Spectral-Spatial Feature Learning Using Cluster-Based Group Sparse Coding for Hyperspectral Image Classification

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

Volumn 9, Issue 9, 2016, Pages 4142-4159

  Zhang, Xiangrong ^a   Song, Qiang ^a   Gao, Zeyu ^a   Zheng, Yaoguo ^a   Weng, Peng ^a   Jiao, L C ^a  

^a XIDIAN UNIVERSITY   (China)

Author keywords

Group sparse coding (GSC); hyperspectral image segmentation; kernel group sparse coding; mean shift (MS); spectral spatial classification

Indexed keywords

CLASSIFICATION (OF INFORMATION); CODES (SYMBOLS); IMAGE CODING; LEARNING SYSTEMS; PIXELS; SPECTROSCOPY;

CLASSIFICATION ACCURACY; HYPERSPECTRAL IMAGE CLASSIFICATION; MEAN SHIFT ALGORITHM; NON-LINEAR RELATIONSHIPS; SPARSE REPRESENTATION; SPATIAL CLASSIFICATION; SPATIAL INFORMATIONS; SPATIAL NEIGHBORHOODS;

IMAGE CLASSIFICATION;

ALGORITHM; IMAGE CLASSIFICATION; MULTISPECTRAL IMAGE; PIXEL; SEGMENTATION; SPECTRAL ANALYSIS;

EID: 84983086503     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2016.2593907     Document Type: Article

References (63)

