Dimensionality Reduction by Spatial-Spectral Preservation in Selected Bands

IEEE Transactions on Geoscience and Remote Sensing

Volumn 55, Issue 9, 2017, Pages 5185-5197

  Zheng, Xiangtao ^a   Yuan, Yuan ^a   Lu, Xiaoqiang ^a  

^a XI AN INSTITUTE OF OPTICS AND PRECISION MECHANICS   (China)

Author keywords

Band selection; determinantal point process (DPP); dimensionality reduction (DR); multiple Laplacian eigenmaps (MLEs)

Indexed keywords

LAPLACE TRANSFORMS; SPECTROSCOPY;

BAND SELECTION; CLASSIFICATION ACCURACY; COMPUTATIONAL BURDEN; DIMENSIONALITY REDUCTION; LAPLACIAN EIGENMAPS; LOW-DIMENSIONAL REPRESENTATION; POINT PROCESS; STATE-OF-THE-ART METHODS;

CLASSIFICATION (OF INFORMATION);

ACCURACY ASSESSMENT; COMPUTER SIMULATION; DISCRIMINANT ANALYSIS; EIGENVALUE; EXPERIMENTAL STUDY; IMAGE CLASSIFICATION; MULTISPECTRAL IMAGE; PERFORMANCE ASSESSMENT;

EID: 85021807370     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2017.2703598     Document Type: Article

References (60)

