1. A SELF-ADJUSTIVE GEOMETRIC CORRECTION METHOD FOR SERIOUSLY OBLIQUE AERO IMAGE IGARSS 2011 Vancouver, 24-29 July Chunyuan Wang, Ye Zhang, Pigang Liu, Qi Xu, Yanfeng Gu from Harbin Institute of Technology, China

2. Conclusion Experiments & Results Method & System Analysis of the Projection Errors Motivation Content

3. Motivation Geometric Correction Importance Important preprocessing of remote sensing image processing and applications Special Situation Image taken in a large angle Two Important Problems Projection errors caused by curvature of the earth & relief Airspace Satellite space

4. Conclusions Experiments & Results Method & System Analysis of the Projection Errors Motivation Content

5. Analysis of the Projection Errors 1.Projection errors caused by relief Linear displacement between image points aa’: image point displacement caused by relief f: the focal length of the sensor h: the relief height H: imaging height.

6. Analysis of the Projection Errors 2.Projection errors caused by curvature of the earth Projection errors caused by the curvature of the earth increase with the increasing view angles

7. Conclusions Experiments & Results Method & System Analysis of the Projection Errors Starting point Content

8. Method & System Polynomial correction model A fitting method using control points. The quadratic term : effective correct projection errors caused by the curvature of the earth The third dimension: effective correct projection errors caused by relief via Digital Elevation Model Ternary quadratic polynomial （ for i,j,k=0,1,2 ）

9. Method & System In practice, only depending on ternary quadratic polynomial to correct projection errors caused by both curvature of the earth and relief, the correction error is relatively large. Relief-projection error is more complex with a curvature terrain. The adjustable ternary quadratic polynomial Based on the generated characteristics of projection errors, suppose y direction is the direction of large view angle, the improved polynomial model is （ for i,j,k=0,1,2 ）

10. Method & System Polynomial correction process with self-adjustable model

11. Conclusions Experiments & Results Method & System Analysis of the Projection Errors Starting point Content

12. Experiment Dataset: gather from our simulation imaging system Imaging in the curvature surface on our earth model with large view angles. The control points and test points in all the experiments have the same quantity and quality. Two criterions : Root mean square error (RMSE) : correction accuracy. Location errors of high objects (LER) : recovery accuracy of the roof location.

13. Experiment 1 1.Correction of curvature -projection error 80 degrees distorted image Quadratic polynomial Affine correction The accuracy of correction (/pixels)

14. Experiment 2 2. Correction based on the self-adjustable model 65°distorted image Ternary cubic polynomial Adjustable model Reference image Ternary quadratic polynomial

15. Experiment 2 2. Correction based on the self-adjustable model

16. Conclusion For correcting the seriously distorted image, the new self- adjustable ternary quadratic polynomial model alleviates the seriously distortions problem caused by relief and earth curvature and recovers the height objects’ location better. It is experimentally demonstrated that self-adjustable polynomial model outperforms the conventional models and is effective for the seriously distorted image acquired in large view angles.

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