Skip to main content
SpringerLink
Log in

Multi-scale Contrastive Learning for Building Change Detection in Remote Sensing Images

  • Conference paper
  • First Online:
Pattern Recognition and Computer Vision (PRCV 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14428))

Included in the following conference series:

  • 374 Accesses

Abstract

Self-supervised contrastive learning (CL) methods can utilize large-scale label-free data to mine discriminative feature representations for vision tasks. However, most existing CL-based approaches focus on image-level tasks, which are insufficient for pixel-level prediction tasks such as change detection (CD). This paper proposes a multi-scale CL pre-training method for CD tasks in remote sensing (RS) images. Firstly, unlike most existing methods that rely on random augmentation to enhance model robustness, we collect a publicly available multi-temporal RS dataset and leverage its temporal variations to enhance the robustness of the CD model. Secondly, an unsupervised RS building extraction method is proposed to separate the representation of buildings from background objects, which aims to balance the samples of building areas and background areas in instance-level CL. In addition, we select an equal number of local regions of the building and background for the pixel-level CL task, which prevents the domination caused by local background class. Thirdly, a position-based matching measurement is proposed to construct local positive sample pairs, which aims to prevent the mismatch issues in RS images due to the object similarity in local areas. Finally, the proposed multi-scale CL method is evaluated on benchmark OSCD and SZTAKI databases, and the results demonstrate the effectiveness of our method.

This work was supported by the Research Foundation of Liaoning Educational Department (Grant No. LJKMZ20220400). Please contact Yao Lu (yaolu@bjirs.org.cn) for access to the pre-training dataset.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 79.99
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

References

  1. Zhang, M., Liu, Z., Feng, J., Liu, L., Jiao, L.: Remote sensing image change detection based on deep multi-scale multi-attention siamese transformer network. Remote Sens. 15(3), 842 (2023)

    Article  Google Scholar 

  2. Bai, T., et al.: Deep learning for change detection in remote sensing: a review. Geo-Spatial Inf. Sci. 26, 262–288 (2022)

    Article  Google Scholar 

  3. Cheng, G., Xie, X., Han, J., Guo, L., Xia, G.S.: Remote sensing image scene classification meets deep learning: challenges, methods, benchmarks, and opportunities. IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens. 13, 3735–3756 (2020)

    Article  Google Scholar 

  4. Jiang, F., Gong, M., Zheng, H., Liu, T., Zhang, M., Liu, J.: Self-supervised global-local contrastive learning for fine-grained change detection in VHR images. IEEE Trans. Geosci. Remote Sens. 61, 1–13 (2023)

    Google Scholar 

  5. Zhu, X.X., et al.: Deep learning in remote sensing: a comprehensive review and list of resources. IEEE Geosci. Remote Sens. Maga. 5(4), 8–36 (2017)

    Article  Google Scholar 

  6. Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607. PMLR (2020)

    Google Scholar 

  7. Bardes, A., Ponce, J., LeCun, Y.: VICReg: variance-invariance-covariance regularization for self-supervised learning. In: International Conference on Learning Representations (2022)

    Google Scholar 

  8. Leenstra, M., Marcos, D., Bovolo, F., Tuia, D.: Self-supervised pre-training enhances change detection in sentinel-2 imagery. In: Del Bimbo, A., et al. (eds.) ICPR 2021. LNCS, vol. 12667, pp. 578–590. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-68787-8_42

    Chapter  Google Scholar 

  9. Manas, O., Lacoste, A., Giró-i Nieto, X., Vazquez, D., Rodriguez, P.: Seasonal contrast: unsupervised pre-training from uncurated remote sensing data. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9414–9423 (2021)

    Google Scholar 

  10. Li, W., Chen, H., Shi, Z.: Semantic segmentation of remote sensing images with self-supervised multitask representation learning. IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens. 14, 6438–6450 (2021)

    Article  Google Scholar 

  11. Li, H., et al.: Global and local contrastive self-supervised learning for semantic segmentation of HR remote sensing images. IEEE Trans. Geosci. Remote Sens. 60, 1–14 (2022)

    Google Scholar 

  12. Gu, X., Li, S., Ren, S., Zheng, H., Fan, C., Xu, H.: Adaptive enhanced swin transformer with u-net for remote sensing image segmentation. Comput. Electr. Eng. 102, 108223 (2022)

    Article  Google Scholar 

  13. Fang, S., Li, K., Shao, J., Li, Z.: SNUNET-CD: a densely connected siamese network for change detection of VHR images. IEEE Geosci. Remote Sens. Lett. 19, 1–5 (2021)

