Skip to main content

Advertisement

Springer Nature Link
Log in

Spectral–spatial co-clustering of hyperspectral image data based on bipartite graph

  • Regular Paper
  • Published:
Multimedia Systems Aims and scope Submit manuscript

Abstract

The high dimensionality of hyperspectral images are usually coupled with limited data available, which degenerates the performances of clustering techniques based only on pixel spectral. To improve the performances of clustering, incorporation of spectral and spatial is needed. As an attempt in this direction, in this paper, we propose an unsupervised co-clustering framework to address both the pixel spectral and spatial constraints, in which the relationship among pixels is formulated using an undirected bipartite graph. The optimal partitions are obtained by spectral clustering on the bipartite graph. Experiments on four hyperspectral data sets are performed to evaluate the effectiveness of the proposed framework. Results also show our method achieves similar or better performance when compared to the other clustering methods.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 12

Similar content being viewed by others

References

  1. Benediktsson, J.A., Palmason, J.A., Sveinsson, J.R.: Classification of hyperspectral data from urban areas based on extended morphological profiles. Geosci. Remote Sens. IEEE Trans. 43(3), 480–491 (2005)

    Article  Google Scholar 

  2. Bilgin, G., Erturk, S., Yildirim, T.: Unsupervised classification of hyperspectral-image data using fuzzy approaches that spatially exploit membership relations. Geosci. Remote Sens. Lett. IEEE 5(4), 673–677 (2008)

    Article  Google Scholar 

  3. Bruzzone, L., Persello, C.: A novel context-sensitive semisupervised svm classifier robust to mislabeled training samples. Geosci. Remote Sens. IEEE Trans. 47(7), 2142–2154 (2009)

    Article  Google Scholar 

  4. Cao, F., Ester, M., Qian, W., Zhou, A.: Density-based clustering over an evolving data stream with noise. In: Proceedings of the 2006 SIAM International Conference on Data Mining, pp. 328–339 (2006)

  5. Chang, C.I.: Hyperspectral imaging: techniques for spectral detection and classification. Springer, New York (2003)

    Book  Google Scholar 

  6. Dhillon, I.S.: Co-clustering documents and words using bipartite spectral graph partitioning. In: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 269–274. ACM (2001)

  7. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern classification. Wiley, New York (2012)

    MATH  Google Scholar 

  8. Friedman, J.H., Stuetzle, W.: Projection pursuit regression. J. Am. Stat. Assoc. 76(376), 817–823 (1981)

    Article  MathSciNet  Google Scholar 

  9. Gao, B., Liu, T.Y., Qin, T., Zheng, X., Cheng, Q.S., Ma, W.Y.: Web image clustering by consistent utilization of visual features and surrounding texts. In: Proceedings of the 13th annual ACM international conference on Multimedia, pp. 112–121. ACM (2005)

  10. Gao, B., Liu, T.Y., Zheng, X., Cheng, Q.S., Ma, W.Y.: Consistent bipartite graph co-partitioning for star-structured high-order heterogeneous data co-clustering. In: Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, pp. 41–50. ACM (2005)

  11. Gao, Y., Chua, T.S.: Hyperspectral image classification by using the pixel spatial correlation. In: 19th International Conference on Multimedia Model, pp. 141–151 (2013)

  12. Golub, G.H., Van Loan, C.F.: Matrix computations, vol. 3. JHU Press (2012)

  13. Hagen, L., Kahng, A.B.: New spectral methods for ratio cut partitioning and clustering. Computer-aided Des. Integrated Circuits sys. IEEE Trans. 11(9), 1074–1085 (1992)

    Article  Google Scholar 

  14. Hartigan, J.A., Wong, M.A.: Algorithm as 136: A k-means clustering algorithm. J. R. Stat. Soc. Series C 28(1), 100–108 (1979)

    MATH  Google Scholar 

  15. Hertz, J.A., Krogh, A.S., Palmer, R.G.: Introduction to the Theory of Neural Computacion, vol. 1. Basic Books (1991)

  16. Hyvärinen, A., Oja, E.: Independent component analysis: algorithms and applications. Neural Netw. 13(4), 411–430 (2000)

    Article  Google Scholar 

  17. Jackson, Q., Landgrebe, D.A.: Adaptive bayesian contextual classification based on markov random fields. Geosci. Remote Sens. IEEE Trans. 40(11), 2454–2463 (2002)

    Article  Google Scholar 

  18. Ji, R., Gao, Y., Hong, R., Liu, Q., Tao, D., Li, X.: Spectral-spatial constraint hyperspectral image classification

  19. Jia, X., Richards, J.: Managing the spectral-spatial mix in context classification using markov random fields. Geosci. Remote Sens. Lett. IEEE 5(2), 311–314 (2008)

    Article  Google Scholar 

  20. Jimenez, L.O., Landgrebe, D.A.: Hyperspectral data analysis and supervised feature reduction via projection pursuit. Geosci. Remote Sens. IEEE Trans. 37(6), 2653–2667 (1999)

    Article  Google Scholar 

  21. Jolliffe, I.: Principal component analysis. Wiley Online Library (2005)

  22. Jordan, F., Bach, F.: Learning spectral clustering. Adv. Neural Inf. Process. Syst. 16 (2003)

  23. Kuo, B.C., Li, C.H., Yang, J.M.: Kernel nonparametric weighted feature extraction for hyperspectral image classification. Geosci. Remote Sens. IEEE Trans. 47(4), 1139–1155 (2009)

    Article  Google Scholar 

  24. Lee, C., Landgrebe, D.A.: Feature extraction based on decision boundaries. Pattern Anal. Mach. Intell. IEEE Trans. 15(4), 388–400 (1993)

