Skip to main content
SpringerLink
Log in

CSA-CDGAN: channel self-attention-based generative adversarial network for change detection of remote sensing images

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Remote sensing images change detection (RSICD) is a task to identify desired significant differences between multi-temporal images acquired at different times. From the existing methods, most of them solved this issue with a Siamese network, focusing on how to utilize the comparison between two image features to generate an initial difference map. However, Siamese network-based methods have three drawbacks: (1) complex architecture; (2) rough change map; (3) cumbersome detecting procedure: including feature extraction and feature comparison. To overcome the above drawbacks, we devoted our work to design a general framework which has a simple architecture, integrated detecting procedure, and good capacity of detecting subtle changes. In this paper, we proposed a channel self-attention network based on the generative adversarial network for change detection of remote sensing images. The network used an encoder–decoder network to directly produce a change map from two input images. It was better to detect small punctate and slim linear changes than Siamese-based networks. By regarding RSICD as an image translation problem, we used a Generative Adversarial Network to detect changes. In addition, a channel self-attention module was proposed to further improve the performance of this network. Experimental results on three public remote sensing RGB-image datasets, including change detection dataset, Wuhan University building change detection dataset and LEVIR building Change Detection dataset demonstrated that our method outperformed other state-of-the-art methods. In terms of the F1 score, the proposed method achieved maximum improvements of 5.1%, 3.1%, and 1.7% on the above datasets, respectively. Models and codes will be available at https://github.com/wangle53/CSA-CDGAN.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Radke RJ, Andra S, Al-Kofahi O, Roysam B (2005) Image change detection algorithms: a systematic survey. IEEE Trans Image Process 14(3):294–307

    Article  MathSciNet  Google Scholar 

  2. Singh A (1989) Review article digital change detection techniques using remotely-sensed data. Int J Remote Sens 10(6):989–1003

    Article  Google Scholar 

  3. Domínguez EM, Meier E, Small D, Schaepman ME, Bruzzone L, Henke D (2018) A multisquint framework for change detection in highresolution multitemporal SAR images. IEEE Trans Geosci Remote Sens 56(6):3611–3623

    Article  Google Scholar 

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

    Article  Google Scholar 

  5. Lu M, Chen J, Tang H, Rao Y, Yang P, Wu W (2016) Land cover change detection by integrating object-based data blending model of Landsat and MODIS. Remote Sens Environ 184:374–386

    Article  Google Scholar 

  6. Fan Y, Wen Q, Wang W, Wang P, Li L, Zhang P (2017) Quantifying disaster physical damage using remote sensing data—a technical work flow and case study of the 2014 Ludian Earthquake in China. Int J Disaster Risk Sci 8(4):471–488

    Article  Google Scholar 

  7. Mucher CA, Steinnocher KT, Kressler FP, Heunks C (2000) Land cover characterization and change detection for environmental monitoring of pan-Europe. Int J Remote Sens 21(6–7):1159–1181

    Article  Google Scholar 

  8. Liang B, Weng Q (2011) Assessing urban environmental quality change of Indianapolis, United States, by the remote sensing and GIS integration. IEEE J Sel Topics Appl Earth Observ Remote Sens 4(1):43–55

    Article  Google Scholar 

  9. Wuxia Z, Xiaoqiang L (2019) The spectral-spatial joint learning for change detection in multispectral imagery. Remote Sensing 11(3):240

    Article  Google Scholar 

  10. Jie C, Ziyang Y, Jian P et al (2020) DASNet: Dual attentive fully convolutional Siamese networks for change detection of high resolution satellite images. IEEE J Sel Top Appl Earth Observ Remote Sens 14:1194–1206

    Google Scholar 

  11. Zhan Y, Fu K, Yan M, Sun X, Wang H, Qiu X (2017) Change detection based on deep Siamese convolutional network for optical aerial images. IEEE Geosci Remote Sens Lett 14(10):1845–1849

    Article  Google Scholar 

  12. Mengya Z, Guangluan X, Keming C et al (2018) Triplet-based semantic relation learning for aerial remote sensing image change detection. IEEE Geosci Remote Sens Lett 16(2):66–270

