A dynamic subspace method for hyperspectral image classification

IEEE Transactions on Geoscience and Remote Sensing

Volumn 48, Issue 7, 2010, Pages 2840-2853

  Yang, Jinn Min ^a,b   Kuo, Bor Chen ^b   Yu, Pao Ta ^a   Chuang, Chun Hsiang ^c  

^a NATIONAL CHUNG CHENG UNIVERSITY   (Taiwan)

^b NATIONAL TAICHUNG UNIVERSITY OF EDUCATION   (Taiwan)

^c NATIONAL CHIAO TUNG UNIVERSITY   (Taiwan)

Author keywords

Kernel smoothing (KS); Random subspace method (RSM); Small sample size (SSS) classification

Indexed keywords

COMPONENT DIMENSIONS; ENSEMBLE CLASSIFIERS; HYPERSPECTRAL IMAGE CLASSIFICATION; KERNEL SMOOTHING; MULTIPLE CLASSIFIER SYSTEMS; RANDOM SUBSPACE METHOD; SMALL SAMPLE SIZE; SUB-SPACE METHODS; SUBSPACE SELECTION;

CLASSIFIERS; IMAGE ANALYSIS; PATTERN RECOGNITION SYSTEMS;

CONTENT BASED RETRIEVAL;

DATA SET; IMAGE CLASSIFICATION; PATTERN RECOGNITION; SMOOTHING; SPECKLE; SPECTRAL ANALYSIS;

EID: 77953872402     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2010.2043533     Document Type: Article

References (43)

