International Journal of Information Engineering and Electronic Business (IJIEEB)
IJIEEB Vol. 2, No. 1, 8 Nov. 2010
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A Method for Building a Mosaic with UAV Images
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Author(s)
Cheng Xing
1,2,*
Jinling Wang
3
Yaming Xu
1,2
Index Terms
UAV, sequence images, mosaicking, optimization
Abstract
At present, satellite and aerial remote sensing are common ways to collect data for territorial resources monitoring in most countries, but they are not effective or rapid enough. Compared with traditional ways of obtaining images, the UAV based platform for photogrammetry and remote sensing is a more flexible and easy way to provide high-resolution images with lower cost. So building UAV based platforms is becoming a hot field throughout the whole world. However, there are also some problems with UAV images, e.g. the views of UAV images from UAV are smaller than those of traditional aerial images, so these images with small views should be pasted together in order to increase the visual field. Therefore, mosaicking UAV images is a critical task. The homographies between sequence images will be affected by the accumulated errors, which will lead to drifts of the position of each image in the mosaic. In this paper, we introduce a two-step optimization method for mosaicking UAV sequence images which can correct the homographies and improve the position of each image in the mosaic. Experimental results will also be presented.
Cite This Paper
Cheng Xing, Jinling Wang, Yaming Xu, "A Method for Building a Mosaic with UAV Images", International Journal of Information Engineering and Electronic Business(IJIEEB), vol.2, no.1, pp.9-15, 2010. DOI:10.5815/ijieeb.2010.01.02
Reference
[1]J. Sun. “Principle and Application of Remote Sensing,” Wuhan University Press, Wuhan, 2003.
[2]J. Semple and G. Kneebone. “Algebraic projective geometry,” Oxford University Press, 1952.
[3]S. Ma and Z. Zhang. “Computer Vision,” Sicience Press, Beijing, 2004.
[4]O. Faugeras. “Three-Dimensional Computer Vision: A Geometric Viewpoint,” MIT Press, Cambride, MA. 1993.
[5]L. Van Gool, T. Moons, and D. Ungureanu. “Affine / photometric invariants for planar intensity patterns,” Proceedings of the 4th European Conference on Computer Vision, Cambridge, UK, 1996, pp. 642-651.
[6]D. Lowe. “Object recognition from local scale-invariant features,” Proceedings of International Conference on Computer Vision, Corfu, Greece, 1999, pp. 1150-1157.
[7]D. Lowe. “Distinctive Image Features from Scale-Invariant Keypoints,” International Journal of Computer vision, vol 60, pp. 91-110, Feb., 2004.
[8]R. Hartley and A. Zisserman. Multiple View Geometry in Computer Vision, 2nd ed, Cambridge University Press, 2004, pp. 258.
[9]P. Wang and Y. Xu. Photogrammetry. Wuhan University Press, 2005, pp. 35-36.
[10]Y. Wang. “Research on Key Technologies of Image Automatic Mosaic on Image Space,” PH.D Dissertation, PLA Information University, 2008, pp. 57-60.
[11]C. Xing, J. Wang and Y. Xu. “Overlap Analysis of the Images from Unmanned Aerial Vehicles,” Proceedings of the International Conference on Electrical and Control Engineering, Wuhan, China, 2010, pp. 1459-1462.
[12]F. Caballero, L. Merino, J. Ferruz, and A. Ollero. “Homography Based Kalman Filter for Mosaic Building. Applications to UAV position estimation,” Proceedings of IEEE International Conference on Robotics and Automation Roma, Italy, 2007, pp. 2004-2009.
[13]W. Press, S. Teukolsky, W. Vetterling and B. Flannery. “Numerical Recipes in C: the Art of Scientific Computing,” 2nd ed., Cambridge University Press, Cambridge, 1992.