A Semi-supervised Deep Rule-Based Approach for Remote Sensing Scene Classification

  • Xiaowei Gu
  • Plamen P. AngelovEmail author
Conference paper
Part of the Proceedings of the International Neural Networks Society book series (INNS, volume 1)


This paper proposes a new approach that is based on the recently introduced semi-supervised deep rule-based classifier for remote sensing scene classification. The proposed approach employs a pre-trained deep convoluational neural network as the feature descriptor to extract high-level discriminative semantic features from the sub-regions of the remote sensing images. This approach is able to self-organize a set of prototype-based IF...THEN rules from few labeled training images through an efficient supervised initialization process, and continuously self-updates the rule base with the unlabeled images in an unsupervised, autonomous, transparent and human-interpretable manner. Highly accurate classification on the unlabeled images is performed at the end of the learning process. Numerical examples demonstrate that the proposed approach is a strong alternative to the state-of-the-art ones.


Deep rule-based Remote sensing scene classification Semi-supervised learning 


  1. 1.
    Sheng, G., Yang, W., Xu, T., et al.: High-resolution satellite scene classification using a sparse coding based multiple feature combination. Int. J. Remote Sens. 33(8), 2395–2412 (2012)CrossRefGoogle Scholar
  2. 2.
    Cheriyadat, A.M.: Unsupervised feature learning for aerial scene classification. IEEE Trans. Geosci. Remote Sens. 52(1), 439–451 (2014)CrossRefGoogle Scholar
  3. 3.
    Hu, F., Xia, G.-S., Hu, J., et al.: Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery. Remote Sens. 7(11), 14680–14707 (2015)CrossRefGoogle Scholar
  4. 4.
    Chen, S., Tian, Y.: Pyramid of spatial relatons for scene-level land use classification. IEEE Trans. Geosci. Remote Sens. 53(4), 1947–1957 (2015)CrossRefGoogle Scholar
  5. 5.
    Zhang, L., Zhang, L., Kumar, V.: Deep learning for remote sensing data. IEEE Geosci. Remote Sens. Mag. 4(2), 22–40 (2016)CrossRefGoogle Scholar
  6. 6.
    Scott, G.-J., England, M.R., Starms, W.A., et al.: Training deep convolutional neural networks for land-cover classification of high-resolution imagery. IEEE Geosci. Remote Sens. Lett. 14(4), 549–553 (2017)CrossRefGoogle Scholar
  7. 7.
    Xia, G.-S., Hu, J., Hu, F., et al.: AID: a benchmark dataset for performance evaluation of aerial scene classification. IEEE Trans. Geosci. Remote Sens. 55(7), 3965–3981 (2017)CrossRefGoogle Scholar
  8. 8.
    Bian, X., Chen, C., Tian, L., et al.: Fusing local and global features for high-resolution scene classification. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 10(6), 2889–2901 (2017)CrossRefGoogle Scholar
  9. 9.
    Li, Y., Zhang, H., Shen, Q.: Spectral-spatial classification of hyperspectral imagery with 3D convolutional neural network. Remote Sens. 9(1), 67 (2017)CrossRefGoogle Scholar
  10. 10.
    Liu, R., Bian, X., Sheng, Y.: Remote sensing image scene classification via multi-feature fusion. In: Chinese Control and Decision Conference, pp. 3495–3500 (2018)Google Scholar
  11. 11.
    Gu, X., Angelov, P., Zhang, C., et al.: A massively parallel deep rule-based ensemble classifier for remote sensing scenes. IEEE Geosci. Remote Sens. Lett. 32(11), 345–349 (2018)CrossRefGoogle Scholar
  12. 12.
    Zhang, M., Li, W., Du, Q., et al.: Feature extraction for classification of hyperspectral and LiDAR data using patch-to-patch CNN. IEEE Trans. Cybern. (2018).
  13. 13.
    Xiang, S., Nie, F., Zhang, C.: Semi-supervised classification via local spline regression. IEEE Trans. Pattern Anal. Mach. Intell. 15(3), 2039–2053 (2010)CrossRefGoogle Scholar
  14. 14.