1. M. Skurichina and R. P. W. Duin, "Bagging, boosting and the random subspace method for linear classifiers," Pattern Anal. Appl., vol.5, no.2, pp. 121-135, Jun. 2002.
2. L. Breiman (1998, Jun.). Arcing classifiers. Ann. Stat. [Online]. 26(3), pp. 801-824. Available: http://www.jstor.org/stable/120055
3. G. F. Hughes, "On the mean accuracy of statistical pattern recognition," IEEE Trans. Inf. Theory, vol.IT-14, no.1, pp. 55-63, Jan. 1968.
4. R. E. Bellman, Adaptive Control Processes-A Guided Tour. Princeton, NJ: Princeton Univ. Press, 1961.
5. T. K. Ho, "The random subspace method for constructing decision forests," IEEE Trans. Pattern Anal. Mach. Intell., vol.20, no.8, pp. 832-844, Aug. 1998.
6. T. K. Ho, "Nearest neighbors in random subspaces," in Proc. IAPR, Lecture Notes in Computer Science: Advances in Pattern Recognition, 1998, pp. 640-648.
7. M. Skurichina and R. P. W. Duin, "Bagging and the random subspace method for redundant feature spaces," in Proc. 2nd Int.Workshop Multiple Classifier Syst., 2001, pp. 1-10.
8. B.-C. Kuo, C.-H. Pai, T.-W. Sheu, and G.-S. Chen, "Hyperspectral data classification using classifier overproduction and fusion strategies," in Proc. IEEE Int. Geosci. Remote Sens. Symp., 2004, vol.5, pp. 2937-2940.
9. A. Bertoni, R. Folgieri, and G. Valentini, "Feature selection combined with random subspace ensemble for gene expression based diagnosis of malignancies," in Proc. 15th ItalianWorkshop Neural Nets, Perugia, Italy, 2004, pp. 29-35.
10. A. Bertoni, R. Folgieri, and G. Valentini, "Random subspace ensembles of support vector machines," in Neurocomputing, vol.63, pp. 535-539, 2005.
11. D. Tao, X. Tang, X. Li, and X. Wu, "Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval," IEEE Trans. Pattern Anal. Mach. Intell., vol.28, no.7, pp. 1088-1099, Jul. 2006.
12. S. Sun, C. Zhang, and D. Zhang, "An experimental evaluation of ensemble methods for EEG signal classification," Pattern Recognit. Lett., vol.28, no.15, pp. 2157-2163, Nov. 2007.
13. J. W. Christopher, "Hyperspectral image classification with limited training data samples using feature subspaces," Proc. SPIE, vol.5425, pp. 170-181, 2004.
14. J. Ham, Y. Chen, M. M. Crawford, and J. Ghosh, "Investigation of the random forest framework for classification of hyperspectral data," IEEE Trans. Geosci. Remote Sens., vol.43, no.3, pp. 492-501, Mar. 2005.
15. C.-H. Chuang, B.-C. Kuo, and H.-P. Wang, "Fuzzy fusion method for combining small number of classifiers in hyperspectral image classification," in Proc. 8th Int. Conf. Intell. Syst. Des. Appl., 2008, vol.1, pp. 26-28.
16. L. I. Kuncheva, Combining Pattern Classifiers: Methods and Algorithms. Hoboken, NJ: Wiley, 2004.
17. B. W. Silverman, Density Estimation for Statistics and Data Analysis. London, U.K.: Chapman & Hall, 1985.
18. C. M. Bishop, Neural Networks for Pattern Recognition. New York: Oxford Univ. Press, 1995.
19. R. Cossu, S. Chaudhuri, and L. Bruzzone, "A context-sensitive Bayesian technique for the partially supervised classification of multitemporal images," IEEE Geosci. Remote Sens. Lett., vol.2, no.3, pp. 352-356, Jul. 2005.
20. L. O. Jimenez, J. L. Rivera-Medina, E. Rodriguez-Diaz, E. Arzuaga-Cruz, and M. Ramirez-Velez, "Integration of spatial and spectral information by means of unsupervised extraction and classification for homogenous objects applied to multispectral and hyperspectral data," IEEE Trans. Geosci. Remote Sens., vol.43, no.4, pp. 844-851, Apr. 2005.
21. G. Camps-Valls, L. Gómez-Chova, J. Muñoz-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. (Pubitemid 43131904)
22. X. Jia and J. A. Richards, "Managing the spectral-spatial mix in context classification using Markov random fields," IEEE Geosci. Remote Sens. Lett., vol.5, no.2, pp. 311-314, Apr. 2008.
23. Q. Jackson and D. A. Landgrebe, "Adaptive Bayesian contextual classification based on Markov random fields," IEEE Trans. Geosci. Remote Sens., vol.40, no.11, pp. 2454-2463, Nov. 2002.
24. Y. Maghsoudi, A. Alimohammadi, M. J. Valadan Zoej, and B. Mojaradi, "Application of feature selection and classifier ensembles for the classification of hyperspectral data," in Proc. 26th Asian Conf. Remote Sens., Hanoi, Vietnam, 2005.
25. K. Fukunaga, Introduction to Statistical Pattern Recognition, 2nd ed. San Diego, CA: Academic, 1990.
26. L. Devroye, Non-Uniform Random Variate Generation. New York: Springer-Verlag, 1986.
27. F. van der Heiden, R. P. W. Duin, D. de Ridder, and D. M. J. Tax, Classification, Parameter Estimation and State Estimation: An Engineering Approach Using MATLAB. Chichester, U.K.: Wiley, 2004.
28. V. N. Vapnik, Statistical Learning Theory. New York: Wiley, 1998.
29. J. P. Hoffbeck and D. A. Landgrebe, "Covariance matrix estimation and classification with limited training data," IEEE Trans. Pattern Anal.Mach. Intell., vol.18, no.7, pp. 763-767, Jul. 1996.
30. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York: Springer-Verlag, 2001.
31. S. D. Bay, "Nearest neighbor classification from multiple feature subsets," Intell. Data Anal., vol.3, no.3, pp. 191-209, Sep. 1999.
32. R. P. W. Duin, P. Juszczak, P. Paclik, E. Pekalska, D. de Ridder, D. M. J. Tax, and S. Verzakov, PRTools4.1, A Matlab Toolbox for Pattern Recognition. Delft, The Netherlands: Delft Univ. Technol., 2007.
33. F. J. Kampas, "Tricks of the trade: using reduce to solve the Kuhn-Tucker equations," Mathematica J., vol.9, pp. 686-689, 2005.
34. C.-C. Chang and C.-J. Lin, LIBSVM: A Library for Support Vector Machines, 2001. [Online]. Available: http://www.csie.ntu.edu.tw/~cjlin/ libsvm
35. C.-W. Hsu, C.-C. Chang, and C.-J. Lin, A Practical Guide to Support Vector Classification, 2004. [Online]. Available: http://www.csie.ntu.edu. tw/~cjlin/papers/guide/guide.pdf
36. 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.
37. L. Bruzzone and C. Persello, "A novel context-sensitive semisupervised SVM classifier robust to mislabeled training samples," IEEE Trans. Geosci. Remote Sens., vol.47, no.7, pp. 2142-2154, Jul. 2009.
38. B.-C. Kuo and K.-Y. Chang, "Feature extractions for small sample size classification problem," IEEE Trans. Geosci. Remote Sens., vol.45, no.3, pp. 756-764, Mar. 2007.
39. B.-C. Kuo, C.-H. Li, and J.-M. Yang, "Kernel nonparametric weighted feature extraction for hyperspectral image classification," IEEE Trans. Geosci. Remote Sens., vol.47, no.4, pp. 1139-1155, Apr. 2008.
40. Y. Jhung and P. H. Swain, "Bayesian contextual classification based on modified M-estimates and Markov random fields," IEEE Trans. Geosci. Remote Sens., vol.34, no.1, pp. 67-75, Jan. 1996.
41. A. H. S. Solberg, T. Taxt, and A. K. 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.
42. D. A. Landgrebe, Signal Theory Methods in Multispectral Remote Sensing. Hoboken, NJ: Wiley, 2003.
43. AVIRIS NW Indiana's Indian Pines 1992 Data Set, [Online]. Available: ftp://ftp.ecn.purdue.edu/biehl/MultiSpec/92AV3C, (original files) and ftp://ftp.ecn.purdue.edu/biehl/PC-MultiSpec/ThyFiles.zip, (ground truth)