Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
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A New Landsat Image Co-Registration and Outlier Removal Techniques

  • Kim, Jong-Hong (School of Civil and Environmental Engineering, Yonsei University) ;
  • Heo, Joon (School of Civil and Environmental Engineering, Yonsei University) ;
  • Sohn, Hong-Gyoo (School of Civil and Environmental Engineering, Yonsei University)
  • Published : 2006.10.31
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Abstract

Image co-registration is the process of overlaying two images of the same scene. One of which is a reference image, while the other (sensed image) is geometrically transformed to the one. Numerous methods were developed for the automated image co-registration and it is known as a timeconsuming and/or computation-intensive procedure. In order to improve efficiency and effectiveness of the co-registration of satellite imagery, this paper proposes a pre-qualified area matching, which is composed of feature extraction with Laplacian filter and area matching algorithm using correlation coefficient. Moreover, to improve the accuracy of co-registration, the outliers in the initial matching point should be removed. For this, two outlier detection techniques of studentized residual and modified RANSAC algorithm are used in this study. Three pairs of Landsat images were used for performance test, and the results were compared and evaluated in terms of robustness and efficiency.

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References

  1. Chen C. S., 2003. RANSAC-Based DARCES: A New Approach to Fast Automatic Registration of Partially Overlapping Range Images. IEEE Trans. on Pattern Analysis and Machine Intelligence, 21(11): 1229-1234
  2. Kim, T. and Y. Im, 2003. Automatic Satellite Image Registration by Combination of Matching and Random Sample Consensus. IEEE Trans. on Geoscience and Remote Sensing, 41(5): 1111-1117 https://doi.org/10.1109/TGRS.2003.811994
  3. Lund, R. E., 1975. Tables for an Approximate Test for Outliers in Linear Models, Technometrics, 17(4): 473-476 https://doi.org/10.2307/1268434
  4. Fischler M. and Bolles R., 1981. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. CACM, 24(6): 381-395 https://doi.org/10.1145/358669.358692
  5. Chum. O, Werner. T, and Matas J., 2004. Epipolar Geometry Estimation via RANSAC Benefits from the Oriented Epipolar Constraint, 17th International Conference on Pattern Recognition (ICPR'04) 1, icpr, pp. 112-115
  6. OKABE T. and SATO Y., 2003. Object Recognition Based on Photometric Alignment Using RANSAC, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '03) 1, cvpr, p. 221
  7. Weisburg, S., 1980. Applied Linear Regression, John Wiley, New York