Skip to main content

Advertisement

segWCD: A new segmentation-based weak supervision neural network for building change detection

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Manual annotation of changes in high-resolution remote sensing images is labor-intensive and limits advancements in change detection. We introduce the Segmentation-based Weakly Supervised Change Detection (segWCD) framework to mitigate this challenge. Our method leverages a semantic segmentation model to generate pseudo-labels, offering weak supervision for detecting changes. The Creator module further refines these labels, enhancing the model’s detection accuracy. Additionally, we address the issue of label noise by variably weighting the pseudo-labels based on their confidence, thus optimizing the training process. Experimental results show that segWCD achieves a Recall of 0.921, an F1 score of 0.627, and an MIOU of 0.708, performing comparably to fully supervised methods. This approach marks a significant step forward in weakly supervised learning, demonstrating the potential of refined pseudo-labeling techniques.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Algorithm 1
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Availability of data and materials

The data that support the findings of this study are openly available.

Code availability

Code availability not applicable.

References

  1. Amani M, Ghorbanian A, Ahmadi SA et al (2020) Google earth engine cloud computing platform for remote sensing big data applications: A comprehensive review. IEEE J Sel Top Appl Earth Observ Remote Sens 13:5326–5350

    Article  MATH  Google Scholar 

  2. Osco LP, Junior JM, Ramos APM et al (2021) A review on deep learning in uav remote sensing. Int J Appl Earth Observ Geoinform 102:102456

    Article  Google Scholar 

  3. Li Y, Ma J, Zhang Y (2021) Image retrieval from remote sensing big data: A survey. Inf Fusion 67:94–115

    Article  MATH  Google Scholar 

  4. Wu W, Liu H, Li L et al (2021) Application of local fully convolutional neural network combined with yolo v5 algorithm in small target detection of remote sensing image. PLoS One 16(10):e0259283

    Article  Google Scholar 

  5. Daudt RC, Le Saux B, Boulch A (2018) Fully convolutional siamese networks for change detection. In: 2018 25th IEEE international conference on image processing (ICIP). IEEE, pp 4063–4067

  6. Bao T, Fu C, Fang T et al (2020) Ppcnet: A combined patch-level and pixel-level end-to-end deep network for high-resolution remote sensing image change detection. IEEE Geosci Remote Sens Lett 17(10):1797–1801

    Article  MATH  Google Scholar 

  7. Hou B, Liu Q, Wang H et al (2020) From w-net to cdgan: Bitemporal change detection via deep learning techniques. IEEE Trans Geosci Remote Sens 58(3):1790–1802

    Article  MATH  Google Scholar 

  8. Lv Z, Liu T, Benediktsson JA et al (2021) Land cover change detection techniques: Very-high-resolution optical images: A review. IEEE Geosci Remote Sens Mag 10(1):44–63

    Article  MATH  Google Scholar 

  9. Zhu M, Wan S, Jin P et al (2022) Dffnet: Dynamic feature fusion network for weakly supervised object detection in remote sensing images. In: 2022 IEEE International Conference on Big Data (Big Data). IEEE, pp 1409–1414

  10. Zheng X, Chen X, Lu X et al (2021) Unsupervised change detection by cross-resolution difference learning. IEEE Trans Geosci Remote Sens 60:1–16

    MATH  Google Scholar 

  11. Sohn K, Berthelot D, Carlini N et al (2020) Fixmatch: Simplifying semi-supervised learning with consistency and confidence. Adv Neural Inf Process Syst 33:596–608

    Google Scholar 

  12. Berthelot D, Carlini N, Goodfellow I et al (2019) Mixmatch: A holistic approach to semi-supervised learning. Adv Neural Inf Process Syst 32

  13. Zhang M, Shi W (2020) A feature difference convolutional neural network-based change detection method. IEEE Trans Geosci Remote Sens 58(10):7232–7246

    Article  MATH  Google Scholar 

  14. Lavallée P, Beaumont JF (2015) Why we should put some weight on weights. Survey methods: Insights from the field (SMIF)

  15. Zhang Q, Zuo S, Liang C et al (2022) Platon: Pruning large transformer models with upper confidence bound of weight importance. In: International conference on machine learning. PMLR, pp 26809–26823

  16. Hu Z, Yang Z, Hu X et al (2021) Simple: Similar pseudo label exploitation for semi-supervised classification. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 15099–15108

  17. Peng D, Bruzzone L, Zhang Y et al (2020) Semicdnet: A semisupervised convolutional neural network for change detection in high resolution remote-sensing images. IEEE Trans Geosci Remote Sens 59(7):5891–5906

    Article  MATH  Google Scholar 

  18. Alvarez JLH, Ravanbakhsh M, Demir B (2020) S2-cgan: Self-supervised adversarial representation learning for binary change detection in multispectral images. In: IGARSS 2020-2020 IEEE international geoscience and remote sensing symposium. IEEE, pp 2515–2518

