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Geolocation Error Analysis of KOMPSAT-5 SAR Imagery Using Monte-Carlo Simulation Method

  • Received : 2019.03.17
  • Accepted : 2019.04.29
  • Published : 2019.04.30

Abstract

Geolocation accuracy is one of the important factors in utilizing all weather available SAR satellite imagery. In this study, an error budget analysis was performed on key variables affecting on geolocation accuracy by generating KOMPSAT-5 simulation data. To perform the analysis, a Range-Doppler model was applied as a geometric model of the SAR imagery. The results show that the geolocation errors in satellite position and velocity are linearly related to the biases in the azimuth and range direction. With 0.03cm/s satellite velocity biases, the simulated errors were up to 0.054 pixels and 0.0047 pixels in the azimuth and range direction, and it implies that the geolocation accuracy is sensitive in the azimuth direction. Moreover, while the clock drift causes a geolocation error in the azimuth direction, a signal delay causes in the range direction. Monte-Carlo simulation analysis was performed to analyze the influence of multiple geometric error sources, and the simulated error was up to 3.02 pixels in the azimuth direction.

Keywords

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Fig. 1. Error budget analysis flow based on Monte-Carlo simulation

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Fig. 2. KOMPSAT-5 SAR images and distribution of GCPs

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Fig. 3. Geolocation accuracy of KOMPSA T-5 images in the study area

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Fig. 4. Distribution of the geolocation error

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Fig. 5. Simulated error by satellite position bias

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Fig. 6. Simulated error by satellite velocity bias

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Fig. 7. Simulated error by clock drift bias

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Fig. 8. Simulated error by signal delay bias

Table 1. Main geometric parameters in SAR imaging system

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Table 2. Information of KOMPSAT-5 images used for the study

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Table 4. Monte-Carlo simulation results (unit: pixel)

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Table 3. Simulated biases for error budget analysis

