Advertisement

Cluster Computing

, Volume 22, Supplement 4, pp 9951–9966 | Cite as

Change detection methods based on low-rank sparse representation for multi-temporal remote sensing imagery

  • Shulei Wu
  • Yong Bai
  • Huandong ChenEmail author
Article

Abstract

With the development of remote sensing image applications, remote sensing imagery is an important technology to make a dynamic detection for changes of lands or coastal zones. Though high resolution remote sensing imagery provides very good performance and large information for the spatial structure variation, the limitation of its spatial structure makes change detection difficult. In this paper, we propose two change detection methods for multi-temporal remote sensing images which are based on low-rank sparse decomposition and based on low-rank matrix representation. An observation matrix is constructed by ordering each band of remote sensing images into a vector. We utilize bilateral random projection method to make low rank decomposition to get a sparse matrix. We then obtain the change map by using nearest neighbor to cluster the change parts from the sparse matrix. On the other hand, by dividing the difference set of multi-temporal remote sensing images into non-overlapping squares with equal size and tiling these squares, an observation matrix is built up. We make up a feature space matrix by low rank matrix representation to build a sparse representation model, and combine nearest neighbor method to make change detection for multi-temporal remote sensing dataset. This change detection method is addressed by iterating between kernel norm minimization and sparsity minimization. The experimental results show that our proposed methods perform better in detecting changes than the other change detection methods.

Keywords

Change detection Sparse representation Low-rank sparse decomposition Low-rank matrix representation 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 61163042 and 61663007).