    Article  Google Scholar 

  25. Manning, C.D., Raghavan, P., Schütze, H.: Introduction to information retrieval, vol. 1. Cambridge University Press, Cambridge (2008)

    Book  MATH  Google Scholar 

  26. Martínez-Usó, A., Pla, F., Sotoca, J.M., García-Sevilla, P.: Clustering-based hyperspectral band selection using information measures. Geosci. Remote Sens. IEEE Trans. 45(12), 4158–4171 (2007)

    Article  Google Scholar 

  27. Neher, R., Srivastava, A.: A bayesian mrf framework for labeling terrain using hyperspectral imaging. Geosci. Remote Sens. IEEE Trans. 43(6), 1363–1374 (2005)

    Article  Google Scholar 

  28. Ng, A.Y., Jordan, M.I., Weiss, Y., et al.: On spectral clustering: Analysis and an algorithm. Adv. Neural Inf. Process. Syst. 2, 849–856 (2002)

    Google Scholar 

  29. Paoli, A., Melgani, F., Pasolli, E.: Clustering of hyperspectral images based on multiobjective particle swarm optimization. Geosci. Remote Sens. IEEE Trans. 47(12), 4175–4188 (2009)

    Article  Google Scholar 

  30. Qiu, G.: Image and feature co-clustering. In: Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on, vol. 4, pp. 991–994. IEEE (2004)

  31. Rege, M., Dong, M., Fotouhi, F.: Co-clustering documents and words using bipartite isoperimetric graph partitioning. In: Data Mining, 2006. ICDM’06. Sixth International Conference on, pp 532–541. IEEE (2006)

  32. Serpico, S.B., DInca, M., Melgani, F., Moser, G.: A comparison of feature reduction techniques for classification of hyperspectral remote-sensing data. In: Proceedings of SPIE, Image and Signal Processing of Remote Sensing VIII, vol. 4885 (2003)

  33. Shi, J., Malik, J.: Normalized cuts and image segmentation. Pattern Anal. Mach. Intell. IEEE Trans. 22(8), 888–905 (2000)

    Article  Google Scholar 

  34. Solberg, A.H., Taxt, T., Jain, A.K.: A markov random field model for classification of multisource satellite imagery. Geosci. Remote Sens. IEEE Trans. 34(1), 100–113 (1996)

    Article  Google Scholar 

  35. Tarabalka, Y., Benediktsson, J.A., Chanussot, J.: Spectral-spatial classification of hyperspectral imagery based on partitional clustering techniques. Geosci. Remote Sens. IEEE Trans. 47(8), 2973–2987 (2009)

    Article  Google Scholar 

  36. Tarabalka, Y., Chanussot, J., Benediktsson, J.A.: Segmentation and classification of hyperspectral images using watershed transformation. Pattern Recognit. 43(7), 2367–2379 (2010)

    Article  MATH  Google Scholar 

  37. Tarabalka, Y., Fauvel, M., Chanussot, J., Benediktsson, J.A.: Svm-and mrf-based method for accurate classification of hyperspectral images. Geosci. Remote Sens. Lett. IEEE 7(4), 736–740 (2010)

    Article  Google Scholar 

  38. Tran, T.N., Wehrens, R., Hoekman, D.H., Buydens, L.M.: Initialization of markov random field clustering of large remote sensing images. Geosci. Remote Sens. IEEE Trans. 43(8), 1912–1919 (2005)

    Article  Google Scholar 

  39. Villa, A., Chanussot, J., Benediktsson, J.A., Jutten, C., Dambreville, R.: Unsupervised methods for the classification of hyperspectral images with low spatial resolution. Pattern Recognit. (2012)

  40. Von Luxburg, U.: A tutorial on spectral clustering. Statistics Comput. 17(4), 395–416 (2007)

    Article  MathSciNet  Google Scholar 

  41. Xia, G.S., He, C., Sun, H.: A rapid and automatic mrf-based clustering method for sar images. Geosci. Remote Sens. Lett. IEEE 4(4), 596–600 (2007)

    Article  Google Scholar 

  42. Zhong, P., Wang, R.: Learning conditional random fields for classification of hyperspectral images. Image Process. IEEE Trans. 19(7), 1890–1907 (2010)

    Article  MathSciNet  Google Scholar 

  43. Zhong, P., Wang, R.: Modeling and classifying hyperspectral imagery by crfs with sparse higher order potentials. Geosci. Remote Sens. IEEE Trans. 49(2), 688–705 (2011)

    Article  MathSciNet  Google Scholar 

  44. Zhou, D., Huang, J., Schölkopf, B.: Learning with hypergraphs: Clustering, classification, and embedding. In: Advances in Neural Information Processing Systems, pp. 1601–1608 (2006)

Download references

Acknowledgments

This work is supported by the Nature Science Foundation of China (No. 61373076) and National Outstanding Youth Science Foundation of China (No. 61422210).

Author information

Authors and Affiliations

  1. Xiamen University, Xiamen, China

    Wei Liu, Shaozi Li, Xianming Lin & Rongrong Ji

  2. Jimei University, Xiamen, China

    YunDong Wu

Authors
  1. Wei Liu
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Shaozi Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Xianming Lin
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. YunDong Wu
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Rongrong Ji
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Rongrong Ji.

Additional information

Communicated by F. Wu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, W., Li, S., Lin, X. et al. Spectral–spatial co-clustering of hyperspectral image data based on bipartite graph. Multimedia Systems 22, 355–366 (2016). https://doi.org/10.1007/s00530-015-0450-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00530-015-0450-0

Keywords