1. W. Sun, L. Zhang, B. Du, W. Li, and Y. M. Lai, "Band selection using improved sparse subspace clustering for hyperspectral imagery classification," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 8, no. 6, pp. 2784-2797, Jun. 2015.
2. W. Li, Q. Du, F. Zhang, and W. Hu, "Collaborative-representation-based nearest neighbor classifier for hyperspectral imagery," IEEE Geosci. Remote Sens. Lett., vol. 12, no. 2, pp. 389-393, Feb. 2015.
3. C. Wu, L. Zhang, and B. Du, "Hyperspectral anomaly change detection with slow feature analysis," Neurocomputing, vol. 151, pp. 175-187, Mar. 2015.
4. Y. Yuan, M. Fu, and X. Lu, "Substance dependence constrained sparse NMF for hyperspectral unmixing," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 6, pp. 2975-2986, Jun. 2015.
5. W. Li, Q. Ruan, and J. Wan, "Dimensionality reduction using graphembedded probability-based semi-supervised discriminant analysis," Neurocomputing, vol. 138, pp. 283-296, Aug. 2014.
6. W. Li, C. Chen, H. Su, and Q. Du, "Local binary patterns and extreme learning machine for hyperspectral imagery classification," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 7, pp. 3681-3693, Jul. 2015.
7. K. Sun, X. Geng, and L. Ji, "A new sparsity-based band selection method for target detection of hyperspectral image," IEEE Geosci. Remote Sens. Lett., vol. 12, no. 2, pp. 329-333, Feb. 2015.
8. J. M. Bioucas-Dias, A. Plaza, G. Camps-Valls, P. Scheunders, N. M. Nasrabadi, and J. Chanussot, "Hyperspectral remote sensing data analysis and future challenges," IEEE Geosci. Remote Sens. Mag., vol. 1, no. 2, pp. 6-36, Jun. 2013.
9. G. Licciardi, P. R. Marpu, J. Chanussot, and J. A. Benediktsson, "Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles," IEEE Geosci. Remote Sens. Lett., vol. 9, no. 3, pp. 447-451, May 2012.
10. D. H. Kim and L. H. Finkel, "Hyperspectral image processing using locally linear embedding," in Proc. 1st Int. IEEE EMBS Conf. Neural Eng., Mar. 2003, pp. 316-319.
11. 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.
12. T. V. Bandos, L. Bruzzone, and G. Camps-Valls, "Classification of hyperspectral images with regularized linear discriminant analysis," IEEE Trans. Geosci. Remote Sens., vol. 47, no. 3, pp. 862-873, Mar. 2009.
13. M. Belkin and P. Niyogi, "Laplacian eigenmaps and spectral techniques for embedding and clustering," in Proc. Adv. Neural Inf. Process. Syst., 2001, pp. 585-591.
14. N. D. Cahill, W. Czaja, and D. W. Messinger, "Schroedinger eigenmaps with nondiagonal potentials for spatial-spectral clustering of hyperspectral imagery," Proc. SPIE, vol. 9088, art. no. 908804, Jun. 2014.
15. L. Wang, X. Jia, and Y. Zhang, "A novel geometry-based featureselection technique for hyperspectral imagery," IEEE Trans. Geosci. Remote Sens., vol. 4, no. 1, pp. 171-175, Jan. 2007.
16. P. Mitra, C. A. Murthy, and S. K. Pal, "Unsupervised feature selection using feature similarity," IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 3, pp. 301-312, Mar. 2002.
17. 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.
18. M. L. Raymer, W. F. Punch, E. D. Goodman, L. A. Kuhn, and A. K. Jain, "Dimensionality reduction using genetic algorithms," IEEE Trans. Evol. Comput., vol. 4, no. 2, pp. 164-171, Jul. 2000.
19. L. Zhang, Y. Zhong, B. Huang, J. Gong, and P. Li, "Dimensionality reduction based on clonal selection for hyperspectral imagery," IEEE Trans. Geosci. Remote Sens., vol. 45, no. 12, pp. 4172-4186, Dec. 2007.
20. Q. Zhang, Y. Tian, Y. Yang, and C. Pan, "Automatic spatial-spectral feature selection for hyperspectral image via discriminative sparse multimodal learning," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 1, pp. 261-279, Jan. 2015.
21. 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.
22. K. Tan, E. Li, Q. Du, and P. Du, "Hyperspectral image classification using band selection and morphological profile," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 7, no. 1, pp. 40-48, Jan. 2014.
23. B. Hou, X. Zhang, Q. Ye, and Y. Zheng, "A novel method for hyperspectral image classification based on laplacian eigenmap pixels distributionflow," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 6, no. 3, pp. 1602-1618, Jun. 2013.
24. X. Lu, H. Wu, Y. Yuan, P. Yan, and X. Li, "Manifold regularized sparse NMF for hyperspectral unmixing," IEEE Trans. Geosci. Remote Sens., vol. 51, no. 5, pp. 2815-2826, May 2013.
25. W. Liao, A. Pizurica, R. Bellens, S. Gautama, and W. Philips, "Generalized graph-based fusion of hyperspectral and LiDAR data using morphological features," IEEE Geosci. Remote Sens. Lett., vol. 12, no. 3, pp. 552-556, Mar. 2015.
26. C. Debes et al., "Hyperspectral and LiDAR data fusion: Outcome of the 2013 GRSS data fusion contest," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 7, no. 6, pp. 2405-2418, Jun. 2014.
27. A. Kulesza and B. Taskar, "Structured determinantal point processes," in Proc. Adv. Neural Inf. Process. Syst., vol. 23. 2010, pp. 1171-1179.
28. A. Kulesza, "Learning with determinantal point processes," Ph.D. dissertation, Dept. Comput. Inf. Sci., Univ. Pennsylvania, Philadelphia, PA, USA, 2012.
29. J. Gillenwater, A. Kulesza, and B. Taskar, "Discovering diverse and salient threads in document collections," in Proc. Conf. Empirical Methods Natural Lang. Process., 2012, pp. 710-720.
30. B. Gong, W.-L. Chao, K. Grauman, and F. Sha, "Diverse sequential subset selection for supervised video summarization," in Proc. Adv. Neural Inf. Process. Syst., 2014, pp. 2069-2077.
