Classification of Hyperspectral Images by Exploiting Spectral-Spatial Information of Superpixel via Multiple Kernels

IEEE Transactions on Geoscience and Remote Sensing

Volumn 53, Issue 12, 2015, Pages 6663-6674

  Fang, Leyuan ^a   Li, Shutao ^a   Duan, Wuhui ^a   Ren, Jinchang ^b   Benediktsson, Jon Atli ^c  

^a HUNAN UNIVERSITY   (China)

^b UNIVERSITY OF STRATHCLYDE   (United Kingdom)

^c UNIVERSITY OF ICELAND   (Iceland)

Author keywords

Hyperspectral image (HSI); multiple kernels; spectral spatial image classification; superpixel; support vector machines (SVMs)

Indexed keywords

CLASSIFICATION (OF INFORMATION); INDEPENDENT COMPONENT ANALYSIS; PIXELS; SPECTROSCOPY; SUPERPIXELS; SUPPORT VECTOR MACHINES;

CLASSIFICATION METHODS; MULTIPLE KERNELS; OVER SEGMENTATION; SPATIAL INFORMATIONS; SPECTRAL CHARACTERISTICS; SPECTRAL INFORMATION; SUPPORT VECTOR MACHINE (SVMS); SUPPORT VECTOR MACHINE CLASSIFIERS;

IMAGE CLASSIFICATION;

ALGORITHM; IMAGE ANALYSIS; IMAGE CLASSIFICATION; MULTISPECTRAL IMAGE; SEGMENTATION; SPATIAL ANALYSIS; SPATIAL DATA; VECTOR;

EID: 85027924217     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2015.2445767     Document Type: Article

References (51)

