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Multimedia Systems
Erschienen in: Multimedia Systems 3/2016

01.06.2016 | Regular Paper

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

verfasst von: Wei Liu, Shaozi Li, Xianming Lin, YunDong Wu, Rongrong Ji

Erschienen in: Multimedia Systems | Ausgabe 3/2016

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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.
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Literatur
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)CrossRef
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)CrossRef
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)CrossRef
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)CrossRef
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
8.
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)CrossRef
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
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)CrossRef
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)CrossRef
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)CrossRef
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)CrossRef
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)CrossRef
24.
Lee, C., Landgrebe, D.A.: Feature extraction based on decision boundaries. Pattern Anal. Mach. Intell. IEEE Trans. 15(4), 388–400 (1993)CrossRef
25.
Manning, C.D., Raghavan, P., Schütze, H.: Introduction to information retrieval, vol. 1. Cambridge University Press, Cambridge (2008)CrossRefMATH
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)CrossRef
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)CrossRef
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)
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)CrossRef
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)CrossRef
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)CrossRef
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)CrossRef
36.
Tarabalka, Y., Chanussot, J., Benediktsson, J.A.: Segmentation and classification of hyperspectral images using watershed transformation. Pattern Recognit. 43(7), 2367–2379 (2010)CrossRefMATH
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)CrossRef
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)CrossRef
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.
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)CrossRef
42.
Zhong, P., Wang, R.: Learning conditional random fields for classification of hyperspectral images. Image Process. IEEE Trans. 19(7), 1890–1907 (2010)MathSciNetCrossRef
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)MathSciNetCrossRef
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)
Metadaten
Titel
Spectral–spatial co-clustering of hyperspectral image data based on bipartite graph
verfasst von
Wei Liu
Shaozi Li
Xianming Lin
YunDong Wu
Rongrong Ji
Publikationsdatum
01.06.2016
Verlag
Springer Berlin Heidelberg
Erschienen in
Multimedia Systems / Ausgabe 3/2016
Print ISSN: 0942-4962
Elektronische ISSN: 1432-1882
DOI
https://doi.org/10.1007/s00530-015-0450-0

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