Band Selection Using Improved Sparse Subspace Clustering for Hyperspectral Imagery Classification

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

Volumn 8, Issue 6, 2015, Pages 2784-2797

  Sun, Weiwei ^a,b   Zhang, Liangpei ^a   Du, Bo ^a   Li, Weiyue ^c   Mark Lai, Yenming ^d  

^a WUHAN UNIVERSITY   (China)

^b NINGBO UNIVERSITY   (China)

^c SHANGHAI NORMAL UNIVERSITY   (China)

^d UNIVERSITY OF TEXAS AT AUSTIN   (United States)

Author keywords

Band selection; classification; hyperspectral imagery (HSI); improved sparse subspace clustering (ISSC)

Indexed keywords

ALGORITHMS; CLASSIFICATION (OF INFORMATION); CLUSTERING ALGORITHMS; FACTORIZATION; MATRIX ALGEBRA; OPTIMIZATION; PRINCIPAL COMPONENT ANALYSIS; REMOTE SENSING; SPECTROSCOPY; STRUCTURE (COMPOSITION);

BAND SELECTION; CLASSIFICATION ACCURACY; HYPERSPECTRAL IMAGERY; HYPERSPECTRAL IMAGERY CLASSIFICATIONS; LINEAR CONSTRAINED MINIMUM VARIANCES; SPARSE NON-NEGATIVE MATRIX FACTORIZATIONS; SPECTRAL INFORMATION DIVERGENCES; SUB-SPACE CLUSTERING;

VECTORS;

ALGORITHM; CLUSTER ANALYSIS; IMAGE CLASSIFICATION; LEAST SQUARES METHOD; OPTIMIZATION; REGRESSION ANALYSIS; SATELLITE DATA; SATELLITE IMAGERY;

EID: 85027925065     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2015.2417156     Document Type: Article

References (57)