References

  1. 1.
    Hussain, M., Chen, D., Cheng, A.: Change detection from remotely sensed images: from pixel-based to object-based approaches. ISPRS J. Photogramm. Remote Sens. 80, 91–106 (2013)CrossRefGoogle Scholar
  2. 2.
    Singh, A.: Digital change detection techniques using remotely-sensed data. Int. J. Remote Sens. 10(6), 989–1003 (1989)CrossRefGoogle Scholar
  3. 3.
    Jianya, G., Haigang, S., Guorui, M., et al.: A review of multi-temporal remote sensing data change detection algorithms. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 37(B7), 757–762 (2008)Google Scholar
  4. 4.
    Araya, Y.H., Hergarten, C.: A comparison of pixel and object-based land cover classification: a case study of the Asmara region, Eritrea. WIT Trans. Built Environ. 100, 233–243 (2008)CrossRefGoogle Scholar
  5. 5.
    Chen, G., Hay, G.J., Carvalho, L.M.T., et al.: Object-based change detection. Int. J. Remote Sens. 33(14), 4434–4457 (2012)CrossRefGoogle Scholar
  6. 6.
    Ghosh, A., Mishra, N.S., Ghosh, S.: Fuzzy clustering algorithms for unsupervised change detection in remote sensing images. Inf. Sci. 181(4), 699–715 (2011)CrossRefGoogle Scholar
  7. 7.
    Huang, C., Song, K., Kim, S., et al.: Use of a dark object concept and support vector machines to automate forest cover change analysis. Remote Sens. Environ. 112(3), 970–985 (2008)CrossRefGoogle Scholar
  8. 8.
    Xiuwan, C.: Using remote sensing and GIS to analyse land cover change and its impacts on regional sustainable development. Int. J. Remote Sens. 23(1), 107–124 (2002)CrossRefGoogle Scholar
  9. 9.
    Vila, J.P.S., Barbosa, P.: Post-fire vegetation regrowth detection in the Deiva Marina region (Liguria-Italy) using Landsat TM and ETM+ data. Ecol. Model. 221(1), 75–84 (2010)CrossRefGoogle Scholar
  10. 10.
    Quarmby, N.A., Cushnie, J.L.: Monitoring urban land cover changes at the urban fringe from SPOT HRV imagery in south-east England. Int. J. Remote Sens. 10(6), 953–963 (1989)CrossRefGoogle Scholar
  11. 11.
    Howarth, P.J., Wickware, G.M.: Procedures for change detection using Landsat digital data. Int. J. Remote Sens. 2(3), 277–291 (1981)CrossRefGoogle Scholar
  12. 12.
    Rignot, E.J.M., Van Zyl, J.J.: Change detection techniques for ERS-1 SAR data. IEEE Trans. Geosci. Remote Sens. 31(4), 896–906 (1993)CrossRefGoogle Scholar
  13. 13.
    Johnson, R.D., Kasischke, E.S.: Change vector analysis: a technique for the multispectral monitoring of land cover and condition. Int. J. Remote Sens. 19(3), 411–426 (1998)CrossRefGoogle Scholar
  14. 14.
    Chen, Z., Elvidge, C.D., Groeneveld, D.P.: Vegetation change detection using high spectral resolution vegetation indices. Remote Sens. Change Change detect. Techn. 2395 (1998)Google Scholar
  15. 15.
    Deng, J.S., Wang, K., Deng, Y.H., et al.: 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
  16. 16.
    Jin, S., Sader, S.A.: Comparison of time series tasseled cap wetness and the normalized difference moisture index in detecting forest disturbances. Remote Sens. Environ. 94(3), 364–372 (2005)CrossRefGoogle Scholar
  17. 17.
    Celik, T., Ma, K.K.: Multitemporal image change detection using undecimated discrete wavelet transform and active contours. IEEE Trans. Geosci. Remote Sens. 49(2), 706–716 (2011)CrossRefGoogle Scholar
  18. 18.
    Celik, T.: Multiscale change detection in multitemporal satellite images. IEEE Geosci. Remote Sens. Lett. 6(4), 820–824 (2009)CrossRefGoogle Scholar
  19. 19.
    Volpi, M., Tuia, D., Bovolo, F., et al.: Supervised change detection in VHR images using contextual information and support vector machines. Int. J. Appl. Earth Obs. Geoinf. 20, 77–85 (2013)CrossRefGoogle Scholar
  20. 20.
    Lei, Z., Fang, T., Huo, H., et al.: Bi-temporal texton forest for land cover transition detection on remotely sensed imagery. IEEE Trans. Geosci. Remote Sens. 52(2), 1227–1237 (2014)CrossRefGoogle Scholar
  21. 21.
    Camps-Valls, G., Gómez-Chova, L., Muñoz-Marí, J., et al.: Kernel-based framework for multitemporal and multisource remote sensing data classification and change detection. IEEE Trans. Geosci. Remote Sens. 46(6), 1822–1835 (2008)CrossRefGoogle Scholar
  22. 22.
    Chen K, Huo C, Zhou Z, et al.: Semi-supervised change detection via Gaussian processes. In: Geoscience and Remote Sensing Symposium, 2009 IEEE International, IGARSS 2009. IEEE, 2009, 2: II-996-II-999Google Scholar
  23. 23.
    Roy, M., Ghosh, S., Ghosh, A.: A novel approach for change detection of remotely sensed images using semi-supervised multiple classifier system. Inf. Sci. 269, 35–47 (2014)CrossRefGoogle Scholar
  24. 24.
    Wu, C., Zhang, L., Zhang, L.: A scene change detection framework for multi-temporal very high resolution remote sensing images. Signal Process. 124, 184–197 (2015)CrossRefGoogle Scholar
  25. 25.
    Huang, X., Lu, Q., Zhang, L.: A multi-index learning approach for classification of high-resolution remotely sensed images over urban areas. ISPRS J. Photogramm. Remote Sens. 90, 36–48 (2014)CrossRefGoogle Scholar
  26. 26.
    Zhou, T., Tao, D.: Godec: Randomized low-rank & sparse matrix decomposition in noisy case. In: International Conference on Machine Learning. Omnipress, Madison (2011)Google Scholar
  27. 27.
    Candès, E.J., Li, X., Ma, Y., et al.: Robust principal component analysis? J. ACM (JACM) 58(3), 11 (2011)MathSciNetCrossRefGoogle Scholar
  28. 28.
    Gao, Z., Cheong, L.F., Wang, Y.X.: Block-sparse RPCA for salient motion detection. IEEE Trans. Pattern Anal. Mach. Intell. 36(10), 1975–1987 (2014)CrossRefGoogle Scholar
  29. 29.
    Liu, X., Zhao, G., Yao, J., et al.: Background subtraction based on low-rank and structured sparse decomposition. IEEE Trans. Image Process. 24(8), 2502–2514 (2015)MathSciNetCrossRefGoogle Scholar
  30. 30.
    Liu, G., Lin, Z., Yu, Y.: Robust subspace segmentation by low-rank representation. In: Proceedings of the 27th International Conference on Machine Learning (ICML-10). pp. 663-670 (2010)Google Scholar
  31. 31.
    Moody, D.I., Brumby, S.P., Rowland, J.C., et al.: Change detection and classification of land cover in multispectral satellite imagery using clustering of sparse approximations (CoSA) over learned feature dictionaries. In: Applied Imagery Pattern Recognition Workshop (AIPR), 2014 IEEE. IEEE, pp. 1–10 (2014)Google Scholar
  32. 32.
    Xu, Y., Wu, Z., Li, J., et al.: Anomaly detection in hyperspectral images based on low-rank and sparse representation. IEEE Trans. Geosci. Remote Sens. 54(4), 1990–2000 (2016)CrossRefGoogle Scholar
  33. 33.
    Zhou, J.: Matrix Analysis and Application. Sichuan University Press, Chengdu (2008)Google Scholar
  34. 34.
    Roweis, S.T.: EM algorithms for PCA and SPCA. In: Advances in Neural Information Processing Systems, pp. 626–632 (1998)Google Scholar
  35. 35.
    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
  36. 36.
    Bazi, Y., Bruzzone, L., Melgani, F.: An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images. IEEE Trans. Geosci. Remote Sens. 43(4), 874–887 (2005)CrossRefGoogle Scholar
  37. 37.
    Wu, S., Bai, Y., et al.: A remote sensing image classification method based on sparse representation. Multimed. Tools Appl. 75(19), 12137–12154 (2016)CrossRefGoogle Scholar
  38. 38.
    Wu, S., Bai, Y., et al.: Remote sensing image noise reduction using wavelet coefficients based on OMP. Optik-Int. J. Light Electron Opt. 126(15), 1439–1444 (2015)CrossRefGoogle Scholar
  39. 39.
    Shulei, Wu, Bai, Yong, et al.: The optimal band combination joint WCOMP+BPNN classification method for remote sensing image. J. Comput. Inf. Syst. 11(8), 2873–2884 (2015)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.College of Information Science and TechnologyHainan Normal UniversityHaikouChina
  2. 2.College of Information Science and TechnologyHainan UniversityHaikouChina

Personalised recommendations