Multitemporal Aerial Image Registration Using Semantic Features

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11872)


A semantic feature extraction method for multitemporal high resolution aerial image registration is proposed in this paper. These features encode properties or information about temporally invariant objects such as roads and help deal with issues such as changing foliage in image registration, which classical handcrafted features are unable to address. These features are extracted from a semantic segmentation network and have shown good robustness and accuracy in registering aerial images across years and seasons in the experiments.


Image registration Semantic features Convolutional neural networks 


  1. 1.
    ACTmapi Aerial Imagery.
  2. 2.
    Chaurasia, A., Culurciello, E.: LinkNet: exploiting encoder representations for efficient semantic segmentation. In: 2017 IEEE Visual Communications and Image Processing, VCIP 2017, vol. 2018, pp. 1–4, June 2018Google Scholar
  3. 3.
    Costea, D., Leordeanu, M.: Aerial image geolocalization from recognition and matching of roads and intersections, pp. 118.1–118.12, May 2017Google Scholar
  4. 4.
    Dellinger, F., Delon, J., Gousseau, Y., Michel, J., Tupin, F.: SAR-SIFT: a SIFT-like algorithm for SAR images. IEEE Trans. Geosci. Remote. Sens. 53(1), 453–466 (2015)CrossRefGoogle Scholar
  5. 5.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)MathSciNetCrossRefGoogle Scholar
  6. 6.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. \(\rm {arXiv}\).\(\rm {org}\) 7(3), 171–180 (2015)Google Scholar
  7. 7.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (2015)Google Scholar
  8. 8.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRefGoogle Scholar
  9. 9.
    Gong, M., Zhao, S., Jiao, L., Tian, D., Wang, S.: A novel coarse-to-fine scheme for automatic image registration based on SIFT and mutual information. IEEE Trans. Geosci. Remote. Sens. 52(7), 4328–4338 (2013)CrossRefGoogle Scholar
  10. 10.
    OpenStreetMap Contributors: Planet dump (2017).
  11. 11.
    Paszke, A., et al.: Automatic differentiation in PyTorch (2017)Google Scholar
  12. 12.
    Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Sedaghat, A., Mokhtarzade, M., Ebadi, H.: Uniform robust scale-invariant feature matching for optical remote sensing images. IEEE Trans. Geosci. Remote Sens. 49(11), 4516–4527 (2011)CrossRefGoogle Scholar
  14. 14.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations, pp. 1–14 (2015)Google Scholar
  15. 15.
    Welch, B.L.: The generalization of ‘Student’s’ problem when several different population variances are involved. Biometrika 34(1–2), 28–35 (1947)MathSciNetzbMATHGoogle Scholar
  16. 16.
    Dai, X., Khorram, S.: The effects of image misregistration on the accuracy of remotely sensed change detection. IEEE Trans. Geosci. Remote. Sens. 36(5), 1566–1577 (1998)CrossRefGoogle Scholar
  17. 17.
    Yang, K., Pan, A., Yang, Y., Zhang, S., Ong, S., Tang, H.: Remote sensing image registration using multiple image features. Remote Sens. 9(6), 581 (2017)CrossRefGoogle Scholar
  18. 18.
    Yang, Z., Dan, I., Yang, Y.: Multi-temporal remote sensing image registration using deep convolutional features. IEEE Access 6, 38544–38555 (2018)CrossRefGoogle Scholar
  19. 19.
    Ye, F., Su, Y., Xiao, H., Zhao, X., Min, W.: Remote sensing image registration using convolutional neural network features. IEEE Geosci. Remote Sens. Lett. 15(2), 232–236 (2018)CrossRefGoogle Scholar
  20. 20.
    Yue, W., Ma, W., Gong, M., Linzhi, S., Jiao, L.: A novel point-matching algorithm based on fast sample consensus for image registration. IEEE Geosci. Remote Sens. Lett. 12(1), 43–47 (2015)CrossRefGoogle Scholar
  21. 21.
    Zagoruyko, S., Komodakis, N.: Learning to compare image patches via convolutional neural networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)Google Scholar
  22. 22.
    Zhang, L., Zhang, L., Du, B.: Deep learning for remote sensing data: a technical tutorial on the state of the art. IEEE Geosc. Remote Sens. Mag. 4(2), 22–40 (2016)CrossRefGoogle Scholar
  23. 23.
    Zitová, B., Flusser, J.: Image registration methods: a survey. Image Vis. Comput. 21(11), 977–1000 (2003)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.The University of ManchesterManchesterUK

Personalised recommendations