Skip to main content

Siamese Networks with Transfer Learning for Change Detection in Sentinel-2 Images

  • Conference paper
  • First Online:
AIxIA 2021 – Advances in Artificial Intelligence (AIxIA 2021)

Abstract

The Earth’s surface is constantly changing due to various anthropogenic and natural causes. Leveraging machine learning to monitor land cover changes over time may provide valuable information on the transformation of the Earth’s environment. This study focuses on the discovery of land cover changes in bi-temporal, Sentinel-2 images. In particular, we rely on a Siamese network trained with labelled, imagery data of the same Earth’s scene acquired with Sentinel-2 at different times. Subsequently, we adopt a transfer learning strategy to adapt the Siamese network to Sentinel-2 data acquired in any new unlabeled scene. To deal with the lack of change labels in the new scene, transfer learning is performed with change pseudo-labels estimated in the new scene in unsupervised manner. We assess the effectiveness of the proposed change detection method in two couples of images acquired with Sentinel-2, at different times, in the urban areas of Cupertino and Las Vegas.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    In Sentinel-2, the optical camera covers 13 bands.

  2. 2.

    https://scikit-image.org/docs/dev/api/skimage.filters.html#skimage.filters.threshold_otsu.

  3. 3.

    https://rcdaudt.github.io/oscd/.

References

  1. Andresini, G., Appice, A., Iaia, D., Malerba, D., Taggio, N., Aiello, A.: Leveraging autoencoders in change vector analysis of optical satellite images. J. Intell. Inf. Sys. 58, 1–20 (2021). https://doi.org/10.1007/s10844-021-00670-9

    Article  Google Scholar 

  2. Appice, A., Ciampi, A., Malerba, D.: Summarizing numeric spatial data streams by trend cluster discovery. Data Min. Knowl. Discov. 29(1), 84–136 (2013). https://doi.org/10.1007/s10618-013-0337-7

    Article  MathSciNet  MATH  Google Scholar 

  3. Appice, A., Di Mauro, N., Lomuscio, F., Malerba, D.: Empowering change vector analysis with autoencoding in bi-temporal hyperspectral images. In: MACLEANECMLPKDD Workshop, vol. 2466, pp. 1–10. CEUR Workshop Proceedings (2019)

    Google Scholar 

  4. Appice, A., Guccione, P., Acciaro, E., Malerba, D.: Detecting salient regions in a bi-temporal hyperspectral scene by iterating clustering and classification. Appl. Intell. 50(10), 3179–3200 (2020). https://doi.org/10.1007/s10489-020-01701-8

    Article  Google Scholar 

  5. Bergstra, J., Yamins, D., Cox, D.D.: Making a science of model search: hyperparameter optimization in hundreds of dimensions for vision architectures. In: ICML, pp. 115–123 (2013)

    Google Scholar 

  6. Bromley, J., Guyon, I., Lecun, Y., Säckinger, E., Shah, R.: Signature verification using a siamese time delay neural network. Int. J. Pattern Recogn. Artif. Intell. - IJPRAI 7(04), 669–688 (1993)

    Article  Google Scholar 

  7. Bruzzone, L., Prieto, D.F.: Automatic analysis of the difference image for unsupervised change detection. IEEE Trans. Geosci. Remote Sens. 38(2), 1171–1182 (2000)

    Article  Google Scholar 

  8. Caye Daudt, R., Le Saux, B., Boulch, A., Gousseau, Y.: Urban change detection for multispectral earth observation using convolutional neural networks. In: IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (2018)

    Google Scholar 

  9. Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), vol. 2, pp. 1735–1742 (2006)

    Google Scholar 

  10. Hussain, M., Chen, D., Cheng, A., Wei, H., Stanley, D.: Change detection from remotely sensed images: from pixel-based to object-based approaches. ISPRS J. Photogrammetry Remote Sens. 80, 91–106 (2013)

    Article  Google Scholar 

  11. Kwan, C.: Methods and challenges using multispectral and hyperspectral images for practical change detection applications. Information 10(11), 353 (2019)

    Article  Google Scholar 

  12. Larabi, M., Souleyman, C., Bakhti, K., Kamel, H., Amine, B.: High-resolution optical remote sensing imagery change detection through deep transfer learning. J. Appl. Remote Sens. 13(11), 046512 (2019)

