SVM- and MRF-based method for accurate classification of hyperspectral images

IEEE Geoscience and Remote Sensing Letters

Volumn 7, Issue 4, 2010, Pages 736-740

  Tarabalka, Yuliya ^a,b   Fauvel, Mathieu ^c   Chanussot, Jocelyn ^a   Benediktsson, Jón Atli ^b  

^a GIPSA LAB   (France)

^b UNIVERSITY OF ICELAND   (Iceland)

^c INRIA   (France)

Author keywords

Classification; hyperspectral images; Markov random field (MRF); support vector machine (SVM)

Indexed keywords

AIRBORNE IMAGES; CLASSIFICATION; CLASSIFICATION ACCURACY; CLASSIFICATION APPROACH; CLASSIFICATION RESULTS; CONTEXTUAL INFORMATION; HYPER-SPECTRAL IMAGES; HYPERSPECTRAL; HYPERSPECTRAL SENSORS; MARKOV RANDOM FIELD; MARKOV RANDOM FIELDS; NOVEL METHODS; PIXELWISE CLASSIFICATION; SPECTRAL BAND; SPECTRAL-SPATIAL CLASSIFICATION; SUPPORT VECTOR MACHINE (SVM);

IMAGE ANALYSIS; SUPPORT VECTOR MACHINES;

INDEPENDENT COMPONENT ANALYSIS;

EID: 77958017904     PISSN: 1545598X     EISSN: None     Source Type: Journal    
DOI: 10.1109/LGRS.2010.2047711     Document Type: Article

References (19)

1. C.-I. Chang, Hyperspectral Data Exploitation: Theory and Applications. New York: Wiley-Interscience, 2007.
2. D. A. Landgrebe, Signal Theory Methods in Multispectral Remote Sensing. New York: Wiley, 2003.
3. 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.
4. V. Vapnik, Statistical Learning Theory. New York: Wiley, 1998.
5. R. L. Kettig and D. A. Landgrebe, "Classification of multispectral image data by extraction and classification of homogeneous objects", IEEE Trans. Geosci. Electron., vol. GE-14, no. 1, pp. 19-26, Jan. 1976.
6. 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. 11, pp. 3804-3814, Nov. 2008.
7. A. Farag, R. Mohamed, and A. El-Baz, "A unified framework for map estimation in remote sensing image segmentation", IEEE Trans. Geosci. Remote Sens., vol. 43, no. 7, pp. 1617-1634, Jul. 2005.
8. 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.
9. 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. (Pubitemid 43131904)
10. F. Bovolo and L. Bruzzone, "A context-sensitive technique based on support vector machines for image classification", in Proc. PReMI, 2005, pp. 260-265.
11. D. Liu, M. Kelly, and P. Gong, "A spatial-temporal approach to monitoring forest disease spread using multi-temporal high spatial resolution imagery", Remote Sens. Environ., vol. 101, no. 2, pp. 167-180, Mar. 2006.
12. A. Solberg, T. Taxt, and A. Jain, "A Markov random field model for classification of multisource satellite imagery", IEEE Trans. Geosci. Remote Sens., vol. 34, no. 1, pp. 100-113, Jan. 1996.
13. J. Platt, "Probabilities for support vector machines", in Advances in Large Margin Classifiers, A. Smola, P. Bartlett, B. Schölkopf, and D. Schuurmans, Eds. Cambridge, MA: MIT Press, 2000, pp. 61-74.
14. T.-F. Wu, C.-J. Lin, and R. C. Weng, "Probability estimates for multiclass classification by pairwise coupling", J. Mach. Learn. Res., no. 5, pp. 975-1005, 2004.
15. 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.
16. R. Gonzalez and R. Woods, Digital Image Processing, 2nd ed. Englewood Cliffs, NJ: Prentice-Hall, 2002.
17. N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, and E. Teller, "Equations of state calculations by fast computing machines", J. Chem. Phys., vol. 21, no. 6, pp. 1087-1092, 1953.
18. S. Geman and D. Geman, "Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images", IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-6, no. 6, pp. 721-741, Nov. 1984.
19. Y. Tarabalka, J. Chanussot, J. A. Benediktsson, J. Angulo, and M. Fauvel, "Segmentation and classification of hyperspectral data using watershed", in Proc. IGARSS, Boston, MA, 2008, pp. III-652-III-655.