Multi-scale Convolutional Neural Network for Remote Sensing Image Change Detection

  • Xiao Yu
  • Junfu FanEmail author
  • Peng Zhang
  • Liusheng Han
  • Dafu Zhang
  • Guangwei Sun
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1228)


Intelligence method to detect changes in remote sensing images is a difficult but important issue and it is of great significance for natural resources, environmental and social-economy monitoring. In this paper, we presented a novel deep learning model named PSPNet-CONC which combined multi-scale feature deep learning model PSPNet and features extraction module ResNet34 in multi-period remote sensing images. Experiments were designed and conducted systematically for the comparison between the deep learning methods and traditional methods. Our experimental accuracy results show that our model got at least 11% higher in recall index than other state-of-the-art methods. Further more PA also increase by 4.5%, and unchanged accuracy is 1% better than other excellence methods. It demonstrates that with the characteristic of deep learning with multi-scale information, the PSPNet-CONC model could generate higher accuracy and stability detection results than other methods.


Multi-scale Change detection PSPNet-CONC Deep neural networks 


  1. 1.
    Zhang, P., Gong, M., Su, L., Liu, J., Li, Z.: Change detection based on deep feature representation and mapping transformation for multi-spatial-resolution remote sensing images. ISPRS J. Photogram. 116, 24–41 (2016)CrossRefGoogle Scholar
  2. 2.
    Stramondo, S., Bignami, C., Chini, M., Pierdicca, N., Tertulliani, A.: Satellite radar and optical remote sensing for earthquake damage detection: results from different case studies. Int. J. Remote Sens. 27(20), 4433–4447 (2006)CrossRefGoogle Scholar
  3. 3.
    Celik, T.: Unsupervised change detection in satellite images using principal component analysis and k-means clustering. IEEE Geosci. Remote Sens. Lett. 6(4), 772–776 (2009)CrossRefGoogle Scholar
  4. 4.
    Daudt, R.C., Le Saux, B, Boulch, A.: Fully convolutional siamese networks for change detection. In: 2018 25th IEEE International Conference on Image Processing (ICIP), pp. 4063–4067. IEEE (2018)Google Scholar
  5. 5.
    Daudt, R.C., Le Saux, B, Boulch, A., Gousseau, Y.: Urban change detection for multispectral earth observation using convolutional neural networks. In: 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018, pp. 2115–2118. IEEE (2018)Google Scholar
  6. 6.
    Deng, J., Wang, K., Deng, Y., Qi, G.: PCA-based land-use change detection and analysis using multitemporal and multisensor satellite data. Int. J. Remote Sens. 29(16), 4823–4838 (2008)CrossRefGoogle Scholar
  7. 7.
    Desclée, B., Bogaert, P., Defourny, P.: Forest change detection by statistical object-based method. Remote Sens. Environ. 102(1–2), 1–11 (2006)CrossRefGoogle Scholar
  8. 8.
    Huo, C., Zhou, Z., Lu, H., Pan, C., Chen, K.: Fast object-level change detection for VHR images. IEEE Geosci. 7(1), 118–122 (2009)Google Scholar
  9. 9.
    Wu, C., Du, B., Zhang, L.: Slow feature analysis for change detection in multispectral imagery. IEEE Trans. Geosci. 52(5), 2858–2874 (2013)CrossRefGoogle Scholar
  10. 10.
    Wu, C., Du, B., Cui, X., Zhang, L.: A post-classification change detection method based on iterative slow feature analysis and Bayesian soft fusion. IEEE Trans. Geosci. 199, 241–255 (2017)Google Scholar
  11. 11.
    Xian, G., Homer, C., Fry, J.: Updating the 2001 National Land Cover Database land cover classification to 2006 by using Landsat imagery change detection methods. Remote Sens. Environ. 113(6), 1133–1147 (2009)CrossRefGoogle Scholar
  12. 12.
    Zhao, M., Zhao, Y.: Object-oriented and multi-feature hierarchical change detection based on CVA for high-resolution remote sensing imagery. Remote Sens. 22(1), 119–131 (2018)Google Scholar
  13. 13.
    Liu, S., Bruzzone, L., Bovolo, F., Zanetti, M., Du, P.: Sequential spectral change vector analysis for iteratively discovering and detecting multiple changes in hyperspectral images. IEEE Trans. Geosci. Remote Sens. 53(8), 4363–4378 (2015)CrossRefGoogle Scholar
  14. 14.
    Yu, W., Zhou, W., Qian, Y., Yan, J.: A new approach for land cover classification and change analysis: integrating backdating and an object-based method. Remote Sens. Environ. 177, 37–47 (2016)CrossRefGoogle Scholar
  15. 15.
    Xian, G., Homer, C.: Updating the 2001 National Land Cover Database impervious surface products to 2006 using Landsat imagery change detection methods. Remote Sens. Environ. 114(8), 1676–1686 (2010)CrossRefGoogle Scholar
  16. 16.
    Falco, N., Marpu, P.R., Benediktsson, J.A.: A toolbox for unsupervised change detection analysis. Int. J. Remote Sens. 37(7), 1505–1526 (2016)CrossRefGoogle Scholar
  17. 17.
    Nielsen, A.A., Conradsen, K., Simpson, J.J.: Multivariate alteration detection (MAD) and MAF postprocessing in multispectral, bitemporal image data: new approaches to change detection studies. Remote Sens. Environ. 64(1), 1–19 (1998)CrossRefGoogle Scholar
  18. 18.
    Bovolo, F., Bruzzone, L.: A theoretical framework for unsupervised change detection based on change vector analysis in the polar domain. IEEE Trans. Geosci. 45(1), 218–236 (2006)CrossRefGoogle Scholar
  19. 19.
    Qi, Z., Yeh, A.G.-O., Li, X., Zhang, X.: A three-component method for timely detection of land cover changes using polarimetric SAR images. ISPRS J. Photogram. Remote Sens. 107, 3–21 (2015)CrossRefGoogle Scholar
  20. 20.
    Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). Scholar
  21. 21.
    Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2881–2890 (2017)Google Scholar
  22. 22.
    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
  23. 23.
    Hastie, T., Tibshirani, R., Friedman, J., Franklin, J.: The elements of statistical learning: data mining, inference and prediction. Math. Intell. 27(2), 83–85 (2005)Google Scholar
  24. 24.
    Wiemker, R., Speck, A., Kulbach, D., Spitzer, H., Bienlein, J.: Unsupervised robust change detection on multispectral imagery using spectral and spatial features. In: Proceedings of the Third International Airborne Remote Sensing Conference and Exhibition, pp. 640–647 (1997)Google Scholar
  25. 25.
    Nielsen, A.A.: The regularized iteratively reweighted MAD method for change detection in multi-and hyperspectral data. IEEE Trans. Image Process. 16(2), 463–478 (2007)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Xiao Yu
    • 1
  • Junfu Fan
    • 1
    Email author
  • Peng Zhang
    • 1
  • Liusheng Han
    • 1
  • Dafu Zhang
    • 1
  • Guangwei Sun
    • 1
  1. 1.School of Civil and Architectural EngineeringShandong University of TechnologyZiboChina

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