    Google Scholar 

  14. Miyai, A., Yu, Q., Ikami, D., Irie, G., Aizawa, K.: Rethinking rotation in self-supervised contrastive learning: adaptive positive or negative data augmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 2809–2818 (2023)

    Google Scholar 

  15. Wang, H., Yao, M., Jiang, G., Mi, Z., Fu, X.: Graph-collaborated auto-encoder hashing for multiview binary clustering. IEEE Trans. Neural Netw. Learn. Syst. (2023)

    Google Scholar 

  16. Wang, H., Peng, J., Fu, X.: Co-regularized multi-view sparse reconstruction embedding for dimension reduction. Neurocomputing 347, 191–199 (2019)

    Article  Google Scholar 

  17. Feng, L., Meng, X., Wang, H.: Multi-view locality low-rank embedding for dimension reduction. Knowl.-Based Syst. 191, 105172 (2020)

    Article  Google Scholar 

  18. He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9729–9738 (2020)

    Google Scholar 

  19. Chen, X., He, K.: Exploring simple siamese representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15750–15758 (2021)

    Google Scholar 

  20. Pang, B., Zhang, Y., Li, Y., Cai, J., Lu, C.: Unsupervised visual representation learning by synchronous momentum grouping. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13690, pp. 265–282. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-20056-4_16

    Chapter  Google Scholar 

  21. Wang, X., Zhang, R., Shen, C., Kong, T., Li, L.: Dense contrastive learning for self-supervised visual pre-training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3024–3033 (2021)

    Google Scholar 

  22. Chen, H., Zao, Y., Liu, L., Chen, S., Shi, Z.: Semantic decoupled representation learning for remote sensing image change detection. In: IGARSS 2022–2022 IEEE International Geoscience and Remote Sensing Symposium, pp. 1051–1054. IEEE (2022)

    Google Scholar 

  23. Drusch, M., et al.: Sentinel-2: esa’s optical high-resolution mission for GMES operational services. Remote Sens. Environ. 120, 25–36 (2012)

    Article  Google Scholar 

  24. Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., Moore, R.: Google earth engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017)

    Article  Google Scholar 

  25. Hafner, S., Ban, Y., Nascetti, A.: Unsupervised domain adaptation for global urban extraction using sentinel-1 sar and sentinel-2 msi data. Remote Sens. Environ. 280, 113192 (2022)

    Article  Google Scholar 

  26. Daudt, R.C., Le Saux, B., Boulch, A., Gousseau, Y.: Urban change detection for multispectral earth observation using convolutional neural networks. In: IGARSS 2018–2018 IEEE International Geoscience and Remote Sensing Symposium, pp. 2115–2118. IEEE (2018)

    Google Scholar 

  27. Benedek, C., Szirányi, T.: Change detection in optical aerial images by a multilayer conditional mixed markov model. IEEE Trans. Geosci. Remote Sens. 47(10), 3416–3430 (2009)

    Article  Google Scholar 

  28. Meyer, G.E., Neto, J.C.: Verification of color vegetation indices for automated crop imaging applications. Comput. Electron. Agric. 63(2), 282–293 (2008)

    Article  Google Scholar 

  29. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  30. Ailimujiang, G., Jiaermuhamaiti, Y., Jumahong, H., Wang, H., Zhu, S., Nurmamaiti, P.: A transformer-based network for change detection in remote sensing using multiscale difference-enhancement. Comput. Intell. Neurosci. 2022 (2022)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Dalian Minzu University, Dalian, 116650, China

    Mingliang Xue, Xinyuan Huo, Pengyuan Niu, Xuan Liang, Hailong Shang & Shucai Jia

  2. Beijing Institute of Remote Sensing Information, Beijing, China

    Yao Lu

Authors
  1. Mingliang Xue
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xinyuan Huo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yao Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Pengyuan Niu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xuan Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hailong Shang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Shucai Jia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yao Lu .

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Qingshan Liu

  2. Xiamen University, Xiamen, China

    Hanzi Wang

  3. Beijing University of Posts and Telecommunications, Beijing, China

    Zhanyu Ma

  4. Sun Yat-sen University, Guangzhou, China

    Weishi Zheng

  5. Peking University, Beijing, China

    Hongbin Zha

  6. Chinese Academy of Sciences, Beijing, China

    Xilin Chen

  7. Chinese Academy of Sciences, Beijing, China

    Liang Wang

  8. Xiamen University, Xiamen, China

    Rongrong Ji

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Xue, M. et al. (2024). Multi-scale Contrastive Learning for Building Change Detection in Remote Sensing Images. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14428. Springer, Singapore. https://doi.org/10.1007/978-981-99-8462-6_26

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-8462-6_26

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8461-9

  • Online ISBN: 978-981-99-8462-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation