Automatic Band Selection Using Spatial-Structure Information and Classifier-Based Clustering

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Volumn 9, Issue 9, 2016, Pages 4352-4360

  Cao, Xianghai ^a   Wu, Bin ^a   Tao, Dacheng ^b   Jiao, Licheng ^a  

^a XIDIAN UNIVERSITY   (China)

^b UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

Author keywords

Automatic band selection (ABS); clustering with classifier; low discriminating bands; number of bands

Indexed keywords

IMAGE PROCESSING; SPECTROSCOPY;

BASED CLUSTERING; DISCRIMINATING POWER; HYPERSPECTRAL IMAGE PROCESSING; NUMBER OF CLUSTERS; SPATIAL STRUCTURE; SPATIAL STRUCTURE INFORMATION; STORAGE REQUIREMENTS; STRUCTURE INFORMATION;

CLASSIFICATION (OF INFORMATION);

CLUSTER ANALYSIS; DATA SET; IMAGE CLASSIFICATION; IMAGE PROCESSING; SPATIAL ANALYSIS;

EID: 84954155328     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2015.2509461     Document Type: Article

References (38)

1. I. Jolliffe, Principal Component Analysis. Hoboken, NJ, USA: Wiley, 2002.
2. M. Li and B. Yuan, "2D-LDA: A statistical linear discriminant analysis for image matrix," Pattern Recognit. Lett., vol. 26, no. 5, pp. 527-532, 2005.
3. H. Yang, Q. Du, H. Su, and Y. Sheng, "An efficient method for supervised hyperspectral band selection," IEEE Geosci. Remote Sens. Lett., vol. 8, no. 1, pp. 138-142, Jan. 2011.
4. M. Riedmann and E. J. Milton, "Supervised band selection for optimal use of data from airborne hyperspectral sensors," in Proc. IEEE Int. Symp. Geosci. Remote Sens. Symp., 2003, pp. 1770-1772.
5. C. Sui, Y. Tian, Y. Xu, and Y. Xie, "Unsupervised band selection by integrating the overall accuracy and redundancy," IEEE Geosci. Remote Sens. Lett., vol. 12, no. 1, pp. 185-189, Jan. 2015.
6. B. Guo, S. R. Gunn, R. Damper, and J. Nelson, "Band selection for hyperspectral image classification using mutual information," IEEE Geosci. Remote Sens. Lett., vol. 3, no. 4, pp. 522-526, Oct. 2006.
7. J. Feng, L. Jiao, F. Liu, T. Sun, and X. Zhang, "Mutual-information-based semi-supervised hyperspectral band selection with high discrimination, high information, and low redundancy," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 5, pp. 2956-2969, May 2015.
8. L. Chen, R. Huang, and W. Huang, "Graph-based semi-supervised weighted band selection for classification of hyperspectral data," in Proc. IEEE Int. Conf. Audio Lang. Image Process. (ICALIP), 2010, pp. 1123-1126.
9. B. Du and L. Zhang, "A discriminative metric learning based anomaly detection method," IEEE Trans. Geosci. Remote Sens., vol. 52, no. 11, pp. 6844-6857, Nov. 2014.
10. B. Du, Y. Zhang, L. Zhang, and L. Zhang, "A hypothesis independent subpixel target detector for hyperspectral images," Signal Process., vol. 110, pp. 244-249, 2015.
11. B. Du and L. Zhang, "Target detection based on a dynamic subspace," Pattern Recognit., vol. 47, no. 1, pp. 344-358, 2014.
12. Q. Du and H. Yang, "Similarity-based unsupervised band selection for hyperspectral image analysis," IEEE Geosci. Remote Sens. Lett., vol. 5, no. 4, pp. 564-568, Oct. 2008.
13. C.-I. Chang, Q. Du, T.-L. Sun, and M. L. Althouse, "A joint band prioritization and band-decorrelation approach to band selection for hyperspectral image classification," IEEE Trans. Geosci. Remote Sens., vol. 37, no. 6, pp. 2631-2641, Nov. 1999.
14. H. Du, H. Qi, X. Wang, R. Ramanath, and W. E. Snyder, "Band selection using independent component analysis for hyperspectral image processing," in Proc. IEEE 32nd Appl. Imagery Pattern Recog. Workshop, 2003, pp. 93-98.
15. Y. Yuan, G. Zhu, and Q. Wang, "Hyperspectral band selection by multitask sparsity pursuit," IEEE Geosci. Remote Sens., vol. 53, no. 2, pp. 631-644, Feb. 2015.
16. P. Estévez et al., "Normalized mutual information feature selection," IEEE Trans. Neural Netw., vol. 20, no. 2, pp. 189-201, Feb. 2009.
17. H. Liu, J. Sun, L. Liu, and H. Zhang, "Feature selection with dynamic mutual information," Pattern Recog., vol. 42, no. 7, pp. 1330-1339, 2009.
