Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography (한국측량학회지)

Korean Society of Surveying, Geodesy, Photogrammetry and Cartography (한국측량학회)
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Conjugate Point Extraction for High-Resolution Stereo Satellite Images Orientation

  • Oh, Jae Hong (Dept. of Civil Engineering, Korea Maritime and Ocean University) ;
  • Lee, Chang No (Dept. of Civil Engineering, Seoul National University of Science and Technology)
  • Received : 2019.01.14
  • Accepted : 2019.04.24
  • Published : 2019.04.30
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Abstract

The stereo geometry establishment based on the precise sensor modeling is prerequisite for accurate stereo data processing. Ground control points are generally required for the accurate sensor modeling though it is not possible over the area where the accessibility is limited or reference data is not available. For the areas, the relative orientation should be carried out to improve the geometric consistency between the stereo data though it does not improve the absolute positional accuracy. The relative orientation requires conjugate points that are well distributed over the entire image region. Therefore the automatic conjugate point extraction is required because the manual operation is labor-intensive. In this study, we applied the method consisting of the key point extraction, the search space minimization based on the epipolar line, and the rigorous outlier detection based on the RPCs (Rational Polynomial Coefficients) bias compensation modeling. We tested different parameters of window sizes for Kompsat-2 across track stereo data and analyzed the RPCs precision after the bias compensation for the cases whether the epipolar line information is used or not. The experimental results showed that matching outliers were inevitable for the different matching parameterization but they were successfully detected and removed with the rigorous method for sub-pixel level of stereo RPCs precision.

Keywords

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Fig. 1. Flow chart of the study

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Fig. 2. Search space minimization

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Fig. 3. Tested Kompsat-2 stereo data and the extracted keypoints (sampled points every 100th)

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Fig. 4. The number of matches as the window size increases whether or not using epipolar information

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Fig. 6. The reprojection error plot as the window size increases for image 1 (a) without epipolar information, (b) with epipolar information

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Fig. 5. The reprojection accuracy matches as the window size increases. (a) without epipolar information, (b) withe pipolar information

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Fig. 7. Outlier detection as the window size increases.(a) without epipolar information, (b) with epipolar information

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Fig. 8. The reprojection accuracy after removing outliersas the window size increases (same results for both epipolar cases)

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Fig. 9. Extracted conjugate points (blue) and removed outliers (red) case of window size 41 pixels

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Fig. 10. Epipolar line before and after RPCs biascompensation (a) a point with epipolar line in #1 image, (b) corresponding epipolar line in #2 image (before RPCs compensation), (c) corresponding epipolar line in #2 image (after RPCs compensation)

Table 1. Specification of tested data

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