    Wang, J., Jebara, T., Chang, S.-F.: Semi-supervised learning using greedy Max-Cut. J. Mach. Learn. Res. 14, 771–800 (2013)MathSciNetzbMATHGoogle Scholar
  15. 15.
    Belkin, M., Niyogi, P., Sindhwani, V.: Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J. Mach. Learn. Res. 7, 2399–2434 (2006)MathSciNetzbMATHGoogle Scholar
  16. 16.
    Liu, W., He, J., Chang, S.-F.: Large graph construction for scalable semi-supervised learning. In: International Conference on Machine Learning, pp. 679–689 (2010)Google Scholar
  17. 17.
    Gómez-Chova, L., Camps-Valls, G., Munoz-Mari, J., et al.: Semisupervised image classification with Laplacian support vector machines. IEEE Geosci. Remote Sens. Lett. 5(3), 336–340 (2008)CrossRefGoogle Scholar
  18. 18.
    Bruzzone, L., Chi, M., Marconcini, M.: A novel transductive SVM for semisupervised classification of remote-sensing images. IEEE Trans. Geosci. Remote Sens. 44(11), 3363–3373 (2006)CrossRefGoogle Scholar
  19. 19.
    Huo, L., Zhao, L., Tang, P.,: Semi-supervised deep rule-based approach for image classification. In: Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), pp. 1–4 (2014)Google Scholar
  20. 20.
    Gu, X., Angelov, P.: Semi-supervised deep rule-based approach for image classification. Appl. Soft Comput. 68, 53–68 (2018)CrossRefGoogle Scholar
  21. 21.
    Angelov, P., Gu, X.: Deep rule-based classifier with human-level performance and characteristics. Inf. Sci. (Ny) 463–464, 196–213 (2018)CrossRefGoogle Scholar
  22. 22.
    Gu, X., Angelov, P.: A deep rule-based approach for satellite scene image analysis. In: IEEE International Conference on Systems, Man and Cybernetics (2018)Google Scholar
  23. 23.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations, pp. 1–14 (2015)Google Scholar
  24. 24.
    Angelov, P., Yager, P.: A new type of simplified fuzzy rule-based system. Int. J. Gen. Syst. 41(2), 163–185 (2011)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Angelov, P., Gu, X.: Empirical Approach to Machine Learning. Springer International Publishing, Cham (2019)CrossRefGoogle Scholar
  26. 26.
    Gan, J., Li, Q., Zhang, Z., et al.: Two-level feature representation for aerial scene classification. IEEE Geosci. Remote Sens. Lett. 13(11), 1626–1639 (2016)CrossRefGoogle Scholar
  27. 27.
    Xia, G., Yang, W., Delon. J., et al.: Structural high-resolution satellite image indexing. In: ISPRS, TC VII Symposium Part A: 100 Years ISPRS–Advancing Remote Sensing Science, pp. 298–303 (2010)Google Scholar
  28. 28.
    Zou, Q., Ni, L., Zhang, T., et al.: Deep learning based feature selection for remote sensing scene classification. IEEE Geosci. Remote Sens. Lett. 12(11), 2321–2325 (2015)CrossRefGoogle Scholar
  29. 29.
    Cristianin, N., Shawe-Taylo, J.: An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press, Cambridge (2000)CrossRefGoogle Scholar
  30. 30.
    Cunningham, P., Delany, S.-J.: K-nearest neighbour classifiers. Mult. Classif. Syst. 34(11), 1–17 (2017)Google Scholar
  31. 31.
    Yang, Y., Newsam, S.: Bag-of-visual-words and spatial extensions for land-use classification. In: International Conference on Advances in Geographic Information Systems, pp. 270–279 (2010)Google Scholar
  32. 32.
    Jégou, H., Douze, M., Schmid, C.: Aggregating local descriptors into a compact representation. In: IEEE Conference on Computer Vision and Pattern Recognition. pp. 3304–3311 (2010)Google Scholar
  33. 33.
    Lazebnik, S., Schmid, C., Ponce, J.,: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2169–2178 (2006)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Computing and CommunicationsLancaster UniversityLancasterUK
  2. 2.Lancaster Intelligent, Robotic and Autonomous Systems Centre (LIRA)Lancaster UniversityLancasterUK
  3. 3.Technical UniversitySofiaBulgaria

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