now stabilized, and as a result, moving objects are easily detected.

  11. Inputs: Example 2: Oblique Motion Imagery Oblique Motion Imagery Over Arizona, U.S. LIDAR DEM Area: 24 km2 Post Spacing: 1 m 16 Mpix, 3.4 fps, pan

  12. Example 2: Results Target Tracking Map coordinates Stabilized Video Inset Aligned Map Graphics Orthorectified Video Background LIDAR DEM Aligned Map Graphics

  13. Example 2 Results (cont) • How fast does the algorithm converge? The initial error is high, but it decreases after only several iterations. Subsequent frames have better initial sensor model estimates and require only 2 iterations.

  14. Inputs: Example 3: Aerial Video LIDAR DEM Aerial Video Over Arizona, U.S. Area: 24 km2 Post Spacing: 1 m 720 x 480 Color 30 fps

  15. Example 3: Results Background Image Draped Over DEM Map coordinates Orthorectified Video Aligned Map Graphics

  16. Example 3 Results (cont) Map Graphics Stay Aligned with Features in Video

  17. Inputs: Example 4: Thermal Infrared Video Commercial LIDAR DEM MWIR Video Over White Tank Mountains in Arizona Post Spacing: 2 m 1 Mpix, 3.3 fps

  18. Example 4: Results Video Mosaic Georegistered and Draped Over Mountains in Google Earth Video Mosaic BackgroundLIDAR DEM Inset: Original Video with Map Graphics Overlay

  19. Demo Click picture to play video

  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

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