Iranian Journal of Remote Sensing and GIS

Iranian Journal of Remote Sensing and GIS

Geometric Assessment of Non-physical Models for Geometric Correction of High Resolution Pushbroom Satellite Image Based on Point and Line Control Feature

Document Type : علمی - پژوهشی

Authors
M Panahi 1
M.J Valadan Zoej 2
S Yavari 3
1 M.Sc. Student, Dep. of Photogrammetry and Remote Sensing, College of Geodesy and Geomatics, K.N. Toosi University of Technology
2 Prof., Dep. of Photogrammetry and Remote Sensing, College of Geodesy and Geomatics, K.N. Toosi University of Technology
3 Postdoctoral Fellow, C-CORE, Memorial University of Newfoundland, St. John’s, Canada
Abstract
Non-physical models have attracted the attention of experts in the field of photogrammetry and remote sensing due to the lack for need of ephemeris data at the time of imaging and not providing raw images by owners of these images. In this paper, a comprehensive research was performed on non-physical models including: 3D Affine Model, First Order Rational Function Model with unequal denominator, SDLT, DLT, Rational Function Model with equal denominator, with the emphasis on the effect of linear and point features as control information to geometrically correct the high spatial resolution images. In addition, a new form of Pushbroom-Projective function is introduced, as a new idea for geometric correction of satellite images. The satellite images used in this research are GeoEye-1 from Urmia and Ikonos from Hamedan. Based on the results obtained, in the case of GeoEye-1 satellite image, First Order Rational Function Model with unequal denominator when using point features as control and +XY term of Rational Function Model with equal denominator when applying linear features as control reached the highest accuracy of 0.75 pixel and 2.03 pixel respectively. In the case of Ikonos satellite image, the +XY term of Rational Function Model with equal denominator when using control point features and First Order Rational Function Model with unequal denominator when using linear control features reached the accuracy of 0.68 pixel and 1.5 pixel respectively at the best. It is worth mentioning that the remaining systematic errors in the case of using linear features as control are always more than those obtained using point control features.
Keywords
Geometric correction of high resolution satellite image (HRSIs)
Line control feature
Point control feature
Systematic error
  1. دادرس جوان، ف.، 1378، کاربرد مدل ریاضی افاین پروجکشن در تصحیح هندسی تصاویر ماهواره‎ای IRS-P5، پایاننامة کارشناسی ارشد، دانشگاه تهران، پردیس دانشکده‎های فنی، گروه مهندسی نقشه‌برداری و ژئوماتیک.
  2. قادری، ق.، 1391، استفاده از عوارض خطی در تصحیح هندسی تصاویر هوایی یا ماهواره‎ای براساس توابع کسری، پایاننامة کارشناسی ارشد، دانشگاه صنعتی خواجه نصیرالدین طوسی، دانشکدة ژئودزی و ژئوماتیک، گروه فتوگرامتری و سنجش از دور.
  3. میلان لک، ا.، 1378، ارزیابی مدل ریاضی DLT جهت تصحیح هندسی تصاویر ماهواره‎ای با آرایش خطی، پایان‌نامة کارشناسی ارشد، دانشگاه صنعتی خواجه نصیرالدین طوسی، دانشکدة ژئودزی و ژئوماتیک، گروه فتوگرامتری و سنجش از دور.
  4. یاوری، س.، 1394، زمین‎مرجع‎سازی اتوماتیک تصاویر با قدرت تفکیک بالای ماهواره‎ای با استفاده از مدل‎های ریاضی سه‎بعدی غیرپارامتریک، پایان‌نامة دکتری، دانشگاه صنعتی خواجه نصیرالدین طوسی، دانشکدة ژئودزی و ژئوماتیک، گروه فتوگرامتری و سنجش از دور.
