Skip to main content

Advertisement

SpringerLink
Log in

Land-use scene classification using multi-scale completed local binary patterns

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

In this paper, we introduce the completed local binary patterns (CLBP) operator for the first time on remote sensing land-use scene classification. To further improve the representation power of CLBP, we propose a multi-scale CLBP (MS-CLBP) descriptor to characterize the dominant texture features in multiple resolutions. Two different kinds of implementations of MS-CLBP equipped with the kernel-based extreme learning machine are investigated and compared in terms of classification accuracy and computational complexity. The proposed approach is extensively tested on the 21-class land-use dataset and the 19-class satellite scene dataset showing a consistent increase on performance when compared to the state of the arts.

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

Similar content being viewed by others

References

  1. Csurka, G., Dance, C.R., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: Proceedings of ECCV Workshop on Statistical Learning in Computer Vision, Prague, pp. 1–2 (2004)

  2. Yang, Y., Newsam, S.: Spatial pyramid co-occurrence for image classification. In: Proceedings of International Conference on Computer Vision, Barcelona, Spain, pp. 1465–1472 (2011)

  3. Yang, Y., Newsam, S.: Bag-of-visual-words and spatial extensions for land-use classification. In: Proceedings of the 18th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, San Jose, CA, pp. 270–279 (2010)

  4. Zhao, L., Tang, P., Huo, L.: A 2-D wavelet decomposition-based bag-of-visual-words model for land-use scene classification. Int. J. Remote Sens. 35(6), 2296–2310 (2014)

    Google Scholar 

  5. Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: Proceedings of the IEEE Computer Vision and Pattern Recognition, New York, NY, pp. 2169–2178 (2006)

  6. Zhou, L., Zhou, Z., Hu, D.: Scene classification using a multi-resolution bag-of-features model. Pattern Recognit. 46(1), 424–433 (2013)

    Article  Google Scholar 

  7. Zhao, L., Tang, P., Huo, L.: Land-use scene classification using a concentric circle-structured multiscale bag-of-visual-words model. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 7(12), 4620–4631 (2014)

    Article  Google Scholar 

  8. Chen, S., Tian, Y.: Pyramid of spatial relatons for scene-level land use classification. IEEE Trans. Geosci. Remote Sens. 53(4), 1947–1957 (2015)

    Article  Google Scholar 

  9. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  10. Risojevic, V., Babic, Z.: Aerial image classification using structural texture similarity. In: Proceedings of IEEE International Symposium on Signal Processing and Information Technology, Bilbao, Spain, pp. 190–195 (2011)

  11. Risojevic, V., Babic, Z.: Fusion of global and local descriptors for remote sensing image classification. IEEE Geosci. Remote Sens. Lett. 10(4), 836–840 (2013)

    Article  Google Scholar 

  12. Cheriyadat, A.M.: Unsupervised feature learning for aerial scene classification. IEEE Trans. Geosci. Remote Sens. 52(1), 439–451 (2014)

    Article  Google Scholar 

  13. Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelope. Int. J. Comput. Vis. 42(3), 145–175 (2001)

    Article  MATH  Google Scholar 

  14. Tola, E., Lepetit, V., Fua, P.: Daisy: an efficient dense descriptor applied to wide-baseline stereo. IEEE Trans. Pattern Anal. Mach. Intell. 32(5), 815–830 (2010)

    Article  Google Scholar 

  15. Zheng, X., Sun, X., Fu, K., Wang, H.: Automatic annotation of satellite images via multifeature joint sparse coding with spatial relation constraint. IEEE Geosci. Remote Sens. Lett. 10(4), 652–656 (2013)

    Article  Google Scholar 

  16. Zhang, Y., Zheng, X., Liu, G., Sun, X., Wang, H., Fu, K.: Semi-supervised manifold learning based multigraph fusion for high-resolution remote sensing image classification. IEEE Geosci. Remote Sens. Lett. 11(2), 464–468 (2014)

  17. Ojala, T., Pietikainen, M., Maenpaa, T.T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)

    Article  MATH  Google Scholar 

  18. Li, W., Chen, C., Su, H., Du, Q.: Local binary patterns and extreme learning machine for hyperspectral imagery classification. IEEE Trans. Geosci. Remote Sens. 53(7), 3681–3693 (2015)

    Article  Google Scholar 

  19. Chen, C., Jafari, R., Kehtarnavaz, N.: Action Recognition from depth sequences using depth motion maps-based local binary patterns. In: Proceedings of the IEEE Winter Conference on Applications of Computer Vision, Waikoloa Beach, HI, pp. 1092–1099 (2015)

  20. Guo, Z., Zhang, L., Zhang, D.: A completed modeling of local binary pattern operator for texture classification. IEEE Trans. Image Process. 19(6), 1657–1663 (2010)

    Article  MathSciNet  Google Scholar 

  21. Huang, G.B., Zhou, H., Ding, X., Zhang, R.: Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst. Man Cybern. Part B Cybern. 42(2), 513–529 (2012)

    Article  Google Scholar 

  22. Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70(1–3), 489–501 (2006)

    Article  Google Scholar 

  23. Dai, D., Yang, W.: Satellite image classification via two-layer sparse coding with biased image representation. IEEE Geosci. Remote Sens. Lett. 8(1), 173176 (2011)

    Article  Google Scholar 

  24. Sheng, G., Yang, W., Xu, T., Sun, H.: High-resolution satellite scene classification using a sparse coding based multiple feature combination. Int. J. Remote Sens. 33(8), 2395–2412 (2011)

    Article  Google Scholar 

  25. Jolliffe, I.: Principal Component Analysis, 2nd edn. Springer, New York (2002)

    MATH  Google Scholar 

  26. Zhang, F., Du, B., Zhang, L.: Saliency-guided unsupervised feature learning for scene classification. IEEE Trans. Geosci. Remote Sens. 53(4), 2175–2184 (2015)

    Article  Google Scholar 

  27. Shao, W., Yang, W., Xia, G.S., Liu, G.: A hierarchical scheme of multiple feature fusion for high-resolution satellite scene categorization. In: Proceedings of the 9th International Conference on Computer Vision Systems, St. Petersburg, Russia, pp. 324–333 (2013)

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, University of Texas at Dallas, Richardson, TX, USA

    Chen Chen

  2. School of Automation Science and Electrical Engineering, Beihang University, Beijing, China

    Baochang Zhang

  3. School of Earth Science and Engineering, Hohai University, Nanjing, China

    Hongjun Su

  4. College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China

    Wei Li & Lu Wang

Authors
  1. Chen Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Baochang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hongjun Su
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wei Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lu Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Baochang Zhang, Hongjun Su or Wei Li.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, C., Zhang, B., Su, H. et al. Land-use scene classification using multi-scale completed local binary patterns. SIViP 10, 745–752 (2016). https://doi.org/10.1007/s11760-015-0804-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-015-0804-2

Keywords

Search

Navigation