Multimedia Systems

, Volume 22, Issue 3, pp 355–366 | Cite as

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

  • Wei Liu
  • Shaozi Li
  • Xianming Lin
  • YunDong Wu
  • Rongrong JiEmail author
Regular Paper


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.


Hyperspectral images Clustering Bipartite graph 



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


  1. 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)CrossRefGoogle Scholar
  2. 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)CrossRefGoogle Scholar
  3. 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)CrossRefGoogle Scholar
  4. 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)Google Scholar
  5. 5.
    Chang, C.I.: Hyperspectral imaging: techniques for spectral detection and classification. Springer, New York (2003)CrossRefGoogle Scholar
  6. 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)Google Scholar
  7. 7.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern classification. Wiley, New York (2012)zbMATHGoogle Scholar
  8. 8.
    Friedman, J.H., Stuetzle, W.: Projection pursuit regression. J. Am. Stat. Assoc. 76(376), 817–823 (1981)MathSciNetCrossRefGoogle Scholar
  9. 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)Google Scholar
  10. 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)Google Scholar
  11. 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)Google Scholar
  12. 12.
    Golub, G.H., Van Loan, C.F.: Matrix computations, vol. 3. JHU Press (2012)Google Scholar
  13. 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)CrossRefGoogle Scholar
  14. 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)zbMATHGoogle Scholar
  15. 15.
    Hertz, J.A., Krogh, A.S., Palmer, R.G.: Introduction to the Theory of Neural Computacion, vol. 1. Basic Books (1991)Google Scholar
  16. 16.
    Hyvärinen, A., Oja, E.: Independent component analysis: algorithms and applications. Neural Netw. 13(4), 411–430 (2000)CrossRefGoogle Scholar
  17. 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)CrossRefGoogle Scholar
  18. 18.
    Ji, R., Gao, Y., Hong, R., Liu, Q., Tao, D., Li, X.: Spectral-spatial constraint hyperspectral image classificationGoogle Scholar
  19. 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)CrossRefGoogle Scholar
  20. 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)CrossRefGoogle Scholar
  21. 21.
    Jolliffe, I.: Principal component analysis. Wiley Online Library (2005)Google Scholar
  22. 22.
    Jordan, F., Bach, F.: Learning spectral clustering. Adv. Neural Inf. Process. Syst. 16 (2003)Google Scholar
  23. 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)CrossRefGoogle Scholar
  24. 24.
    Lee, C., Landgrebe, D.A.: Feature extraction based on decision boundaries. Pattern Anal. Mach. Intell. IEEE Trans. 15(4), 388–400 (1993)CrossRefGoogle Scholar
  25. 25.
    Manning, C.D., Raghavan, P., Schütze, H.: Introduction to information retrieval, vol. 1. Cambridge University Press, Cambridge (2008)CrossRefzbMATHGoogle Scholar
  26. 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)CrossRefGoogle Scholar
  27. 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)CrossRefGoogle Scholar
  28. 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. 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)CrossRefGoogle Scholar
  30. 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)Google Scholar
  31. 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)Google Scholar
  32. 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)Google Scholar
  33. 33.
    Shi, J., Malik, J.: Normalized cuts and image segmentation. Pattern Anal. Mach. Intell. IEEE Trans. 22(8), 888–905 (2000)CrossRefGoogle Scholar
  34. 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)CrossRefGoogle Scholar
  35. 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)CrossRefGoogle Scholar
  36. 36.
    Tarabalka, Y., Chanussot, J., Benediktsson, J.A.: Segmentation and classification of hyperspectral images using watershed transformation. Pattern Recognit. 43(7), 2367–2379 (2010)CrossRefzbMATHGoogle Scholar
  37. 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)CrossRefGoogle Scholar
  38. 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)CrossRefGoogle Scholar
  39. 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)Google Scholar
  40. 40.
    Von Luxburg, U.: A tutorial on spectral clustering. Statistics Comput. 17(4), 395–416 (2007)MathSciNetCrossRefGoogle Scholar
  41. 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)CrossRefGoogle Scholar
  42. 42.
    Zhong, P., Wang, R.: Learning conditional random fields for classification of hyperspectral images. Image Process. IEEE Trans. 19(7), 1890–1907 (2010)MathSciNetCrossRefGoogle Scholar
  43. 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)MathSciNetCrossRefGoogle Scholar
  44. 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)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Wei Liu
    • 1
  • Shaozi Li
    • 1
  • Xianming Lin
    • 1
  • YunDong Wu
    • 2
  • Rongrong Ji
    • 1
    Email author
  1. 1.Xiamen UniversityXiamenChina
  2. 2.Jimei UniversityXiamenChina

Personalised recommendations