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References

  1. Ager, T.P. and Bresnahan, P.C. (2009), Geometric Precision in Space Radar Imaging: Results from TerraSAR-X, NGA CCAP Report.
  2. Breit, H., Fritz, T., Balss, U., Lachaise, M., Niedermeier, A., and Vonavka, M. (2010), TerraSAR-X SAR processing and products, IEEE Transactions on Geoscience and Remote Sensing, Vol. 48, No. 2, pp. 727-740. https://doi.org/10.1109/TGRS.2009.2035497
  3. Bresnahan, P.C. and Jamison, T.A. (2007), A Monte Carlo simulation of the impact of sample size and percentile method implementation on imagery geolocation accuracy assessments. In Proceedings of ASPRS 2007 Conference, 7-11 May, Tampa, Florida, USA, pp. 7-11.
  4. Curlander, J.C. (1984), Utilization of spaceborne SAR data for mapping, IEEE Transactions on Geoscience and Remote Sensing, 2, pp. 106-112. https://doi.org/10.1109/TGRS.1984.350601
  5. Curlander, J.C. and McDonough, R.N. (1991), Synthetic Aperture Radar: Systems and Signal Processing, New York, USA, John Wiley & Sons, INC.
  6. Eineder, M., Breit, H., Adam, N., Holzner, J., Suchandt, S., and Rabus, B. (2001), SRTM X-SAR calibrations results, IEEE Geoscience and Remote Sensing Symposium, 9-13 July, Sydney, Ausralia, pp. 748-750.
  7. Eineder, M., Minet, C., Steigenberger, P., Cong, X., and Fritz, T. (2011), Imagine geodesy - toward centimeter-level ranging accuracy with TerraSAR-X, Geoscience and Remote Sensing, IEEE Transactions on Geoscience and Remote Sensing, Vol. 49, No. 2, pp. 661-671. https://doi.org/10.1109/TGRS.2010.2060264
  8. Frey, O., Meier, E., Nuesch, D., and Roth, A. (2004), Geometric error budget for TerraSAR-X, Proceedings of the 5th European Conference on Synthetic Aperture Radar EUSAR, 25-27 May, Ulm, Germany, pp. 513-516.
  9. Hong, S., Choi, Y., Park, I., and Sohn, H.G. (2017), Comparison of orbit-based and time-offset-based geometric correction models for SAR satellite imagery based on error simulation, Sensors, 17, 170. https://doi.org/10.3390/s17010170
  10. Hong, S.H., Sohn, H.G., Kim, S.P., and Jang, H.S. (2013), Error budget analysis for geolocation accuracy of high resolution SAR satellite imagery, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 31, No. 6-1, pp. 447-454. (in Korean with English abstract) https://doi.org/10.7848/ksgpc.2013.31.6-1.447
  11. Hwang, Y., Lee, B.S., Kim, Y.R., Roh, K.M., Jung, O.C., and Kim, H. (2011), GPS-based orbit determination for KOMPSAT-5 satellite, ETRI Journal, 33, pp. 487-496. https://doi.org/10.4218/etrij.11.1610.0048
  12. Jehle, M., Perler, D., Small, D., Schubert, A., and Meier, E. (2008), Estimation of atmospheric path delays in TerraSAR-X data using models vs. measurements, Sensors, 8, pp. 8479-8491. https://doi.org/10.3390/s8128479
  13. Jung, O., Chung, D., Kim, E., Yoon, J., and Hwang, Y. (2014), Analysis on the orbit accuracy of KOMPSAT-5, Aerospace Engineering and Technology, Vol. 13, No. 2, pp. 108-114.
  14. Kim, Y.J., Park, C.S., and Kim, I.H. (2012), Sampling methods and stochastic inference in Monte Carlo building simulation, Architectural Institute of Korea, Vol. 28, No. 6, pp. 227-236. (in Korean with English abstract)
  15. Melchior, P. (1974), Earth tides, Geophysical Surveys, Vol. 1, No. 3, pp. 275-303. https://doi.org/10.1007/BF01449116
  16. Milbert, D. (2016), Solid Earth Tide, http://geodesyworld.github.io/SOFTS/solid.htm (last date accessed: 25 April 2019).
  17. Nonaka, T., Ishizuka, Y., Yamane, N., Shibayama, T., Takagishi, S., and Sasagawa, T. (2008), Evaluation of the geometric accuracy of TerraSAR-X, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Science, 37, pp. 135-140.
  18. Penna, N.T., Bos, M.S., Baker, T.F., and Scherneck, H.G. (2008), Assessing the accuracy of predicted ocean tide loading displacement values, Journal of Geodesy, Vol. 82, No. 12, pp. 893-907. https://doi.org/10.1007/s00190-008-0220-2
  19. Schreier, G. (1993), SAR Geocoding: Data and Systems, Karlsruhe, Germany, Wichmann.
  20. Schubert, A., Jehle, M., Small, D., and Meier, E. (2010), Influence of atmospheric path delay on the absolute geolocation accuracy of TerraSAR-X high-resolution products, IEEE Transactions on Geoscience and Remote Sensing, Vol. 48, No. 2, pp. 751-758. https://doi.org/10.1109/TGRS.2009.2036252
  21. Schubert, A., Jehle, M., Small, D., and Meier, E. (2012), Mitigation of atmospheric perturbations and solid earth movements in a TerraSAR-X time-series, Journal of Geodesy, Vol. 86, No. 4, pp. 257-270. https://doi.org/10.1007/s00190-011-0515-6
  22. Schwerdt, M., Brautigam, B., Bachmann, M., and Doring, B. (2008a), TerraSAR-X calibration results, In 7th European Conference on Synthetic Aperture Radar. VDE, pp. 1-4.
  23. Schwerdt, M., Brautigam, B., Bachmann, M., Doring, B., Schrank, D., and Gonzalez, J.H. (2008b), Final results of the efficient TerraSAR-X calibration method, In 2008 IEEE Radar Conference, 26-30 May, Rome, Italy, pp. 1-6.
  24. Schwerdt, M., Brautigam, B., Bachmann, M., Doring, B., Schrank, D., and Gonzalez, J.H. (2010), Final TerraSAR-X calibration results based on novel efficient methods, IEEE Transactions on Geoscience and Remote Sensing, Vol. 48, No. 2, pp. 677-689. https://doi.org/10.1109/TGRS.2009.2035308
  25. Toutin, T. (2004), Geometric processing of remote sensing images: models, algorithm and methods, International Journal of Remote Sensing, Vol. 25, No. 10, pp. 1893-1924. https://doi.org/10.1080/0143116031000101611
  26. Yoon, J., Keum, J., Shin, J., Kim, J., Lee, S., Bauleo, A., Farina, C., Germani, C., Mappini, M., and Venturini, R. (2011), KOMPSAT-5 SAR design and performance, 2011 3rd I nternational A sia-Pacific Conference on Synthetic Aperture Radar (APSAR), 26-30 September, Seoul, Korea.
  27. Yoon, Y.T., Eineder, M., Yague-Martinez, N., and Montenbruck, O. (2009), TerraSAR-X precise trajectory estimation and quality assessment, IEEE Transactions on Geoscience and Remote Sensing, Vol. 47, No. 6, pp. 1859-1868. https://doi.org/10.1109/TGRS.2008.2006983
  28. Zanin, K.A. (2014), Quality analysis of image geolocation for a space synthetic aperture radar. Solar System Research, Vol. 48, No. 7, pp. 555-560. https://doi.org/10.1134/S0038094614070235