  5. یاوری، س.، 1385، تصحیح هندسی تصاویر ماهواره‎ای مخدوش‌شده با استفاده از مدل‎های ریاضی سه‎بعدی غیرپارامتریک، پایان‎نامة کارشناسی ارشد، دانشگاه صنعتی خواجه نصیرالدین طوسی، دانشکدة ژئودزی و ژئوماتیک، گروه فتوگرامتری و سنجش از دور.
  6. Elaksher, A., 2011, Potential of Using Automatically Extracted Straight Lines in Rectifying High Resolution Satellite Images, ASPRS 2011 Annual Conference Milwaukee, Wisconsin, May 1– 5, 2011.
  7. Fraser, C.S. & Yamakawa, T., 2004, Insights into the Affine Model for High-Resolution Satellite Sensor Orientation, ISPRS Journal of Photogrammetry & Remote Sensing, 58(5), PP. 275– 288.
  8. Gianinetto, M., Scaioni, M., Mondino, E.B. & Tonolo, F.G., 2004, Satellite Images Geometric Correction Based on Non-Parametric Algorithms and Self-Extracted GCPs, Geoscience and Remote Sensing Symposium, 20–24 Sept. 2004. IGARSS'04 Proceedings, IEEE International, 4, PP. 37755–3758.
  9. Goshtasby, A.A., 2005, 2-D and 3-D Image Registration for Medical, Remote Sensing, and Industrial Applications, John Wiley & Sons, Inc, Hoboken, New Jersey, 255, PP. 43–60.
  10. Habib, A., Morgan, M., Kim, E.M. & Cheng, R., 2004, Linear Features in Photogrammetric Activities, XXth ISPRS Congress, Istanbul, Turkey, Automated Geo-Spatial Data Production and Updating, PP. 610, July 12–23, 2004.
  11. Hu, B., Zhang, Y. & Zhang, J., 2011, Relative Orientation Based on Multi-Features, INTERNATIONAL Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V.
  12. Hu, Y. & Tao, C.V., 2001, A Comprehensive Study of the Rational Function Model for Photogrammetric Processing, Photogrammetric Engineering and Remote Sensing, 67 (12), PP. 1347–1357.
  13. Kubik, K., 1991, Relative and Absolute Orientation Based on Linear Features, ISPRS Journal of Photogrammetry and Remote Sensing, 46(4), PP. 199–204.
  14. Liew, L.H., Wang, Y.C. & Cheah, W.S., 2012, Evaluation of Control Points Distribution on Distortions and Geometric Transformations for Aerial Images Rectification, Procedia Engineering, 41, PP. 1002–1008.
  15. Li, Ch. & Shi, W., 2014, The Generalized-Line-Based Iterative Transformation Model for Imagery Registration and Rectification, IEEE Geoscience and Remote Sensing Letters, 11 (8), PP. 1394–1398.
  16. Long, T. & Jiao, W., 2012, The Geometric Correction Model Based on Areal Features for Multisource Images Rectification, Chinese Academy of Sciences, XXII ISPRS Congress, Vol. XXXIX-B1, PP. 245–249
  17. Maras, E.E., 2015, Improved Non-Parametric Geometric Corrections for Satellite Imagery Through Covariance Constraints, Journal of the Indian Society of Remote Sensing, 43(1), PP. 19–26.
  18. Mulawa, D.C. & Mikhail, E.M., 1988, Photogrammetric Treatment of Linear Features, International Archives of Photogrammetry and Remote Sensing, 27(B3), PP. 383–393.
  19. Okamoto, A., Ono, T., Akamatsu, S., Fraser, C.S., Hattori, S. & Hasegawa, H., 1999. The Geometric Characteristics of Six Alternative Triangulation Models for Satellite Imagery, ASPRS Annual Conference, Portland, Oregon, May 17–21(published on CD-ROM), PP. 64–72.
