Ant Colony Optimization of RFM for Geometric Correction

Document Type : علمی - پژوهشی

Authors

1 Ph.D. Candidate, Dep. of Photogrammetry Engineering, K.N. Toosi University of Technology

2 Associate Prof., Dep. of Photogrammetry Engineering, K.N. Toosi University of Technology

3 Assistant Prof., Dep. of Photogrammetry Engineering, K.N. Toosi University of Technology

Abstract

Due to the absence of either satellite ephemeris information or camera model for various high resolution satellite images, rational functions models (RFMs) are widely used by photogrammetric and remote sensing communities. This method has various disadvantages such as: The dependency of this method on many ground control points (GCPs), numerical complexity and particularly terms selection. As there is no physical meaning for the terms of RFM, in traditional solution all of them are involved in the computational process which causes over-parameterization. In this letter, a modified Ant Colony Optimization is applied to identify the optimal terms for RFMs. For this purpose this method is tested on three images with different geometric correction levels, different coordinate systems (UTM, CT & Geodetic) and different combination of Ground Control Points (GCPs) and Independent Check Points (ICPs), without normalization of the image and ground coordinates. Experimental results demonstrate how well the proposed algorithm can determine an RFM, which is optimal in both the total number of terms and the positional accuracy. The results have showed that the CT coordinate system has the better capability in accuracy and convergence’s speed. As a conclusion, ACO when using for RFM optimization, can achieve subpixel accuracy even with just four GCPs.

Keywords

References

  1. Chen, L.C., Teo, T.A., Liu, C.L., 2006, The Geometrical Comparisons of RSM and RFM for FORMOSAT-2 Satellite Images, Photogrammetric Engineering and Remote Sensing, 72 (5), PP. 573–579.
  2. Dorigo, M., Di Caro, G., 1999a, Ant Colony Optimization: A new meta-heuristic, Proceedings of the 1999 Congress on Evolutionary Computation (CEC’99), Piscataway, NJ, IEEE Press, PP. 1470–1477.
  3. Fraser, C.S., Dial,G., Grodecki, J., 2006, Sensor Orientation via RFMs, ISPRS Journal of Photogrammetry and Remote Sensing, 72(3), PP. 182–194.
  4. Fraser, C.S., Hanley, H.B., 2003, Bias Compensation in Rational Functions for IKONOS Satellite Imagery, Photogrammetric Engineering and Remote Sensing, 69, PP. 53–57.
  5. Fraser, C.S., Hanley, H.B., 2005, Bias-compensated RFMs for Sensor Orientation of High-resolution Satellite Imagery, Photogrammetric Engineering and Remote Sensing, 71(8), PP. 909–915.
  6. Fraser, C.S., Ravanbakhsh, M., 2009, Georeferencing Accuracy of GEOEYE-1 Imagery, Photogrammetric Engineering and Remote Sensing, 75(6), PP. 634–638.
  7. Grodecki, J., Dial, G., 2003, Block Adjustment of High-resolution Satellite Images Described by Rational Functions, Photogrammetric Engineering and Remote Sensing, 69(1), PP. 59–68.
  8. Habib, A., Kim, K., Shin, S.W., Kim, C., Bang, K.I., Kim, E.M., Lee, D.C., 2007, Comprehensive Analysis of Sensor Modeling Alternatives for High-resolution Imaging Satellites, Photogrammetric Engineering and Remote Sensing, 73(11), PP. 1241–1251.
  9. Hu, Y., Tao, C.V., 2002, Updating Solutions of the Rational Function Model Using Additional Control Information, Photogrammetric Engineering and Remote Sensing, 68(7), PP. 715–724.
  10. Li, R., Zhou, F., Niu, X., Di, K., 2007, Integration of IKONOS and QUICKBIRD Imagery for Geopositioning Accuracy Analysis, Photogrammetric Engineering & Remote Sensing, 73(9),
  11. PP. 1067–1074.
  12. Puatanachokchai, C., Mikhail, E.M., 2008, Adjustability and Error Propagation for True Replacement Sensor Models, ISPRS Journal of Photogrammetry and Remote Sensing, 63(3), PP. 352–364.
  13. Tao, C.V., Hu, Y., 2001, A Comprehensive Study of the Rational Function Model Photogrammetric Processing, Photogrammetric Engineering and Remote Sensing, 67 (12), PP. 1347–1357.
  14. Tao, C.V., Hu, Y., 2001b, Use of Rational Function Model for Image Rectification, Canadian Journal of Remote Sensing, 27(6), PP. 593–602.
  15. Tao, C.V., Hu, Y., 2002, 3D Construction Methods based on the Rational Function Model, Photogrammetric Engineering and Remote Sensing, 68 (7), PP. 705–714.
  16. Tao, C.V., Hu, Y., Jiang, W., 2004, Photogrammetric Exploitation of IKONOS Imagery for Mapping Applications, International Journal of Remote Sensing, 25(14), PP. 2833–2853.
  17. Tong, X., Liu, S., Weng, Q., 2010, Bias-corrected Rational Polynomial Coefficients for High Accuracy Geo-positioning of QUICKBIRD Stereo Imagery, ISPRS Journal of Photogrammetry and Remote Sensing, 65(2), PP. 218–226.
  18. Toutin, T., 2004, Review Article: Geometric processing of remote sensing images: models, algorithms and methods, International Journal of Remote Sensing, 25(10), PP. 1893-1924.
  19. Toutin, T., 2006, Comparison of 3D Physical and Empirical Models for Generating DSMs from Stereo HR Images, Photogrammetric Engineering and Remote Sensing, 72 (5), PP. 597–604.
  20. Valadan Zoej, M.J., Mokhtarzadeh, M., Mansourian, A., Ebadi, H., Sadeghian, S., 2007, Rational Function Optimization using Genetic Algorithms, International Journal of Applied Earth Observation and Geoinformation, 9(4), PP. 403–413.
  21. Xiong, Z., Zhang, Y., 2009, A Generic Method for RFM Refinement using Ground Control Information, Photogrammetric Engineering and Remote Sensing, 75(9),
  22. PP. 1083–1092.
  23. Yavari, S., Valadan Zoej, M.J., Mohammadzadeh, M., Mokhtarzade, M., 2013, Particle Swarm Optimization of RFM for Georeferencing of Satellite Images, Geoscience and Remote Sensing Letters, 10(1), PP. 135-139.
  24. Zhang, L., He, X., Balz, T., Wei, X., Liao, M., 2012, Rational Function Modeling for Spaceborne SAR Datasets, ISPRS Journal of Photogrammetry and Remote Sensing, 66(1), PP. 133-145.
Iranian Journal of Remote Sensing & GIS
Volume 7, Issue 2 - Serial Number 2
November 2015
Pages 1-18

Files

Share

How to cite

Statistics