Advertisement

Unsupervised Learning of Robust Representations for Change Detection on Sentinel-2 Earth Observation Images

  • Michelle AubrunEmail author
  • Andres Troya-Galvis
  • Mohanad Albughdadi
  • Romain Hugues
  • Marc Spigai
Conference paper
  • 96 Downloads
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 554)

Abstract

The recent popularity of artificial intelligence techniques and the wealth of free and open access Copernicus data have led to the development of new data analytics applications in the Earth Observation domain. Among them, is the detection of changes on image time series, and in particular, the estimation of levels and superficies of changes. In this paper, we propose an unsupervised framework to detect generic but relevant and reliable changes using pairs of Sentinel-2 images. To illustrate this method, we will present a scenario focusing on the detection of changes in vineyards due to natural hazards such as frost and hail.

Keywords

Change detection Unsupervised method Artificial intelligence Sentinel-2 data Vineyard use case 

Notes

Acknowledgements

The work presented in this paper was supported by the H2020 CANDELA project under grant agreement No. 776193.

References

  1. 1.
  2. 2.
    Singh, A.: Review article digital change detection techniques using remotely-sensed data. Int. J. Remote Sens. 10(6), 989–1003 (1989)CrossRefGoogle Scholar
  3. 3.
    Laws, K.: Textured image segmentation. Ph.D. Dissertation, University of Southern California (1980)Google Scholar
  4. 4.
    Serra, P., Pons, X., Saurí, D.: Post-classification change detection with data from different sensors: Some accuracy considerations. Int. J. Remote Sens. 24(16), 3311–3340 (2003)CrossRefGoogle Scholar
  5. 5.
    Alboody, A., Sedes, F., Inglada, J.: Post-classification and spatial reasoning: new approach to change detection for updating GIS database. In: 3rd International Conference on Information and Communication Technologies, pp. 1–7. From Theory to Applications, Damascus (2008)Google Scholar
  6. 6.
    Gong, M., Niu, X., Zhang, P., Li, Z.: Generative adversarial networks for change detection in multispectral imagery. IEEE Geosci. Remote Sens. Lett. 14(12), 2310–2314 (2017)CrossRefGoogle Scholar
  7. 7.
    de Jong, K.L., Bosman, A.S.: Unsupervised change detection in satellite images using convolutional neural networks. In: International Joint Conference on Neural Networks, Budapest (2019)Google Scholar
  8. 8.
    LeCun, Y.: Modèles connexionistes de l’apprentissage. Ph.D. thesis, Université de Paris VI (1987)Google Scholar
  9. 9.
    Planinsic, P., Gleich, D.: Temporal change detection in sar images using log cumulants and stacked autoencoder. IEEE Geosci. Remote Sens. Lett. 15(2), 1–5 (2018)CrossRefGoogle Scholar
  10. 10.
    Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral-spatial feature learning via deep residual Conv-Deconv network for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 56(1), 391–406 (2018)CrossRefGoogle Scholar
  11. 11.

Copyright information

© IFIP International Federation for Information Processing 2020

Authors and Affiliations

  • Michelle Aubrun
    • 1
    Email author
  • Andres Troya-Galvis
    • 1
  • Mohanad Albughdadi
    • 2
  • Romain Hugues
    • 1
  • Marc Spigai
    • 1
  1. 1.Thales Alenia SpaceToulouseFrance
  2. 2.TerraNISRamonville-Saint-AgneFrance

Personalised recommendations