Simultaneous sparse graph embedding for hyperspectral image classification

IEEE Transactions on Geoscience and Remote Sensing

Volumn 53, Issue 11, 2015, Pages 6114-6133

  Xue, Zhaohui ^a   Du, Peijun ^a   Li, Jun ^b   Su, Hongjun ^c  

^a NANJING UNIVERSITY   (China)

^b SUN YAT SEN UNIVERSITY   (China)

^c HOHAI UNIVERSITY   (China)

Author keywords

Classification; hyperspectral image (HSI); linear graph embedding (LGE); multimanifold learning; simultaneous sparse representation (SSR); sparse graph embedding (SGE)

Indexed keywords

CLASSIFICATION (OF INFORMATION); DATA STRUCTURES; FEATURE EXTRACTION; GRAPH THEORY; INDEPENDENT COMPONENT ANALYSIS; MATRIX ALGEBRA; SPECTROSCOPY;

HYPERSPECTRAL IMAGES; LINEAR GRAPH; MULTI-MANIFOLD LEARNING; SPARSE GRAPHS; SPARSE REPRESENTATION;

IMAGE CLASSIFICATION;

ALGORITHM; DATA SET; EXPERIMENTAL STUDY; GRAPHICAL METHOD; IMAGE CLASSIFICATION; MACHINE LEARNING; MODEL VALIDATION; PERFORMANCE ASSESSMENT; SPATIAL VARIATION; SPECTRAL ANALYSIS;

EID: 85027931104     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2015.2432059     Document Type: Article

References (46)

1. A. Plaza et al., "Recent advances in techniques for hyperspectral image processing," Remote Sens. Environ., vol. 113, pp. S110-S122, Sep. 2009.
2. J. Bioucas-Dias et al., "Hyperspectral remote sensing data analysis and future challenges," IEEE Geosci. Remote Sens. Mag., vol. 1, no. 2, pp. 6-36, Jun. 2013.
3. Q. X. Tong, Y. Q. Xue, and L. F. Zhang, "Progress in hyperspectral remote sensing science and technology in china over the past three decades," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 7, no. 1, pp. 70-91, Jan. 2014.
4. G. Camps-Valls, D. Tuia, L. Bruzzone, and J. A. Benediktsson, "Advances in hyperspectral image classification," IEEE Signal Process. Mag., vol. 31, no. 1, pp. 45-54, Jan. 2014.
5. D. A. Landgrebe, Signal Theory Methods in Multispectral Remote Sensing. New York, NY, USA: Wiley, 2003.
6. Q. Jackson and D. A. Landgrebe, "An adaptive method for combined covariance estimation and classification," IEEE Trans. Geosci. Remote Sens., vol. 40, no. 5, pp. 1082-1087, May 2002.
7. X. P. Jia, B. C. Kuo, and M. M. Crawford, "Feature mining for hyper-spectral image classification," Proc. IEEE, vol. 101, no. 3, pp. 676-697, Mar. 2013.
8. D. Lunga, S. Prasad, M. M. Crawford, and O. Ersoy, "Manifold-learning-based feature extraction for classification of hyperspectral data," IEEE Signal Process. Mag., vol. 31, no. 1, pp. 55-66, Jan. 2014.
9. S. T. Roweis and L. K. Saul, "Nonlinear dimensionality reduction by locally linear embedding," Science, vol. 290, no. 5500, pp. 2323-2326, Dec. 22, 2000.
10. X. He and P. Niyogi, "Locality preserving projections," in Proc. Adv. Neural Inf. Process. Syst., Vancouver, BC, Canada, 2003, vol. 16, pp. 234-241.
11. T. V. Bandos, L. Bruzzone, and G. Camps-Valls, "Classification of hy-perspectral images with regularized linear discriminant analysis," IEEE Trans. Geosci. Remote Sens., vol. 47, no. 3, pp. 862-873, Mar. 2009.
12. M. Sugiyama, "Dimensionality reduction of multimodal labeled data by local fisher discriminant analysis," J. Mach. Learn. Res., vol. 8, pp. 1027-1061, May 2007.
13. Y. L. Chang, J. N. Liu, C. C. Han, and Y. N. Chen, "Hyperspectral image classification using nearest feature line embedding approach," IEEE Trans. Geosci. Remote Sens., vol. 52, no. 1, pp. 278-287, Jan. 2014.
14. L. P. Zhang, L. F. Zhang, D. C. Tao, and X. Huang, "Tensor discriminative locality alignment for hyperspectral image spectral-spatial feature extraction," IEEE Trans. Geosci. Remote Sens., vol. 51, no. 1, pp. 242-256, Jan. 2013.
15. I. Jolliffe, Principal Component Analysis. New York, NY, USA: Wiley, 2005.
16. X. F. He, S. C. Yan, Y. X. Hu, P. Niyogi, and H. J. Zhang, "Face recognition using Laplacianfaces," IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 3, pp. 328-340, Mar. 2005.
17. J. B. Tenenbaum, V. de Silva, and J. C. Langford, "A global geometric framework for nonlinear dimensionality reduction," Science, vol. 290, no. 5500, pp. 2319-2323, Dec. 2000.
18. Q. Shi, L. P. Zhang, and B. Du, "Semisupervised discriminative locally enhanced alignment for hyperspectral image classification," IEEE Trans. Geosci. Remote Sens., vol. 51, no. 9, pp. 4800-4815, Sep. 2013.
19. D. Q. Zhang, Z. H. Zhou, and S. C. Chen, "Semi-supervised dimensionality reduction," in Proc. 7th SIAM Int. Conf. Data Mining, 2007, pp. 629-634.
20. S. C. Yan et al., "Graph embedding and extensions: A general framework for dimensionality reduction," IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 1, pp. 40-51, Jan. 2007.
21. G. Camps-Valls and L. Bruzzone, "Kernel-based methods for hyperspec-tral image classification," IEEE Trans. Geosci. Remote Sens., vol. 43, no. 6, pp. 1351-1362, Jun. 2005.
22. J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, "Robust face recognition via sparse representation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 2, pp. 210-227, Feb. 2009.
23. 1-graph for image analysis," IEEE Trans. Image Process., vol. 19, no. 4, pp. 858-866, Apr. 2010.
24. J. Wright et al., "Sparse representation for computer vision and pattern recognition," Proc. IEEE, vol. 98, no. 6, pp. 1031-1044, Jun. 2010.
25. L. S. Qiao, S. C. Chen, and X. Y Tan, "Sparsity preserving projections with applications to face recognition," Pattern Recog., vol. 43, no. 1, pp. 331-341, Jan. 2010.
26. H. Huang, "Classification of hyperspectral remote-sensing images based on sparse manifold learning," J. Appl. Remote Sens., vol. 7, no. 1, pp. 073464-073464, Nov. 2013.
27. 1-graph for hyperspectral remote-sensing image classification," Int. J. Remote Sens., vol. 36, no. 5, pp. 1307-1328, Mar. 2015.
28. N. H. Ly, Q. Du, and J. E. Fowler, "Sparse graph-based discriminant analysis for hyperspectral imagery," IEEE Trans. Geosci. Remote Sens., vol. 52, no. 7, pp. 3872-3884, Jul. 2014.
29. J. A. Lee and M. Verleysen, "Quality assessment of dimensionality reduction: Rank-based criteria," Neurocomputing, vol. 72, no. 7-9, pp. 1431-1443, Mar. 2009.
30. J. A. Tropp, A. C. Gilbert, and M. J. Strauss, "Algorithms for simultaneous sparse approximation. Part I: Greedy pursuit," Signal Process., vol. 86, no. 3, pp. 572-588, Mar. 2006.
31. 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.
32. H. Y Zhang, J. Y Li, Y C. Huang, and L. P. Zhang, "A nonlocal weighted joint sparse representation classification method for hyperspectral imagery," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 7, no. 6, pp. 2056-2065, Jun. 2014.
33. D. Lunga and O. Ersoy, "Spherical stochastic neighbor embedding of hyperspectral data," IEEE Trans. Geosci. Remote Sens., vol. 51, no. 2, pp. 857-871, Feb. 2013.
34. D. Lunga and O. Ersoy, "Multidimensional artificial field embedding with spatial sensitivity," IEEE Trans. Geosci. Remote Sens., vol. 52, no. 2, pp. 1518-1532, Feb. 2014.
35. C. M. Bachmann, T. L. Ainsworth, and R. A. Fusina, "Exploiting manifold geometry in hyperspectral imagery," IEEE Trans. Geosci. Remote Sens., vol. 43, no. 3, pp. 441-454, Mar. 2005.
36. X. W. Dong, P. Frossard, P. Vandergheynst, and N. Nefedov, "Clustering on multi-layer graphs via subspace analysis on Grassmann manifolds," IEEE Trans. Signal Process., vol. 62, no. 4, pp. 905-918, Feb. 2014.
37. H. Hong-Bing, H. Hong, and F. Tao, "Hierarchical manifold learning with applications to supervised classification for high-resolution remotely sensed images," IEEE Trans. Geosci. Remote Sens., vol. 52, no. 3, pp. 1677-1692, Mar. 2014.
38. D. Eynard, A. Kovnatsky, M. Bronstein, K. Glashoff, and A. Bronstein, "Multimodal manifold analysis by simultaneous diagonalization of Laplacians," IEEE Trans. Pattern Anal. Mach. Intell., 2015, to be published. DOI: 10.1109/TPAMI.2015.2408348.
39. A. Foi, V. Katkovnik, and K. Egiazarian, "Pointwise shape-adaptive DCT for high-quality denoising and deblocking of grayscale and color images," IEEE Trans. Image Process., vol. 16, no. 5, pp. 1395-1411, May 2007.
40. J. Li, J. M. Bioucas-Dias, and A. Plaza, "Hyperspectral image segmentation using a new Bayesian approach with active learning," IEEE Trans. Geosci. Remote Sens., vol. 49, no. 10, pp. 3947-3960, Oct. 2011.
41. Y Y Tang, H. Yuan, and L. Li, "Manifold-based sparse representation for hyperspectral image classification," IEEE Trans. Geosci. Remote Sens., vol. 52, no. 12, pp. 7606-7618, Dec 2014.
42. W. Liu, J. Wang, and S. F Chang, "Robust and scalable graph-based semisupervised learning," Proc. IEEE, vol. 100, no. 9, pp. 2624-2638, Sep. 2012.
43. W Di and M. M. Crawford, "View generation for multiview maximum disagreement based active learning for hyperspectral image classification," IEEE Trans. Geosci. Remote Sens., vol. 50, no. 5, pp. 1942-1954, May 2012.
44. J. Hamm and D. D. Lee, "Grassmann discriminant analysis: A unifying view on subspace-based learning," in Proc. 25th ICML, New York, NY, USA, 2008, pp. 376-383.
45. J. A. Tropp and A. C. Gilbert, "Signal recovery from random measurements via orthogonal matching pursuit," IEEE Trans. Inf. Theory, vol. 53, no. 12, pp. 4655-4666, Dec. 2007.
46. A. Singer, "From graph to manifold Laplacian: The convergence rate," Appl. Comput. Harmonic Anal., vol. 21, no. 1, pp. 128-134, Jul. 2006.