Journal of the Indian Society of Remote Sensing

, Volume 46, Issue 12, pp 2081–2092 | Cite as

Evaluation of the RPC Model for Ziyuan-3 Three-line Array Imagery

  • Zhonghua HongEmail author
  • Shengyuan Xu
  • Yun ZhangEmail author
  • Yanling Han
  • Yongjiu Feng
Research Article


Ziyuan-3 (ZY-3) satellite is the first civilian stereo mapping satellite in China and was designed to achieve the 1: 50,000 scale mapping for land and ocean. Rigorous sensor model (RSM) is required to build the relationship between the three-dimensional (3D) object space and two-dimensional (2D) image space of ZY-3 satellite imagery. However, each satellite sensor has its own imaging system with different physical sensor models, which increase the difficulty of geometric integration of multi-source images with different sensor models. Therefore, it is critical to generate generic model, especially rational polynomial coefficients (RPCs) of optical imagery. Recently, relatively a few researches have been conducted on RPCs generation to ZY-3 satellite. This paper proposes an approach to evaluate the performance of RPCs generation from RSM of ZY-3 imagery. Three scenario experiments with different terrain features (such as ocean, hill, city and grassland) are designed and conducted to comprehensively evaluate the replacement accuracies of this approach and analyze the RPCs fitting error. All the experimental results demonstrate that the proposed method achieved the encouraging accuracy of better than 1.946E−04 pixel in both x-axis direction and y-axis direction, and it indicates that the RPCs are suitable for ZY-3 imagery and can be used as a replacement for the RSM of ZY-3 imagery.


Rational polynomial coefficients (RPCs) Ziyuan-3 satellite Rigorous sensor model (RSM) 



The work described in this paper was substantially supported by the National Natural Science Foundation of China (Project No. 41871325), Shanghai Foundation for University Youth Scholars (Project No. ZZHY13033), Innovation Program of Shanghai Municipal Education Commission (Project No. 15ZZ082).