    Google Scholar 

  13. Zhenchao Z, Vosselman G, Gerke M et al (2019) Detecting building changes between airborne laser scanning and photogrammetric data. Remote Sens 11(20):2417

    Article  Google Scholar 

  14. Liu Y, Pang C, Zhan Z, Zhang X, Yang X (2021) Building change detection for remote sensing images using a dual-task constrained deep Siamese convolutional network model. IEEE Geosci Remote Sens Lett 18(5):811–815

    Article  Google Scholar 

  15. 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  Google Scholar 

  16. Mou L, Bruzzone L, Xiaoxiang Z (2019) Learning spectral-spatial-temporal features via a recurrent convolutional neural network for change detection in multispectral imagery. IEEE Trans Geosci Remote Sens 57(2):924–935

    Article  Google Scholar 

  17. Shunping J, Yanyun S, Meng L et al (2019) Building instance change detection from large-scale aerial images using convolutional neural networks and simulated samples. Remote Sens 11(11):1343

    Article  Google Scholar 

  18. Bo D, Lixiang R, Chen W et al (2019) Unsupervised deep slow feature analysis for change detection in multi-temporal remote sensing images. IEEE Trans Geosci Remote Sens 57(12):9976–9992

    Article  Google Scholar 

  19. Saha S, Bovolo F, Bruzzone L (2019) Unsupervised deep change vector analysis for multiple-change detection in VHR images. IEEE Trans Geosci Remote Sens 57(6):3677–3693

    Article  Google Scholar 

  20. Arabi MEA, Karoui MS, Djerriri K (2018) Optical remote sensing change detection through deep Siamese network. In: 2018 IEEE international geoscience and remote sensing symposium, pp 5041–5044

  21. Daifeng P, Guan H (2019) Unsupervised change detection method based on saliency analysis and convolutional neural network. J Appl Remote Sens 13(2):024512

    Google Scholar 

  22. Huiwei J, Xiangyun H, Kun L (2020) PGA-SiamNet: pyramid feature-based attention-guided siamese network for remote sensing orthoimagery building change detection. Remote Sens 12(3):484

    Article  Google Scholar 

  23. Hedjam R, Abdesselam A, Melgani F (2019) Change detection from unlabeled remote sensing images using Siamese ANN. In: The IGARSS 2019—2019 IEEE international geoscience and remote sensing symposium, pp 1530–1533

  24. Larabi MEA, Chaib S, Bakhti K et al (2019) High-resolution optical remote sensing imagery change detection through deep transfer learning. J Appl Remote Sens 13(4):046512

    Article  Google Scholar 

  25. Hou B, Wang Y, Liu Q (2017) Change detection based on deep features and low rank. IEEE Geosci Remote Sens Lett 14:2418–2422

    Article  Google Scholar 

  26. 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), pp 4063–4067

  27. Sakurada K, Okatani T (2015) Change detection from a street image pair using CNN features and superpixel segmentation. In: The British machine vision conference (BMVC), pp 61.1–61.12

  28. Khan S, He X, Porikli F, Bennamoun M (2017) Forest change detection in incomplete satellite images with deep neural networks. IEEE Trans Geosci Remote Sens 55:5407–5423

    Article  Google Scholar 

  29. Lindquist E, D’Annunzio R (2016) Assessing global forest land-use change by object-based image analysis. Remote Sens 8:678

    Article  Google Scholar 

  30. Qi W, Zhenghang Y, Qian D et al (2019) GETNET: a general end-to-end 2-D CNN framework for hyperspectral image change detection. Trans Geosci Remote Sens 57(1):3–13

    Article  Google Scholar 

  31. Min Z, Wenzhong S (2020) A feature difference convolutional neural network-based change detection method. Trans Geosci Remote Sens 58(10):7232–7246

    Article  Google Scholar 

  32. Lebedev M, Vizilter YV, Vygolov O et al (2018) Change detection in remote sensing images using conditional adversarial networks. Int Arch Photogramm Remote Sens Spat Inf Sci 42(2):565–571