1. R. O. Green et al., "Imaging spectroscopy and the airborne visible/infrared imaging spectrometer (AVIRIS)," Remote Sens. Environ., vol. 65, no. 3, pp. 227-248, Sep. 1998.
2. P. Gamba, "A collection of data for urban area characterization," in Proc. IEEE Int. Geosci. Remote Sens. Symp., Sep. 2004, vol. 1, pp. 69-72.
3. J. Pearlman et al., "Development and operations of the EO-1 Hyperion imaging spectrometer," Proc. SPIE, vol. 4135, pp. 243-253, 2000.
4. K. Staenz, "Terrestrial imaging spectroscopy - Some future perspectives," in Proc. 6th EARSEL SIG IS Workshop, Tel Aviv, Israel, 2009, pp. 1-12.
5. N. K. Patel, C. Patnaik, S. Dutta, A. M. Shekh, and A. J. Dave, "Study of crop growth parameters using airborne imaging spectrometer data," Int. J. Remote Sens., vol. 22, no. 12, pp. 2401-2411, Aug. 2001.
6. M. De Martinao, F. Causa, and S. B. Serpico, "Classification of optical high resolution images in urban environment using spectral and textural information," in Proc. IEEE Int. Geosci. Remote Sens. Symp., Jul. 2003, vol. 1, pp. 467-469.
7. M. T. Eismann, A. D. Stocker, and N. M. Nasrabadi, "Automated hyperspectral cueing for civilian search and rescue," Proc. IEEE, vol. 97, no. 6, pp. 1031-1055, Jun. 2009.
8. N. Keshava and J. Mustard, "Spectral unmixing," IEEE Signal Process. Mag., vol. 19, no. 1, pp. 44-57, Jan. 2002.
9. Y. Tarabalka, J. Chanussot, and J. Benediktsson, "Segmentation and classification of hyperspectral images using watershed transformation," Pattern Recognit., vol. 43, no. 7, pp. 2367-2379, Jul. 2010.
10. J. A. Gualtieri and R. F. Cromp, "Support vector machines for hyperspectral remote sensing classification," Proc. SPIE, vol. 3584, pp. 221-232, Jan. 1998.
11. G. Hughes, "On the mean accuracy of statistical pattern recognizers," IEEE Trans. Inf. Theory, vol. IT-14, no. 1, pp. 55-63, Jan. 1968.
12. C. H. Chen and P. G. P. Ho, "Statistical pattern recognition in remote sensing," Pattern Recognit., vol. 41, no. 9, pp. 2731-2741, Sep. 2008.
13. C. Hernández-Espinosa, M. Fernández-Redondo, and J. Torres-Sospedra, "Some experiments with ensembles of neural networks for classification of hyperspectral images," in Proc. Int. Symp. Neural Netw., 2004, vol. 1, pp. 912-917.
14. P. K. Goel, S. O. Prasher, R. M. Patel, J. A. Landry, R. B. Bonnell, and A. A. Viau, "Classification of hyperspectral data by decision trees and artificial neural networks to identify weed stress and nitrogen status of corn," Comput. Electron. Agric., vol. 39, no. 2, pp. 67-93, 2003.
15. S. Li, H. Wu, D. Wan, and J. Zhu, "An effective feature selection method for hyperspectral image classification based on genetic algorithm and support vector machine," Knowl.-Based Syst., vol. 24, no. 1, pp. 40-48, Feb. 2011.
16. G. Camps-Valls and L. Bruzzone, "Kernel-based methods for hyperspectral image classification," IEEE Trans. Geosci. Remote Sens., vol. 43, no. 6, pp. 1351-1362, Jun. 2005.
17. B. Guo, S. R. Gunn, R. I. Damper, and J. D. B. Nelson, "Customizing kernel functions for SVM-based hyperspectral image classification," IEEE Trans. Image Process., vol. 17, no. 4, pp. 622-629, Apr. 2008.
18. 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.
19. F. Bovolo, L. Bruzzone, and M. Marconcini, "A novel context-sensitive SVM for classification of remote sensing images," in Proc. IEEE Int. Geosci. Remote Sens. Symp., Jul. 2006, pp. 2498-2501.
20. Y. Tarabalka, M. Fauvel, J. Chanussot, and J. A. Benediktsson, "SVMand MRF-based method for accurate classification of hyperspectral images," IEEE Geosci. Remote Sens. Lett., vol. 7, no. 4, pp. 736-740, Oct. 2010.
21. J. Li, J. Bioucas-Dias, and A. Plaza, "Spectral-spatial hyperspectral image segmentation using subspace multinomial logistic regression and Markov random fields," IEEE Trans. Geosci. Remote Sens., vol. 50, no. 3, pp. 809-823, Mar. 2012.
22. M. Fauvel, J. Chanussot, J. A. Benediktsson, and J. R. Sveinsson, "Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles," IEEE Trans. Geosci. Remote Sens., vol. 46, no. 10, pp. 3804-3814, Oct. 2008.
23. M. Fauvel, J. Chanussot, and J. A. Benediktsson, "A spatial-spectral kernel-based approach for the classification of remote-sensing images," Pattern Recognit., vol. 45, no. 1, pp. 381-392, 2012.
24. G. Camps-Valls, L. Gomez-Chova, J. Munoz-Mari, J. Vila-Frances, and J. Calpe-Maravilla, "Composite kernels for hyperspectral image classification," IEEE Geosci. Remote Sens. Lett., vol. 3, no. 1, pp. 93-97, Jan. 2006.
25. J. Bolton and P. Gader, "Random set framework for context-based classification with hyperspectral imagery," IEEE Trans. Geosci. Remote Sens., vol. 47, no. 11, pp. 3810-3821, Nov. 2009.
26. Z. He, Q. Wang, Y. Shen, and M. Sun, "Kernel sparse multitask learning for hyperspectral image classification with empirical mode decomposition and morphological wavelet-based features," IEEE Trans. Geosci. Remote Sens., vol. 52, no. 8, pp. 5150-5163, Aug. 2014.
27. S. Velasco-Forero and J. Angulo, "Classification of hyperspectral images by tensor modeling and additive morphological decomposition," Pattern Recognit., vol. 46, no. 2, pp. 566-577, 2013.
28. Y. Qian, M. Ye, and J. Zhou, "Hyperspectral image classification based on structured sparse logistic regression and three-dimensional wavelet texture features," IEEE Trans. Geosci. Remote Sens., vol. 51, no. 4, pp. 2276-2291, Apr. 2013.
29. 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.
30. T. Guha and R. Ward, "Learning sparse representations for human action recognition," IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 8, pp. 1576-1588, Aug. 2012.
31. M. Zhou et al., "Nonparametric Bayesian dictionary learning for analysis of noisy and incomplete images," IEEE Trans. Image Process., vol. 21, no. 1, pp. 130-144, Jan. 2012.
32. M. D. Iordache, J. Bioucas-Dias, and A. Plaza, "Sparse unmixing of hyperspectral data," IEEE Trans. Geosci. Remote Sens., vol. 49, no. 6, pp. 2014-2039, Jun. 2011.
33. Z. Guo, T. Wittman, and S. Osher, "L1 unmixing and its application to hyperspectral image enhancement," Proc. SPIE, vol. 1, pp. 1-12, 2009.
34. J. M. Bioucas-Dias et al., "Hyperspectral unmixing overview: Geometrical, statistical and sparse regression-based approaches," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 5, no. 2, pp. 354-379, Apr. 2012.
35. J. Greer, "Sparse demixing of hyperspectral images," IEEE Trans. Image Process., vol. 21, no. 1, pp. 219-228, Jan. 2012.
36. S. U. Haq, L. Tao, F. Sun, and S. Yang, "A fast and robust sparse approach for hyperspectral data classification using a few labeled samples," IEEE Trans. Geosci. Remote Sens., vol. 50, no. 6, pp. 2287-2302, Jun. 2012.
37. A. Castrodad, Z. Xing, J. Greer, E. Bosch, L. Carin, and G. Sapiro, "Learning discriminative sparse representations for modeling, source separation, and mapping of hyperspectral imagery," IEEE Trans. Geosci. Remote Sens., vol. 49, no. 11, pp. 4263-4281, Nov. 2011.
38. 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.
39. U. Srinivas, Y. Chen, V. Monga, N. M. Nasrabadi, and T.D. Tran, "Exploiting sparsity in hyperspectral image classification via graphical models," IEEE Geosci. Remote Sens. Lett., vol. 10, no. 3, pp. 505-509, May 2013.
40. Y. Chen, N. M. Nasrabadi, and T. D. Tran, "Hyperspectral image classification via kernel sparse representation," IEEE Trans. Geosci. Remote Sens., vol. 51, no. 1, pp. 217-231, Jan. 2013.
41. X. Zhang, P. Weng, J. Feng, E. Zhang, and B. Hou, "Spatial-spectral classification based on group sparse coding for hyperspectral image," in Proc. IEEE Int. Geosci. Remote Sens. Symp., 2013, pp. 1745-1748.
42. A. S. Charles, B. A. Olshausen, and C. J. Rozell, "Learning sparse codes for hyperspectral imagery," IEEE J. Sel. Topics Signal Process., vol. 5, no. 5, pp. 963-978, Sep. 2011.
43. Z. Xing, M. Zhou, A. Castrodad, G. Sapiro, and L. Carin, "Dictionary learning for noisy and incomplete hyperspectral images," SIAM J. Imag. Sci., vol. 5, no. 1, pp. 33-56, 2012.
44. J. Li, H. Zhang, L. Zhang, X. Huang, and L. Zhang, "Joint collaborative representation with multi-task learning for hyperspectral image classification," IEEE Trans. Geosci. Remote Sens., vol. 52, no. 9, pp. 5923-5936, Jun. 2013, doi:10.1109/TGRS.2013.2293732.
45. J. Li, H. Zhang, Y. Huang, and L. Zhang, "Hyperspectral image classification by nonlocal joint collaborative representation with a locally adaptive dictionary," IEEE Trans. Geosci. Remote Sens., vol. 52, no. 6, pp. 3707-3719, Jun. 2014.
46. H. Zhang, J. Li, Y. Huang, and L. 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, doi:10.1109/JS-TARS.2013.2264720.
47. 1 spatial filtering," IEEE Geosci. Remote Sens. Lett., vol. 11, no. 3, pp. 602-606, Mar. 2014.
48. L. Fang, S. Li, X. Kang, and J. A. Benediktsson, "Spectral-spatial classification of hyperspectral images with a superpixel-based discriminative sparse model," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 8, pp. 4186-4201, Aug. 2015.
49. Y. Tarabalka, J. A. Benediktsson, and J. Chanussot, "Spectral-spatial classification of hyperspectral imagery based on partitional clustering techniques," IEEE Trans. Geosci. Remote Sens., vol. 47, no. 8, pp. 2973-2987, Aug. 2009.
50. Y. Tarabalka, J. Chanussot, and J. A. Benediktsson, "Segmentation and classification of hyperspectral images using minimum spanning forest grown from automatically selected markers," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 40, no. 5, pp. 1267-1279, Oct. 2010.
51. X. Huang and L. Zhang, "An adaptive mean-shift analysis approach for object extraction and classification from urban hyperspectral imagery," IEEE Trans. Geosci. Remote Sens., vol. 46, no. 12, pp. 4173-4185, Dec. 2008.
52. D. Comaniciu and P. Meer, "Mean shift: A robust approach toward feature space analysis," IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 5, pp. 603-619, May 2002.
53. X. Ren and J. Malik, "Learning a classification model for segmentation," in Proc. 9th IEEE Int. Conf. Comput. Vis., 2003, pp. 10-17.
54. S. Bengio, F. Pereira, and Y. Singer, "Group sparse coding," in Proc. Adv. Neural Inf. Process. Syst., 2009, pp. 82-89.
55. L. Bo and C. Sminchisescu, "Efficient match kernels between sets of features for visual recognition," in Proc. 23rd Adv. Neural Inf. Process. Syst., 2009, pp. 135-143.
56. A. Hyvarinen, "The fixed-point algorithm and maximum likelihood estimation for independent component analysis," Neural Process. Lett., vol. 10, no. 1, pp. 1-5, Aug. 1999.
57. AVIRIS NW Indiana's Indian Pines 1992 Data Set. [Online]. Available: https://engineering.purdue.edu/-biehl/MultiSpec/hyperspectral.html
58. J. A. Richards and X. Jia, Remote Sensing Digital Image Analysis: An Introduction, 4th ed. New York, NY, USA: Springer-Verlag, 2006.
59. H. Lee, A. Battle, R. Raina, and A. Y. Ng, "Efficient sparse coding algorithms," in Proc. Adv. Neural Inf. Process. Syst., 2007, pp. 801-808.
60. A. Plaza et al., "Recent advances in techniques for hyperspectral image processing," Remote Sens. Environ., vol. 113, no. Supplement 1, pp. S110-S122, Sep. 2009.
61. R. Roscher and B. Waske, "Superpixel-based classification of hyperspectral data using sparse representation and conditional random fields," in Proc. IEEE Int. Geosci. Remote Sens. Symp., 2014, pp. 3674-3677.
62. J. Feng, Z. Cao, and Y. Pi, "Polarimetric contextual classification of Pol-SAR images using sparse representation and superpixels," Remote Sens., 2014, vol. 6, no. 8, pp. 7158-7181.
63. K. Kavukcuoglu et al., "Learning invariant features through topographic filter maps," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2009, pp. 1605-1612.