  1. Chen, Y., Xie, Z., Qiu, Z., Zhang, Q., & Hu, Z. (2015). Calibration and validation of ZY-3 optical sensors. IEEE Transactions on Geoscience and Remote Sensing, 53(8), 4616–4626.CrossRefGoogle Scholar
  2. Dial, G., Bowen, H., Gerlach, F., Grodecki, J., & Oleszczuk, R. (2003). IKONOS satellite, imagery, and products. Remote Sensing of Environment, 88(1), 23–36.CrossRefGoogle Scholar
  3. Eftekhari, A., Saadatseresht, M., & Motagh, M. (2013). A study on rational function model generation for TerraSAR-X imagery. Sensors, 13(9), 12030–12043.CrossRefGoogle Scholar
  4. Fraser, C. S., Dial, G., & Grodecki, J. (2006). Sensor orientation via RPCs. ISPRS Journal of Photogrammetry and Remote Sensing, 60, 182–194.CrossRefGoogle Scholar
  5. Fraser, C. S., & Hanley, H. B. (2003). Bias compensation in rational functions for IKONOS satellite imagery. Photogrammetric Engineering & Remote Sensing, 69, 53–57.CrossRefGoogle Scholar
  6. Fraser, C. S., & Hanley, H. B. (2005). Bias-compensated RFMs for sensor orientation of high-resolution satellite imagery. Photogrammetric Engineering & Remote Sensing, 71, 909–915.CrossRefGoogle Scholar
  7. Fraser, C. S., & Ravanbakhsh, M. (2009). Georeferencing accuracy of GeoEye-1 imagery. Photogrammetric Engineering & Remote Sensing, 75, 634–638.Google Scholar
  8. Grodecki, J., & Dial, G. (2003). Block adjustment of high-resolution satellite images described by rational polynomials. Photogrammetric Engineering & Remote Sensing, 69, 59–68.CrossRefGoogle Scholar
  9. Jannati, M., Valadan Zoej, M. J., & Mokhtarzade, M. (2017). Epipolar resampling of cross-track pushbroom satellite imagery using the rigorous sensor model. Sensors, 17(1), 129.CrossRefGoogle Scholar
  10. Li, R., Deshpande, S., Niu, X., Zhou, F., Di, K., & Wu, B. (2008). Geometric integration of aerial and high-resolution satellite imagery and application in shoreline mapping. Marine Geodesy, 31, 143–159.CrossRefGoogle Scholar
  11. Li, R., Zhou, F., Niu, X., & Di, K. (2007). Integration of IKONOS and QuickBird imagery for geopositioning accuracy analysis. Photogrammetric Engineering & Remote Sensing, 73, 1067–1074.Google Scholar
  12. Naeini, A. A., Moghaddam, S. H., Mirzadeh, S. M., Homayouni, S., & Fatemi, S. B. (2017). Multiobjective genetic optimization of terrain-independent RFMs for VHSR satellite images. IEEE Geoscience and Remote Sensing Letters, 14(8), 1368–1372.CrossRefGoogle Scholar
  13. Noh, M. J., & Howat, I. M. (2018). Automatic relative RPC image model bias compensation through hierarchical image matching for improving DEM quality. ISPRS Journal of Photogrammetry and Remote Sensing, 136, 120–133.CrossRefGoogle Scholar
  14. Poli, D. (2007). A rigorous model for spaceborne linear array sensors. Photogrammetric Engineering & Remote Sensing, 73, 187–196.CrossRefGoogle Scholar
  15. Poli, D., Remondino, F., Angiuli, E., & Agugiaro, G. (2015). Radiometric and geometric evaluation of GeoEye-1, WorldView-2 and Pléiades-1A stereo images for 3D information extraction. ISPRS Journal of Photogrammetry and Remote Sensing, 100, 35–47.CrossRefGoogle Scholar
  16. Poli, D., & Toutin, T. (2012). Review of developments in geometric modelling for high resolution satellite pushbroom sensors. The Photogrammetric Record, 27(137), 58–73.CrossRefGoogle Scholar
  17. Tang, X., Zhou, P., Zhang, G., Wang, X., & Pan, H. (2015). Geometric accuracy analysis model of the ZiYuan-3 satellite without GCPs. Photogrammetric Engineering & Remote Sensing, 81(12), 927–934.CrossRefGoogle Scholar
  18. Tao, C. V., & Hu, Y. (2001). A comprehensive study of the rational function model for photogrammetric processing. Photogrammetric Engineering & Remote Sensing, 67, 1347–1357.Google Scholar
  19. Tong, X. H., Hong, Z. H., Liu, S. J., Zhang, X., Xie, H., Li, Z. Y., et al. (2012). Building-damage detection using pre- and post-seismic high-resolution IKONOS satellite stereo imagery: a case study of the May 2008 Wenchuan Earthquake. ISPRS Journal of Photogrammetry and Remote Sensing, 68, 13–27.CrossRefGoogle Scholar
  20. Tong, X. H., Liu, S. J., & Weng, Q. H. (2010). Bias-corrected rational polynomial coefficients for high accuracy geo-positioning of QuickBird stereo imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 65, 218–226.CrossRefGoogle Scholar
  21. Tong, X. H., Xu, Y. S., Ye, Z., Liu, S. J., Tang, X. M., Li, L. Y., et al. (2015). Attitude oscillation detection of the ZY-3 satellite by using multispectral parallax images. IEEE Transactions on Geoscience and Remote Sensing, 53(6), 3522–3534.CrossRefGoogle Scholar
  22. Toutin, T. (2004). Review article: geometric processing of remote sensing images: models, algorithms and methods. International Journal of Remote Sensing, 25, 1893–1924.CrossRefGoogle Scholar
  23. Yavari, S., Valadan Zoej, M. J., Sahebi, M. R., & Mokhtarzade, M. (2018). Accuracy improvement of high resolution satellite image georeferencing using an optimized line-based rational function model. International Journal of Remote Sensing, 39(6), 1655–1670.CrossRefGoogle Scholar

Copyright information

© Indian Society of Remote Sensing 2018

Authors and Affiliations

  1. 1.College of Information TechnologyShanghai Ocean UniversityShanghaiPeople’s Republic of China
  2. 2.College of Marine ScienceShanghai Ocean UniversityShanghaiPeople’s Republic of China

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