1. 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.
2. R. A. Garcia, P. R. Fearns, and L. I. McKinna, "Detecting trend and seasonal changes in bathymetry derived from HICO imagery: A case study of Shark Bay, Western Australia," Remote Sens. Environ., vol. 147, pp. 186-205, 2014.
3. L. Demarchi et al., "Assessing the performance of two unsupervised dimensionality reduction techniques on hyperspectral APEX data for high resolution urban land-cover mapping," ISPRS J. Photogramm. Remote Sens., vol. 87, pp. 166-179, 2014.
4. M. Alonzo, B. Bookhagen, and D. A. Roberts, "Urban tree species mapping using hyperspectral and lidar data fusion," Remote Sens. Environ., vol. 148, pp. 70-83, 2014.
5. W. D. Hively et al., "Use of airborne hyperspectral imagery to map soil properties in tilled agricultural fields," Appl. Environ. Soil Sci., vol. 2011, pp. 1-13, 2011.
6. I. Herrmann et al., "Ground-level hyperspectral imagery for detecting weeds in wheat fields," Precis. Agric., vol. 14, no. 6, pp. 637-659, 2013.
7. R. J. Murphy and S. T. Monteiro, "Mapping the distribution of ferric iron minerals on a vertical mine face using derivative analysis of hyperspectral imagery (430-970 nm)," ISPRS J. Photogramm. Remote Sens., vol. 75, pp. 29-39, 2013.
8. N. Zabcic et al., "Using airborne hyperspectral data to characterize the surface pH and mineralogy of pyrite mine tailings," Int. J. Appl. Earth Observ. Geoinf., vol. 32, pp. 152-162, 2014.
9. D. Scott, "The curse of dimensionality and dimension reduction," in Multivariate Density Estimation: Theory, Practice, and Visualization. Hoboken, NJ, USA: Wiley, 1992, pp. 195-217.
10. A. Plaza et al., "Dimensionality reduction and classification of hyperspectral image data using sequences of extended morphological transformations," IEEE Trans. Geosci. Remote Sens., vol. 43, no. 3, pp. 466-479, Mar. 2005.
11. P. H. Hsu, "Feature extraction of hyperspectral images using wavelet and matching pursuit," ISPRS J. Photogramm. Remote Sens., vol. 62, no. 2, pp. 78-92, 2007.
12. M. Pal and G. M. Foody, "Feature selection for classification of hyperspectral data by SVM," IEEE Trans. Geosci. Remote Sens., vol. 48, no. 5, pp. 2297-2307, May 2010.
13. W. Sun et al., "Nonlinear dimensionality reduction via the ENH-LTSA method for hyperspectral image classification," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 7, no. 2, pp. 375-388, Feb. 2014.
14. E. Arzuaga-Cruz, L. O. Jimenez-Rodriguez, and M. Velez-Reyes, "Unsupervised feature extraction and band subset selection techniques based on relative entropy criteria for hyperspectral data analysis," AeroSense, vol. 2003, pp. 462-473, 2003.
15. P. Bajcsy and P. Groves, "Methodology for hyperspectral band selection," Photogramm. Eng. Remote Sens., vol. 70, no. 7, pp. 793-802, 2004.
16. B. Guo et al., "Band selection for hyperspectral image classification using mutual information," IEEE Geosci. Remote Sens. Lett., vol. 3, no. 4, pp. 522-526, Oct. 2006.
17. C.-I. Chang and S. Wang, "Constrained band selection for hyperspectral imagery," IEEE Trans. Geosci. Remote Sens., vol. 44, no. 6, pp. 1575-1585, Jun. 2006.
18. A. Martínez-Usó et al., "Clustering-based hyperspectral band selection using information measures," IEEE Trans. Geosci. Remote Sens., vol. 45, no. 12, pp. 4158-4171, Dec. 2007.
19. S. Jia et al., "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.
20. P. Mausel, W. Kramber, and J. Lee, "Optimum band selection for supervised classification of multispectral data," Photogramm. Eng. Remote Sens., vol. 56, pp. 55-60, 1990.
21. 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.
22. W. Xia, B. Wang, and L. Zhang, "Band selection for hyperspectral imagery: A new approach based on complex networks," IEEE Geosci. Remote Sens. Lett., vol. 10, no. 5, pp. 1229-1233, Sep. 2013.
23. Q. Du, "Band selection and its impact on target detection and classification in hyperspectral image analysis," in Proc. IEEE Workshop Adv. Tech. Anal. Remotely Sens. Data, Greenbelt, MD, USA, Oct. 27-28, 2003, pp. 374-377.
24. H. Yang et al., "An efficient method for supervised hyperspectral band selection," IEEE Geosci. Remote Sens. Lett., vol. 8, no. 1, pp. 138-142, Jan. 2011.
25. Y. Zhang, B. Du, and L. Zhang, "A sparse representation-based binary hypothesis model for target detection in hyperspectral images," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 3, pp. 1346-1354, Mar. 2015.
26. Y. Chen, N. M. Nasrabadi, and T. D. Tran, "Hyperspectral image classification using dictionary-based sparse representation," IEEE Trans. Geosci. Remote Sens., vol. 49, no. 10, pp. 3973-3985, Oct. 2011.
27. Y. Chen, N. M. Nasrabadi, and T. D. Tran, "Sparse representation for target detection in hyperspectral imagery," IEEE J. Sel. Topics Signal Process., vol. 5, no. 3, pp. 629-640, Jun. 2011.