    Google Scholar 

  13. Lopez-Fandino, J., Garea, A.S., Heras, D.B., Argüello, F.: Stacked autoencoders for multiclass change detection in hyperspectral images. In: 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018, pp. 1906–1909. IEEE (2018)

    Google Scholar 

  14. Lu, J., Hu, J., Zhou, J.: Deep metric learning for visual understanding: an overview of recent advances. IEEE Signal Process. Mag. 34(6), 76–84 (2017)

    Article  Google Scholar 

  15. López-Fandiño, J., B. Heras, D., Argüello, F., Dalla Mura, M.: GPU framework for change detection in multitemporal hyperspectral images. Int. J. Parallel Program. 47(2), 272–292 (2017). https://doi.org/10.1007/s10766-017-0547-5

    Article  Google Scholar 

  16. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Geosci. Remote Sens. 9(1), 62–66 (1972)

    Google Scholar 

  17. Planinšič, P., Gleich, D.: Temporal change detection in SAR images using log cumulants and stacked autoencoder. IEEE Geosci. Remote Sens. Lett. 15(2), 297–301 (2018)

    Article  Google Scholar 

  18. Sefrin, O., Riese, F.M., Keller, S.: Deep learning for land cover change detection. Remote Sens. 13(1), 78 (2021)

    Article  Google Scholar 

  19. Seydi, S.T., Hasanlou, M.: A new land-cover match-based change detection for hyperspectral imagery. Eur. J. Remote Sens. 50(1), 517–533 (2017)

    Article  Google Scholar 

  20. Shi, W., Zhang, M., Zhang, R., Chen, S., Zhan, Z.: Change detection based on artificial intelligence: state-of-the-art and challenges. Remote Sens. 12(10), 1688 (2020)

    Article  Google Scholar 

  21. Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., Liu, C.: A survey on deep transfer learning. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds.) ICANN 2018. LNCS, vol. 11141, pp. 270–279. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01424-7_27

    Chapter  Google Scholar 

  22. Wang, M., Tan, K., Jia, X., Wang, X., Chen, Y.: A deep siamese network with hybrid convolutional feature extraction module for change detection based on multi-sensor remote sensing images. Remote Sens. 12(01), 205 (2020)

    Article  Google Scholar 

  23. Wu, C., Du, B., Cui, X., Zhang, L.: A post-classification change detection method based on iterative slow feature analysis and Bayesian soft fusion. Remote Sens. Environ. 199, 241–255 (2017)

    Article  Google Scholar 

  24. Wu, K., Du, Q., Wang, Y., Yang, Y.: Supervised sub-pixel mapping for change detection from remotely sensed images with different resolutions. Remote Sens. 9(3), 284 (2017)

    Article  Google Scholar 

  25. Yang, Z., Mueller, R.: Spatial-spectral cross-correlation for change detection: a case study for citrus coverage change detection. In: ASPRS 2007 Annual Conference, vol. 2, no. 01, pp. 767–777 (2007)

    Google Scholar 

  26. Yuan, F., Sawaya, K.E., Loeffelholz, B.C., Bauer, M.E.: Land cover classification and change analysis of the twin cities (Minnesota) metropolitan area by multitemporal landsat remote sensing. Remote Sens. Environ. 98(2), 317–328 (2005)

    Article  Google Scholar 

Download references

Acknowledgements

This work fulfills the research objectives of the PON “Ricerca e Innovazione” 2014–2020 project “CLOSE – Close to the Earth” (ARS01_00141), funded by the Italian Ministry for Universities and Research (MIUR).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Giuseppina Andresini .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Andresini, G., Appice, A., Dell’Olio, D., Malerba, D. (2022). Siamese Networks with Transfer Learning for Change Detection in Sentinel-2 Images. In: Bandini, S., Gasparini, F., Mascardi, V., Palmonari, M., Vizzari, G. (eds) AIxIA 2021 – Advances in Artificial Intelligence. AIxIA 2021. Lecture Notes in Computer Science(), vol 13196. Springer, Cham. https://doi.org/10.1007/978-3-031-08421-8_33

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-08421-8_33

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-08420-1

  • Online ISBN: 978-3-031-08421-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics