Journal of IKEEE (전기전자학회논문지)

Institute of Korean Electrical and Electronics Engineers (한국전기전자학회)
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Stereo matching for large-scale high-resolution satellite images using new tiling technique

  • Hong, An Nguyen (R&D Center, FPT IS Soft, Vietnam, and Dept. of Electronics Eng., Myongji University) ;
  • Woo, Dong-Min (Dept. of Electronics Eng., Myongji University)
  • Received : 2013.11.13
  • Accepted : 2013.12.09
  • Published : 2013.12.30
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Abstract

Stereo matching has been grabbing the attention of researchers because it plays an important role in computer vision, remote sensing and photogrammetry. Although most methods perform well with small size images, experiments applying them to large-scale data sets under uncontrolled conditions are still lacking. In this paper, we present an empirical study on stereo matching for large-scale high-resolution satellite images. A new method is studied to solve the problem of huge size and memory requirement when dealing with large-scale high resolution satellite images. Integrating the tiling technique with the well-known dynamic programming and coarse-to-fine pyramid scheme as well as using memory wisely, the suggested method can be utilized for huge stereo satellite images. Analyzing 350 points from an image of size of 8192 x 8192, disparity results attain an acceptable accuracy with RMS error of 0.5459. Taking the trade-off between computational aspect and accuracy, our method gives an efficient stereo matching for huge satellite image files.

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References

  1. Vincent Tao and Yong Hu, "3D reconstruction methods based on the Rational Function Model", Photogrammetric Enginerring & Remote Sensing, Vol. 68, No. 7, pp. 705-71, July 2002
  2. Barbara Zitova and Jan Flusser, "Image registration methods: a survey", Image and Vision Computing, Vol. 21, pp. 977-1000, 2003 https://doi.org/10.1016/S0262-8856(03)00137-9
  3. G. Gupta, M. S. Rawat and R. Bhagava, "Region growing stereo matching method for 3D building reconstruction", Int. J Computational Vision and Robotics, Vol. 2, No. 1, pp. 89-98, 2011 https://doi.org/10.1504/IJCVR.2011.039359
  4. Hae-Yeoun Lee, Taejung Kim, Wonkyu Park and Heung Kyu Lee, "Extraction of digital elevation models from satellite stereo images through stereo matching based on epipolarity and scene geometry", Image and Vision Computing, Vol. 21, pp. 789-706, 2003 https://doi.org/10.1016/S0262-8856(03)00092-1
  5. Nalpantidis Lazaros, Georgios Christou Siraloulis & Antonios Gasteratos, "Review of stereo vision algorithm: From software to hardware", International Journal of Optomechatronics, Vol. 2, No. 4, pp. 435-462, 2008 https://doi.org/10.1080/15599610802438680
  6. Zhen Xiong and Yun Zhang "A novel interest-point-matching algorithm for high-resolution satellite images", IEEE Transaction on Geoscience and Remote Sensing, Vol. 47, No. 12, pp. 4189-4200, December 2009 https://doi.org/10.1109/TGRS.2009.2023794
  7. Changming Sun, "Fast stereo matching using rectangular subregioning and 3D maximum-surface techniques", International Journal of Computer Vision. Vol. 47, pp. 99-117, May 2002 https://doi.org/10.1023/A:1014585622703
  8. Changming Sun, "Multi-Resolution Rectangular Subregioning stereo matching using fast correlation and dynamic programming techniques" CMIS Report No. 98/246, December 1998
  9. Arturo Donate, Xiuwen Liu, and Emmanuel G. Collins, Jr., "Efficient path-based stereo matching with subpixel accuracy", IEEE Transactions on Systems, Man, and Cybernetics, Vol. 41, No. 1, pp. 183-195, February 2011 https://doi.org/10.1109/TSMCB.2010.2049839
  10. Carlos Leung, Ben Appleton and Changming Sun, "I terated dynamic programming and quadtree subregioning for fast stereo matching", Image and Vision Computing, Vol. 26, pp. 1371-1383, 2008 https://doi.org/10.1016/j.imavis.2007.11.013
  11. Andreas Geiger, Martin Roser, and Raquel Urtasun, "Efficient large-scale stereo m,atching", Proceeding of the 10th Asian conference on Computer vision - Volume Part 1. pp. 25-38, 2010
  12. Daniel Scharstein and Richard Szeliski, "A taxonomy and evaluation of dense two-frame stereo correspondence algorithms", International Journal of Computer Vision, Vol. 47, pp. 7-42, 2002 https://doi.org/10.1023/A:1014573219977
  13. M. J. McDonnell, "Box-filtering techniques", Computer Graphics and Image Processing, vol. 17, pp. 65-70, 1981 https://doi.org/10.1016/S0146-664X(81)80009-3
  14. Jia Guo, Ganesh Bikshandi, Basilio B. Fraguela, Maria J. Garzaran and David Padua, "Programming with tiles", Proceeding of the 13th ACM SIGPLAN Symposium on Principles and practice of parallel programming, pp. 111-122, 2008
  15. Daniel Scharstein and Richard Szeliski, "A taxonomy and evaluation of dense two-frame stereo correspondence algorithms", International Journal of Computer Vision, Vol 47, pp. 7-42, 2002 https://doi.org/10.1023/A:1014573219977