1. 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.
2. L. Zhang, L. Zhang, D. Tao, and X. Huang, "Sparse transfer manifold embedding for hyperspectral target detection," IEEE Trans. Geosci. Remote Sens., vol. 52, no. 2, pp. 1030-1043, Feb. 2014.
3. B. Du and L. Zhang, "Random-selection-based anomaly detector for hyperspectral imagery," IEEE Trans. Geosci. Remote Sens., vol. 49, no. 5, pp. 1578-1589, May 2011.
4. 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.
5. N. Younan, S. Aksoy, and R. King, "Foreword to the special issue on pattern recognition in remote sensing," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 5, no. 5, pp. 1331-1334, Oct. 2012.
6. V. Vapnik, The Nature of Statistical Learning Theory. Berlin, Germany: Springer-Verlag, 1995.
7. 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.
8. D. Böhning, "Multinomial logistic regression algorithm," Ann. Inst. Stat. Math., vol. 44, no. 1, pp. 197-200, Mar. 1992.
9. J. Li, J. M. 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.
10. J. Li, J. M. Bioucas-Dias, and A. Plaza, "Semisupervised hyperspectral image segmentation using multinomial logistic regression with active learning," IEEE Trans. Geosci. Remote Sens., vol. 48, no. 11, pp. 4085-4098, Nov. 2010.
11. B. Du and L. Zhang, "Target detection based on a dynamic subspace," Pattern Recog., vol. 47, no. 1, pp. 344-358, Jan. 2014.
12. 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.
13. 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.
14. 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.
15. L. Fang, S. Li, X. Kang, and J. A. Benediktsson, "Spectral-spatial hyperspectral image classification via multiscale adaptive sparse representation," IEEE Trans. Geosci. Remote Sens., vol. 52, no. 12, pp. 7738-7749, Dec. 2014.
16. 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.
17. 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.
18. S. Prasad and L. Mann Bruce, "Limitations of principal components analysis for hyperspectral target recognition," IEEE Geosci. Remote Sens. Lett., vol. 5, no. 4, pp. 625-629, Oct. 2008.
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. H. Li, Z. Ye, and G. Xiao, "Hyperspectral image classification using spectral-spatial composite kernels discriminant analysis," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., to be published.
21. Q. Shi, L. 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.
22. G. Camps-Valls and L. Bruzzone, Kernel Methods for Remote Sensing Data Analysis. New York, NY, USA: Wiley, 2009.
23. J. Li, I. Dópido, P. Gamba, and A. Plaza, "Complementarity of discriminative classifiers and spectral unmixing techniques for the interpretation of hyperspectral images," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 5, pp. 2899-2912 May 2015.
24. J. A. Benediktsson, J. A. Palmason, and J. R. Sveinsson, "Classification of hyperspectral data from urban areas based on extended morphological profiles," IEEE Trans. Geosci. Remote Sens., vol. 43, no. 3, pp. 480-491, Mar. 2005.
25. J. Li, P. R. Marpu, A. Plaza, J. M. Bioucas-Dias, and J. A. Benediktsson, "Generalized composite kernel framework for hyperspectral image classification," IEEE Trans. Geosci. Remote Sens., vol. 51, no. 9, pp. 4816-4829, Sep. 2013.
26. 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.
27. G. Camps-Valls, L. Gomez-Chova, J. Munõz-Marí, J. Vila-Francés, and J. Calpe-Maravilla, "Composite kernels for hyperspectral image classification," IEEE Geosci. Remote Sens. Lett., vol. 3, no. 1, pp. 93-97, Jan. 2006.
28. G. Mori, X. Ren, A. A. Efros, and J. Malik, "Recovering human body configurations: Combining segmentation and recognition," in Proc. IEEE Conf. Comput. Soc. Conf. Comput. Vis. Pattern Recog., 2004, pp. II-326-II-333.
29. R. Achanta et al., "SLIC superpixels compared to state-of-the-art superpixel methods," IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 11, pp. 2274-2282, Nov. 2012.
30. M.-Y. Liu, O. Tuzel, S. Ramalingam, and R. Chellappa, "Entropy rate superpixel segmentation," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2011, pp. 2097-2104.
31. G. Zhang, X. Jia, and N.M. Kwok, "Super pixel based remote sensing image classification with histogram descriptors on spectral and spatial data," in Proc. IEEE Int. Geosci. Remote Sens. Symp., 2012, pp. 4335-4338.
32. J. Liu, W. Yang, S. Tan, and Z. Wang, "Remote sensing image classification based on random projection super-pixel segmentation," in Proc. SPIE, 2013, Art ID. 89210T.
33. Z. Sun, C. Wang, H. Wang, and J. Li, "Learn multiple-kernel SVMs for domain adaptation in hyperspectral data," IEEE Geosci. Remote Sens. Lett., vol. 10, no. 5, pp. 1224-1228, Sep. 2013.
34. Y. Gu et al., "Representative multiple kernel learning for classification in hyperspectral imagery," IEEE Trans. Geosci. Remote Sens., vol. 50, no. 7, pp. 2852-2865, Jul. 2012.
35. V. N. Vapnik, Statistical Learning Theory. New York, NY, USA:Wiley, 1998, vol. 2.
36. I. Jolliffe, Principal Component Analysis. New York, NY, USA: Wiley, 2005.
37. R. C. Gonzalez and R. E.Woods, Digital Image Processing. Englewood Cliffs, NJ, USA: Prentice-Hall, 2009.
38. G. L. Nemhauser, L. A. Wolsey, and M. L. Fisher, "An analysis of approximations for maximizing submodular set functions," Math. Program., vol. 14, no. 1, pp. 265-294, Jan. 1978.
39. J. Liu, Z. Wu, Z. Wei, L. Xiao, and L. Sun, "Spatial-spectral kernel sparse representation for hyperspectral image classification," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 6, no. 6, pp. 2462-2471, Dec. 2013.
40. K. He, J. Sun, and X. Tang, "Guided image filtering," IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 6, pp. 1397-1409, Jun. 2013.
41. L. Fang et al., "Sparsity based denoising of spectral domain optical coherence tomography images," Biomed. Opt. Exp., vol. 3, no. 5, pp. 927-942, May 2012.
42. L. Fang et al., "Fast acquisition and reconstruction of optical coherence tomography images via sparse representation," IEEE Trans. Med. Imag., vol. 32, no. 11, pp. 2034-2049, Nov. 2013.
43. L. Fang, S. Li, X. Kang, J. A. Izatt, and S. Farsiu, "3-D adaptive sparsity based image compression with applications to optical coherence tomography," IEEE Trans. Med. Imag., vol. 34, no. 6, pp. 1306-1320, Jun. 2015.
44. 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.
45. C.-C. Chang and C.-J. Lin, "LIBSVM: A library for support vector machines," ACM Trans. Intell. Syst. Technol., vol. 2, no. 3, pp. 27:1-27:27, Jul. 2011.
46. M. Dalla Mura, J. Atli Benediktsson, B. Waske, and L. Bruzzone, "Extended profiles with morphological attribute filters for the analysis of hyperspectral data," Int. J. Remote Sens., vol. 31, no. 22, pp. 5975-5991, Jul. 2010.
47. J. A. Gualtieri and R. F. Cromp, "Support vector machines for hyperspectral remote sensing classification," in Proc. SPIE, 1999, vol. 3584, pp. 221-232.
48. A. Plaza et al., "Recent advances in techniques for hyperspectral image processing," Remote Sens. Environ., vol. 113, pp. S110-S122, Sep. 2009.
49. 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.
50. B. Song et al., "Remotely sensed image classification using sparse representations of morphological attribute profiles," IEEE Trans. Geosci. Remote Sens., vol. 52, no. 8, pp. 5122-5136, Aug. 2014.
51. R. Ji et al., "Spectral-spatial constraint hyperspectral image classification," IEEE Trans. Geosci. Remote Sens., vol. 52, no. 3, pp. 1811-1824, Mar. 2014.