    Article  Google Scholar 

  33. Jia L, Maoguo G, Kai Q et al (2018) A deep convolutional coupling network for change detection based on heterogeneous optical and radar images. IEEE Trans Neural Netw Learn Syst 29(3):545–559

    Article  MathSciNet  Google Scholar 

  34. Tewkesbury AP, Comber AJ, Tate NJ et al (2015) A critical synthesis of remotely sensed optical image change detection techniques. Remote Sens Environ 160:1–14

    Article  Google Scholar 

  35. Bin H, Qingjie L, Heng W (2020) From W-Net to CDGAN: bitemporal change detection via deep learning techniques. IEEE Trans Geosci Remote Sens 58(3):1790–1802

    Article  Google Scholar 

  36. Maoguo G, Xudong N, Puzhao Z (2017) Generative adversarial networks for change detection in multispectral imagery. IEEE Geosci Remote Sens Lett 14(2):2310–2314

    Google Scholar 

  37. Maoguo G, Yuelei Y, Tao Z (2019) A generative discriminatory classified network for change detection in multispectral imagery. IEEE J Sel Top Appl

  38. Niu X, Gong M, Zhan T, Yang Y (2018) A conditional adversarial network for change detection in heterogeneous images. IEEE J Sel Top Appl Earth Observ Remote Sens 16(1):45–49

    Google Scholar 

  39. Goodfellow I, Pouget-Abadie J, Mirza M et al (2014) Generative adversarial nets. In: Advances in neural information processing systems, pp 2672–2680

  40. Babu KK, Dubey SR (2020) CDGAN: cyclic discriminative generative adversarial networks for image-to-image transformation. arXiv: 200105489

  41. Hu J, Shen L, Albanie S et al (2020) Squeeze-and-excitation networks. IEEE Trans Pattern Anal Mach Intell 42(8):2011–2023

    Article  Google Scholar 

  42. Wang Q, Wu B, Zhu P (2020) ECA-Net: efficient channel attention for deep convolutional neural networks. In: 2020 IEEE/CVF conference on computer and pattern recognition (CVPR), IEEE

  43. Guo E et al (2020) Learning to measure change: fully convolutional siamese metric networks for scene change detection. arXiv:181009111

  44. Xueli P, Ruofei Z, Zhen L et al (2021) Optical remote sensing image change detection based on attention mechanism and image difference. IEEE Trans Geosci Remote Sens 59(9):7296–7307

    Article  Google Scholar 

  45. Yi Z, Min D, Fen H et al (2021) FODA: building change detection in high-resolution remote sensing images based on feature-output space dual-alignment. IEEE J Sel Top Appl Earth Observ Remote Sens 14:8125–8134

    Article  Google Scholar 

  46. Zhixue W, Chaoyong P, Yu Z et al (2021) Fully convolutional Siamese networks based change detection for optical aerial images with focal contrastive loss. Neurocomputing 457:155–167

    Article  Google Scholar 

  47. Decheng W, Xiangning C, Mingyong J et al (2021) ADS-Net: an attention-based deeply supervised network for remote sensing image change detection. Int J Appl Earth Observ Geoinform 101:102348

    Article  Google Scholar 

  48. Yi Z, Shizhou Z, Ying L et al (2020) Coarse-to-fine satellite images change detection framework via boundary-aware attentive network. Sensors 20:6735

    Article  Google Scholar 

  49. Daifeng P, Yongjun Z, Haiyan G (2019) End-to-end change detection for high resolution satellite images using improved UNet++. Remote Sens 11:1382

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (Grant Number 61771409), the Science and Technology Program of Sichuan (Grant Number 2021YJ0080).

Author information

Authors and Affiliations

  1. School of Physical Science and Technology, Southswest Jiaotong University, Chengdu, China

    Zhixue Wang, Yu Zhang, Lin Luo & Nan Wang

Authors
  1. Zhixue Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lin Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yu Zhang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

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 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

Wang, Z., Zhang, Y., Luo, L. et al. CSA-CDGAN: channel self-attention-based generative adversarial network for change detection of remote sensing images. Neural Comput & Applic 34, 21999–22013 (2022). https://doi.org/10.1007/s00521-022-07637-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-022-07637-z

Keywords

Search

Navigation