  20. Ramzi, A., Georgiev, N. & Nedkov, R., 2008, Planimetric Accuracy of Orthorectified Quickbird Imagery Using Non-Parametric Sensor Models, Fourth Scientific Conference with International Participation SPACE, Ecology, Nanotechnology, Safety, 4–7 June 2008, Varna, Bulgaria.
  21. Shaker, A., 2007, Feature-Based Transformation Models for Satellite Image Orientation and Terrain Modeling, Proceedings of the ASPRS 2007 Annual Conference, Tampa, Florida.
  22. Teo, T., 2013, Line-Based Rational Function Model for High-Resolution Satellite Imagery, International Journal of Remote Sensing, 34 (4), PP. 1355–1372.
  23. 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.
  24. Tommaselli, A.M.G. & Tozzi, C.L., 1996, A Recursive Approach to Space Resection Using Straight Lines, Photogrammetric Engineering and Remote Sensing, 62(1), PP. 57–65.
  25. Valadan Zoej, M.J. & Sadeghian, S., 2003, Orbital Parameter Modeling Accuracy Testing of IKONOS Geo Image, Photo-grammetric Journal of Finland, 18 (2), PP. 70–80.
  26. Valadan Zoej, M.J., Mokhtarzade, 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.
  27. Valadan Zoej, M.J. & Sadeghian, S., 2003, Rigorous and Non-Rigorous Photo-grammetric Processing of IKONOS Geo image, Proceedings of Joint Workshop of ISPRS Working groups I/2, I/5, IC WG II/IV, and EARSeL Special Interest Group: 3D remote sensing.
  28. Venkatesan, E. & Selvaragini, S., 2017, A Study on the Geometric Correctin Using Satellite Images, International Journal of Pure and Applied Mathematics, Vol. 116, No. 16, PP. 471–477.
  29. Yavari, S., Valadan Zoej, M.J., Mokhtarzade, M. & Mohammadzadeh, A., 2012, Comparison of Particle Swarm Optimization and Genetic Algorithm in Rational Function Model Optimization, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XXXIX-B1, 2012, XXII ISPRS Congress, 25 August – 01 September 2012, Melbourne, Australia.
  30. Yavari, S., Valadan Zoej, M.J., Mohammad-zadeh, A. & Mokhtarzade, M., 2013, Particle Swarm Optimization of RFM for Georeferencing of Satellite Images, 21 May 2012, IEEE Geoscience and Remote Sensing Letters, 10(1), PP. 135–139.
  31. Yavari, S., Valadan Zoej, M.J., Sahebi, M.R. & Mokhtarzade. M., 2016, An Automatic Novel Structural Linear Feature-Based Matching Based on New Concepts of Mathematically Generated Lines and Points, Photogrammetric Engineering and Remote Sensing, Vol. 82(5), PP. 365–376.
  32. Zang, W., Lin, J., Zhang, B., Tao, H. & Wang, Z., 2011, Line-Based Registration for UAV Remote Sensing Imagery of Wide-Spanning River Basin, 19th International Conference on Geoinformatics, Geoinformatics, 2011 19th International Conference on, IEEE.
  33. Zhang, Z. & Zhang J., 2004, Generalized Point Photogrammetry and its Application, Proceedings of The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Istanbul, Turkey, 2–23 july 2004, 35(B/5), PP. 77–81.
  34. Zhao, Z., Ye, D., Zhang, X., Chen, G., Chen, B., Zhang, B., 2016, Improved Direct Linear Transformation for Parameter Decoupling in Camera Calibration, licensee MDPI, Basel, Switzerland, Algorithms Journal, 9(2(.
  35. Zhang, Z., Zhang, Y., Zhang, J. & Zhang, H., 2008, Photogrammetric Modeling of Linear Features with Generalized Point Photogrammetry, Photogrammetric Engi-neering & Remote Sensing, 74(9), PP. 1119–1127.
  36. Zitova, B. & Flusser, J., 2003, Image Registration Methods a Survey, Image and Vision Computing, 21(11), PP. 977–1000.