28. S. Li and H. Qi, "Sparse representation based band selection for hyperspectral images," in Proc. 18th IEEE Int. Conf. Image Process. (ICIP), Brussels, Belgium, Sep. 11-14, 2011, pp. 2693-2696.
29. J. M. Li and Y. T. Qian, "Clustering-based hyperspectral band selection using sparse nonnegative matrix factorization," J. Zhejiang Univ. Sci. C, vol. 12, no. 7, pp. 542-549, 2011.
30. Q. Du, J. M. Bioucas-Dias, and A. Plaza, "Hyperspectral band selection using a collaborative sparse model," in Proc. IEEE Int. Geosci. Remote Sens. Symp. (IGARSS), Munich, Germany, Jul. 22-27, 2012, pp. 3054-3057.
31. S. Chepushtanova, C. Gittins, and M. Kirby, "Band selection in hyperspectral imagery using sparse support vector machines," in Proc. SPIE DSS Conf., 2014, p. 90881F.
32. E. Elhamifar and R. Vidal, "Sparse subspace clustering," in Proc. IEEE Conf. Comput. Vis. Pattern Recog. (CVPR'09), Miami, FL, USA, Jun. 20-26, 2009, pp. 2790-2797.
33. E. Elhamifar and R. Vidal, "Sparse subspace clustering: Algorithm, theory, and applications," IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 11, pp. 2765-2781, Nov. 2013.
34. U. Von Luxburg, "A tutorial on spectral clustering," Statist. Comput., vol. 17, no. 4, pp. 395-416, 2007.
35. G. H. Mohimani, M. Babaie-Zadeh, and C. Jutten, "Fast sparse representation based on smoothed L0 norm," in Independent Component Analysis and Signal Separation. New York, NY, USA: Springer, 2007, pp. 389-396.
36. K. Koh, S.-J. Kim, and S. P. Boyd, "An interior-point method for largescale L1-regularized logistic regression," J. Mach. Learn. Res., vol. 8, no. 8, pp. 1519-1555, 2007.
37. S. S. Chen, D. L. Donoho, and M. A. Saunders, "Atomic decomposition by basis pursuit," SIAM J. Sci. Comput., vol. 20, no. 1, pp. 33-61, 1999.
38. J. Yang and Y. Zhang, "Alternating direction algorithms for L1-problems in compressive sensing," SIAM J. Sci. Comput., vol. 33, no. 1, pp. 250-278, 2011.
39. R. Tibshirani, "Regression shrinkage and selection via the lasso," J. Roy. Stat. Soc. B, vol. 58, no. 1, pp. 267-288, 1996.
40. H. Zou and T. Hastie, "Regularization and variable selection via the elastic net," J. Roy. Stat. Soc. B, vol. 67, no. 2, pp. 301-320, 2005.
41. S. Wang et al., "Efficient subspace segmentation via quadratic programming," in Proc. 25th AAAI Conf. Artif. Intell., San Francisco, CA, USA, Aug. 7-11, 2011, pp. 519-524.
42. C.-Y. Lu et al., "Robust and efficient subspace segmentation via least squares regression," in Proc. 12th Eur. Conf. Comput. Vis. Part VII, Florence, Italy, Oct. 7-13, 2012, pp. 347-360.
43. G. H. Golub and C. F. Van Loan, Matrix Computations. Baltimore, Maryland: Johns Hopkins University Press, 2012.
44. S. Yan and H. Wang, "Semi-supervised learning by sparse representation," in Proc. SIAM Int. Conf. Data Min. (SDM'09), Sparks, NV, USA, Apr. 30/May 2, 2009, pp. 792-801.
45. Y. Gao, A. Choudhary, and G. Hua, "A nonnegative sparsity induced similarity measure with application to cluster analysis of spam images," in Proc. IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP), Dallas, TX, USA, Mar. 14-19, 2010, pp. 5594-5597.
46. S. Wu, X. Feng, and W. Zhou, "Spectral clustering of high-dimensional data exploiting sparse representation vectors," Neurocomputing, vol. 135, pp. 229-239, 2014.
47. S. Still and W. Bialek, "How many clusters? An information-theoretic perspective," Neural Comput., vol. 16, no. 12, pp. 2483-2506, 2004.
48. R. Tibshirani, G. Walther, and T. Hastie, "Estimating the number of clusters in a data set via the gap statistic," J. Roy. Stat. Soc. B, vol. 63, no. 2, pp. 411-423, 2001.
49. M. Honarkhah and J. Caers, "Classifying existing and generating new training image patterns in kernel space," in Proc. 21st Stanford Center Reservoir Forecast. Aliate Meeting (SCRF), Stanford University, CA, USA, 2008, pp. 1-38.
50. N. Bassiou, V. Moschou, and C. Kotropoulos, "Speaker diarization exploiting the eigengap criterion and cluster ensembles," IEEE Trans. Audio Speech Lang. Process., vol. 18, no. 8, pp. 2134-2144, Nov. 2010.
51. R. Patil and K. Jondhale, "Edge based technique to estimate number of clusters in k-means color image segmentation," in Proc. 3rd IEEE Int. Conf. Comput. Sci. Inf. Technol. (ICCSIT), Beijing, China, Jul. 9-11, 2010, pp. 117-121.
52. M. Honarkhah and J. Caers, "Stochastic simulation of patterns using distance-based pattern modeling," Math. Geosci., vol. 42, no. 5, pp. 487-517, 2010.
53. 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, Dec. 2009.
54. C.-I. Chang et al., "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.
55. T. Cover and P. Hart, "Nearest neighbor pattern classification," IEEE Trans. Inf. Theory, vol. 13, no. 1, pp. 21-27, Jan. 1967.
56. I. Steinwart and A. Christmann, Support Vector Machines. Berlin, Germany: Springer-Verlag, 2008.
57. R. Rubinstein, M. Zibulevsky, and M. Elad, "Efficient implementation of the K-SVD algorithm using batch orthogonal matching pursuit," CS Technion, vol. 40, pp. 1-15, 2008.