Presentation on theme: "IGARSS 2011, Vancouver, Canada"— Presentation transcript:
1 IGARSS 2011, Vancouver, Canada Stabilization and Georegistration of Aerial Video Over Mountain Terrain by Means of LIDARIGARSS 2011, Vancouver, CanadaJuly 24-29, 2011Mark Pritt, PhDLockheed MartinGaithersburg, MarylandKevin LaTouretteLockheed MartinGoodyear, Arizona
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 dataAlignment of imagery with GIS and map graphicsAccurate 3-D geolocationInaccurate georegistration can be a major problem:Misaligned GISCorrectly aligned
3 SolutionOur 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, CommercialStereo Photogrammetry: Socet Set® DSMSAR: Stereo and InterferometryUSGS DEMs
4 MethodsCreate predicted images from the DEM, illumination conditions, sensor model estimates and actual images.Register the images while refining the sensor model.Iterate.Aerial Video SensorImage PlaneSceneOcclusionIlluminationShadowPredicted Images
5 Methods (cont) Predicted Image from DEM Predicted Image from Aerial ImageRegistration Tie Point DetectionsThe algorithm identifies tie points between the predicted and the actual images by means of NCC (normalized cross correlation) with RANSAC outlier removal.
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 anglesInterior orientation: Focal length, pixel aspect ratio, principal point and radial distortionInitial CameraEstimate camera modelUse camera focal length & platform GPS if avail.RegisterPredict images from DEM and cameraRegister images with NCCRefineCompose registration fcn & cameraLS fit for better cam estimateIterateNext FrameRegister to previous frameCompose with cam of prev. frame for init. cam estimateIterate for each video frameFinishTrajectoryPropagate geo data from DEMResample images for orthomosaic
8 Problem: Too shaky to find moving objects Example 1 (cont)Problem: Too shaky to find moving objectsZoomed to full resolution (1 m)
9 Example 1: Results Outputs: Sensor camera models Images georegistered to DEMPlatform trajectory
10 Example 1 Results (cont) ATV VehicleHumanPickup TruckVideo is now stabilized, and as a result, moving objects are easily detected.
11 Example 2: Oblique Motion Imagery Inputs:Oblique Motion Imagery Over Arizona, U.S.LIDAR DEMArea: 24 km2Post Spacing: 1 m16 Mpix, 3.4 fps, pan
12 Stabilized Video Inset Example 2: ResultsMap coordinatesStabilized Video InsetOrthorectified VideoBackground LIDAR DEMAligned Map GraphicsTarget Tracking
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.IMAGE 1Camera Iteration123Num tie points:319318282RMSE:126.96.36.199Mean Δx:1.4-0.70.1Mean Δy:-3.8-0.1Sigma Δx:15.842.5Sigma Δy:62.61.5IMAGE 591Camera Iteration123Num tie points681687RMSE188.8.131.52Mean ΔxMean Δy0.9Sigma Δx2.10.5Sigma Δy0.20.1
14 Aerial Video Over Arizona, U.S. Example 3: Aerial VideoInputs:LIDAR DEMArea: 24 km2Post Spacing: 1 mAerial Video Over Arizona, U.S.720 x 480 Color 30 fps
15 Background Image Draped Over DEM Example 3: ResultsMap coordinatesOrthorectified VideoBackground Image Draped Over DEMAligned Map Graphics
16 Example 3 Results (cont) Map Graphics Stay Aligned with Features in Video
17 Example 4: Thermal Infrared Video Inputs:Commercial LIDAR DEMMWIR Video Over White Tank Mountains in ArizonaPost Spacing: 2 m1 Mpix, 3.3 fps
18 Video Mosaic Georegistered and Draped Over Mountains in Google Earth Example 4: ResultsVideo Mosaic Georegistered and Draped Over Mountains in Google EarthBackgroundLIDAR DEMVideo MosaicInset: Original Video with Map Graphics Overlay
20 ConclusionWe 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 mosacsCross-sensor registrationAlignment with GIS map graphics
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