18. F. Fleuret, "Fast binary feature selection with conditional mutual information," J. Mach. Learn. Res., vol. 5, pp. 1531-1555, 2004.
19. J. Feng, L. Jiao, X. Zhang, and T. Sun, "Hyperspectral band selection based on trivariate mutual information and clonal selection," IEEE Trans. Geosci. Remote Sens., vol. 52, no. 7, pp. 4092-4105, Jul. 2014.
20. A. Martínez-Usó, F. Pla, J. M. Sotoca, and P. García-Sevilla, "Clusteringbased hyperspectral band selection using information measures," IEEE Trans. Geosci. Remote Sens., vol. 45, no. 12, pp. 4158-4171, Dec. 2007.
21. P. Yan-Bin and A. Jie-Qing, "Hyperspectral imagery classification based on spectral clustering band selection," Opto-Electron. Eng., vol. 2, p. 017, 2012.
22. Y. Qian, F. Yao, and S. Jia, "Band selection for hyperspectral imagery using affinity propagation," IET Comput. Vis., vol. 3, no. 4, pp. 213-222, 2009.
23. B. J. Frey and D. Dueck, "Clustering by passing messages between data points," Science, vol. 315, no. 5814, pp. 972-976, 2007.
24. H. Su, H. Yang, Q. Du, and Y. Sheng, "Semisupervised band clustering for dimensionality reduction of hyperspectral imagery," IEEE Geosci. Remote Sens. Lett., vol. 8, no. 6, pp. 1135-1139, Nov. 2011.
25. A. Datta, S. Ghosh, and A. Ghosh, "Combination of clustering and ranking techniques for unsupervised band selection of hyperspectral images," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 8, no. 6, pp. 2814-2823, Jun. 2015.
26. X. Geng, K. Sun, L. Ji, and Y. Zhao, "A fast volume-gradient-based band selection method for hyperspectral image," IEEE Trans. Geosci. Remote Sens., vol. 52, no. 11, pp. 7111-7119, Nov. 2014.
27. P. Pant, V. Heikkinen, I. Korpela, M. Hauta-Kasari, and T. Tokola, "Logistic regression-based spectral band selection for tree species classification: Effects of spatial scale and balance in training samples," IEEE Geosci. Remote Sens. Lett., vol. 11, no. 9, pp. 1604-1608, Sep. 2014.
28. Y.-L. Chang, J.-N. Liu, Y.-L. Chen, W.-Y. Chang, T.-J. Hsieh, and B. Huang, "Hyperspectral band selection based on parallel particle swarm optimization and impurity function band prioritization schemes," J. Appl. Remote Sens., vol. 8, no. 1, pp. 084798-084798, 2014.
29. H. Yang, Q. Du, and G. Chen, "Unsupervised hyperspectral band selection using graphics processing units," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 4, no. 3, pp. 660-668, Sep. 2011.
30. S. Jia, Z. Ji, Y. Qian, and L. Shen, "Unsupervised band selection for hyperspectral imagery classification without manual band removal," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 5, no. 2, pp. 531-543, Apr. 2012.
31. K. Sun, X. Geng, L. Ji, and Y. Lu, "A new band selection method for hyperspectral image based on data quality," IEEE Sel. Topics Appl. Earth Observ. Remote Sens., vol. 7, no. 6, pp. 2697-2703, Jun. 2014.
32. C.-I. Chang and Q. Du, "Estimation of number of spectrally distinct signal sources in hyperspectral imagery," IEEE Trans. Geosci. Remote Sens., vol. 42, no. 3, pp. 608-619, Mar. 2004.
33. J. Canny, "A computational approach to edge detection," IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-8, no. 6, pp. 679-698, Nov. 1986.
34. S. Cacciatore, C. Luchinat, and L. Tenori, "Knowledge discovery by accuracy maximization," Proc. Nat. Acad. Sci., vol. 111, no. 14, pp. 5117-5122, 2014.
35. K. Sun, X. Geng, and L. Ji, "Exemplar component analysis: A fast band selection method for hyperspectral imagery," IEEE Geosci. Remote Sens. Lett., vol. 12, no. 5, pp. 998-1002, May 2015.
36. M. Ahmad, D. I. U. Haq, Q. Mushtaq, and D. K. Bhatti, "A new statistical approach for band clustering and band selection using K-means clustering," IACSIT Int. J. Eng. Technol., vol. 3, no. 6, pp. 606-614, 2011.
37. H.-S. Park and C.-H. Jun, "A simple and fast algorithm for K-medoids clustering," Expert Syst. Appl., vol. 36, no. 2, pp. 3336-3341, 2009.
38. C.-C. Chang and C.-J. Lin, "LIBSVM: A library for support vector machines," ACM Trans. Intell. Syst. Technol. (TIST), vol. 2, no. 3, p. 27, 2011.