  19. Heidler K, Mou L, Hu D et al (2023) Self-supervised audiovisual representation learning for remote sensing data. Int J Appl Earth Observ Geoinf 116:103130

    Google Scholar 

  20. Zheng Z, Ma A, Zhang L et al (2021) Change is everywhere: Single-temporal supervised object change detection in remote sensing imagery. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 15193–15202

  21. Hafner S, Ban Y, Nascetti A (2022) Urban change detection using a dual-task siamese network and semi-supervised learning. In: IGARSS 2022-2022 IEEE international geoscience and remote sensing symposium, IEEE, pp 1071–1074

  22. Chen LC, Zhu Y, Papandreou G et al (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European conference on computer vision (ECCV), pp 801–818

  23. Zhou Z, Rahman Siddiquee MM, Tajbakhsh N et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep Learning in medical image analysis and multimodal learning for clinical decision support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings 4, Springer, pp 3–11

  24. Qin X, Zhang Z, Huang C et al (2020) U2-net: Going deeper with nested u-structure for salient object detection. Pattern Recognit 106:107404

    Article  MATH  Google Scholar 

  25. Chen J, Lu Y, Yu Q et al (2021) Transunet: Transformers make strong encoders for medical image segmentation. arXiv:2102.04306

  26. Xu J, Xiong Z, Bhattacharyya SP (2023) Pidnet: A real-time semantic segmentation network inspired by pid controllers. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 19529–19539

  27. Cordts M, Omran M, Ramos S et al (2016) The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3213–3223

  28. Brostow GJ, Fauqueur J, Cipolla R (2009) Semantic object classes in video: A high-definition ground truth database. Pattern Recognit Lett 30(2):88–97

    Article  Google Scholar 

  29. Mottaghi R, Chen X, Liu X et al (2014) The role of context for object detection and semantic segmentation in the wild. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 891–898

  30. Chen H, Shi Z (2020) A spatial-temporal attention-based method and a new dataset for remote sensing image change detection. Remote Sens 12(10):1662

    Article  MATH  Google Scholar 

  31. Ji S, Wei S, Lu M (2018) Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery data set. IEEE Trans Geosci Remote Sens 57(1):574–586

    Article  MATH  Google Scholar 

  32. Bylinskii Z, Judd T, Oliva A et al (2018) What do different evaluation metrics tell us about saliency models? IEEE Trans Pattern Anal Mach Intell 41(3):740–757

    Article  Google Scholar 

  33. Fielding AH, Bell JF (1997) A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ Conserv 24(1):38–49

  34. Sakurada K, Shibuya M, Wang W (2020) Weakly supervised silhouette-based semantic scene change detection. In: 2020 IEEE International conference on robotics and automation (ICRA). IEEE, pp 6861–6867

  35. Wu C, Du B, Zhang L (2023) Fully convolutional change detection framework with generative adversarial network for unsupervised, weakly supervised and regional supervised change detection. IEEE Trans Pattern Anal Mach Intell

  36. Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18, Springer, pp 234–241

  37. Andermatt P, Timofte R (2020) A weakly supervised convolutional network for change segmentation and classification. In: Proceedings of the Asian conference on computer vision

  38. Cao Y, Huang X, Weng Q (2023) A multi-scale weakly supervised learning method with adaptive online noise correction for high-resolution change detection of built-up areas. Remote Sens Environ 297:113779

    Article  MATH  Google Scholar 

  39. Huang R, Wang R, Guo Q et al (2023) Background-mixed augmentation for weakly supervised change detection. AAAI

  40. Luppino LT, Kampffmeyer M, Bianchi FM et al (2022) Deep image translation with an affinity-based change prior for unsupervised multimodal change detection. IEEE Trans Geosci Remote Sens 60:1–22

    Article  Google Scholar 

  41. Tang X, Zhang H, Mou L et al (2022) An unsupervised remote sensing change detection method based on multiscale graph convolutional network and metric learning. IEEE Trans Geosci Remote Sens 60:1–15

    MATH  Google Scholar 

Download references

Funding

This research was supported by the National Natural Science Foundation of China (Nos. 61976247 and 62102330), and the Key Research and Development Program in Sichuan Province of China (Nos. 2023YFS0404 and 2024YFFK0410).

Author information

Authors and Affiliations

Authors

Contributions

Yunyang Wu: Writing - original draft, Methodology. Xiaobo Zhang: Writing - review & editing, Investigation. Xiaole Zhao: Supervision, Visualization, Writing - review & editing. Yimin Sun: Writing - review & editing. Tianrui Li: Writing - review & editing.

Corresponding author

Correspondence to Xiaobo Zhang.

Ethics declarations

Conflict of interest

The authors have no competing interests to declare that are relevant to the content of this article.

Ethics approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, Y., Zhang, X., Zhao, X. et al. segWCD: A new segmentation-based weak supervision neural network for building change detection. Appl Intell 55, 147 (2025). https://doi.org/10.1007/s10489-024-06003-x

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10489-024-06003-x

Keywords