UAV Remote Sensing Image Stitching

  • Hui WuEmail author
  • Jun Chen
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1074)


The majority of image stitching (include UAV remote sensing images stitching) models are homography matrix transformation function, which could effectively simulate the rigid transformation of 2D images in 3D coordinate system. In order to prevent the accumulation of alignment errors (drift) and the projection distortion in the multi-image stitching task, we proposes a novel UAV remote sensing image stitching access consisting of homography transformation and Helmert transformation. In the overlapping and non-overlapping region we utility homography transformation and Helmert transformation respectively. With proposed method the UAV remote sensing image stitching result has fewer alignment errors and less projection distortion.


UAV remote sensing image stitching VFC Helmert APAP 


  1. 1.
    Zaragoza, J., Chin, T.J., Brown, M.S., et al.: As-projective-as-possible image stitching with moving DLT. In: CVPR (2013)Google Scholar
  2. 2.
    Chang, C.H., Sato, Y., Chuang, Y.Y.: Shape-preserving half-projective warps for image stitching. In: CVPR (2014)Google Scholar
  3. 3.
    Lin, C.C., Pankanti, S.U., Ramamurthy, K.N., et al.: Adaptive as-natural-as-possible image stitching. In: CVPR (2015)Google Scholar
  4. 4.
    Brown, M., Lowe, D.G.: Automatic panoramic image stitching using invariant features. IJCV (2007) Google Scholar
  5. 5.
    Triggs, B.: Bundle Adjustment –A Modern Synthesis (1999)Google Scholar
  6. 6.
    Burt, P.J., Adelson, E.H.: A multi resolution spline with application to image mosaics. ACM TOG 2(4), 217–236 (1983)CrossRefGoogle Scholar
  7. 7.
    Gao, J., Kim, S.J., Brown, M.S.: Constructing image panoramas using dual-homography warping Google Scholar
  8. 8.
    Lin, W.Y., Liu, S., Matsushita, Y., et al.: Smoothly varying affine stitching. In: CVPR (2011)Google Scholar
  9. 9.
    Zhang, Z.: Parameter estimation techniques: a tutorial with application to conic fitting. Water Res. 39(15), 3686–3696 (1997)Google Scholar
  10. 10.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRefGoogle Scholar
  11. 11.
    Ma, J., Zhao, J., Tian, J., et al.: Robust point matching via vector field consensus. IEEE Trans. Image Process. Publ. IEEE Sig. Process. Soc. 23(4), 1706–1721 (2014)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Watson, A.: Computing Helmert transformations. J. Comput. Appl. Math. 197(2), 387–394 (2006)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Fischler, M.A.: Random Sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. In: Readings in Computer Vision. pp. 726–740 (1987)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of AutomationChina University of GeosciencesWuhanChina
  2. 2.Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex SystemsWuhanChina

Personalised recommendations