31. A. Kulesza and B. Taskar, "Determinantal point processes for machine learning," Found. Trends Mach. Learn., vol. 5, nos. 2-3, pp. 123-286, 2012.
32. M. Fauvel, C. Dechesne, A. Zullo, and F. Ferraty. (Jan. 2015). "Fast forward feature selection for the nonlinear classification of hyperspectral images." [Online]. Available: https://arxiv.org/abs/1501.00857
33. G. Chen and S.-E. Qian, "Dimensionality reduction of hyperspectral imagery using improved locally linear embedding," J. Appl. Remote Sens., vol. 1, no. 1, p. 013509, 2007.
34. E. Aptoula, "Hyperspectral image classification with multidimensional attribute profiles," IEEE Geosci. Remote Sens. Lett., vol. 12, no. 10, pp. 2031-2035, Oct. 2015.
35. C. M. Bachmann, T. L. Ainsworth, and R. A. Fusina, "Improved manifold coordinate representations of large-scale hyperspectral scenes," IEEE Trans. Geosci. Remote Sens., vol. 44, no. 10, pp. 2786-2803, Oct. 2006.
36. D. Lunga, S. Prasad, M. M. Crawford, and O. Ersoy, "Manifold-learningbased feature extraction for classification of hyperspectral data: A review of advances in manifold learning," IEEE Signal Process. Mag., vol. 31, no. 1, pp. 55-66, Jan. 2014.
37. R. Luo and Y. Pi, "GPU-based parallel kernel PCA feature extraction for hyperspectral images," in Proc. Int. Conf. Remote Sens. Wireless Commun. (RSWC), 2014, pp. 140-145.
38. L. D. Plessis, R. Xu, S. Damelin, M. Sears, and D. C. Wunsch, "Reducing dimensionality of hyperspectral data with diffusion maps and clustering with k-means and fuzzy art," Int. J. Syst., Control Commun., vol. 3, no. 3, pp. 232-251, 2011.
39. S. T. Tu, J. Y. Chen, W. Yang, and H. Sun, "Laplacian eigenmapsbased polarimetric dimensionality reduction for SAR image classification," IEEE Trans. Geosci. Remote Sens., vol. 50, no. 1, pp. 170-179, Jan. 2012.
40. S. B. Kim and P. Rattakorn, "Unsupervised feature selection using weighted principal components," Expert Syst. Appl., vol. 38, no. 5, pp. 5704-5710, May 2011.
41. H. Peng, F. Long, and C. Ding, "Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and minredundancy," IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 8, pp. 1226-1238, Aug. 2005.
42. 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.
43. L. Shen, Z. Zhu, S. Jia, J. Zhu, and Y. Sun, "Discriminative Gabor feature selection for hyperspectral image classification," IEEE Geosci. Remote Sens. Lett., vol. 10, no. 1, pp. 29-33, Jan. 2013.
44. A. Ifarraguerri and M. W. Prairie, "Visual method for spectral band selection," IEEE Geosci. Remote Sens. Lett., vol. 1, no. 2, pp. 101-106, Apr. 2004.
45. R. Huang and M. He, "Band selection based on feature weighting for classification of hyperspectral data," IEEE Geosci. Remote Sens. Lett., vol. 2, no. 2, pp. 156-159, Apr. 2005.
46. P. Ghamisi and J. A. Benediktsson, "Feature selection based on hybridization of genetic algorithm and particle swarm optimization," IEEE Geosci. Remote Sens. Lett., vol. 12, no. 2, pp. 309-313, Feb. 2015.
47. 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.
48. J. M. Sotoca, F. Pla, and J. S. Sánchez, "Band selection in multispectral images by minimization of dependent information," IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 37, no. 2, pp. 258-267, Mar. 2007.
49. C.-I. Chang, Q. Du, T.-L. Sun, and M. L. G. 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.
50. K. Z. Mao, "Orthogonal forward selection and backward elimination algorithms for feature subset selection," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 34, no. 1, pp. 629-634, Feb. 2004.
51. Y. Yuan, G. Zhu, and Q. Wang, "Hyperspectral band selection by multitask sparsity pursuit," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 2, pp. 631-644, Feb. 2015.
52. H. Su, Q. Du, G. Chen, and P. Du, "Optimized hyperspectral band selection using particle swarm optimization," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 7, no. 6, pp. 2659-2670, Jun. 2014.
53. J. Gillenwater, A. Kulesza, and B. Taskar, "Near-optimal MAP inference for determinantal point processes," in Proc. Adv. Neural Inf. Process. Syst., 2012, pp. 2735-2743.
54. M. Fauvel, Y. Tarabalka, J. A. Benediktsson, J. Chanussot, and J. C. Tilton, "Advances in spectral-spatial classification of hyperspectral images," Proc. IEEE, vol. 101, no. 3, pp. 652-675, Mar. 2013.
55. D. Eynard, A. Kovnatsky, M. M. Bronstein, K. Glashoff, and A. M. Bronstein, "Multimodal manifold analysis by simultaneous diagonalization of laplacians," IEEE Trans. Pattern Anal. Mach. Intell., vol. 37, no. 12, pp. 2505-2517, Dec. 2015.
56. P. R. Marpu, M. Pedergnana, M. D. Mura, J. A. Benediktsson, and L. Bruzzone, "Automatic generation of standard deviation attribute profiles for spectral-spatial classification of remote sensing data," IEEE Geosci. Remote Sens. Lett., vol. 10, no. 2, pp. 293-297, Mar. 2013.
57. J. Li et al., "Multiple feature learning for hyperspectral image classification," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 3, pp. 1592-1606, Mar. 2015.
58. F. Melgani and L. Bruzzone, "Classification of hyperspectral remote sensing images with support vector machines," IEEE Trans. Geosci. Remote Sens., vol. 42, no. 8, pp. 1778-1790, Aug. 2004.
59. Y. Yuan, X. Zheng, and X. Lu, "Discovering diverse subset for unsupervised hyperspectral band selection," IEEE Trans. Image Process., vol. 26, no. 1, pp. 51-64, Jan. 2017.
60. J. Bai, S. Xiang, and C. Pan, "A graph-based classification method for hyperspectral images," IEEE Trans. Geosci. Remote Sens., vol. 51, no. 2, pp. 803-817, Feb. 2013.