PerTurbo manifold learning algorithm for weakly labeled hyperspectral image classification

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

Volumn 7, Issue 4, 2014, Pages 1070-1078

  Chapel, Laetitia ^a   Burger, Thomas ^b   Courty, Nicolas ^a   Lefevre, Sebastien ^a  

^a Université de Bretagne 1Sud   (France)

^b UNIV GRENOBLE ALPES   (France)

Author keywords

Classification; generative method; hyperspectral images; low sized training sets; manifold learning; PerTurbo algorithm; remote sensing; support vector machines (SVM)

Indexed keywords

CLASSIFICATION (OF INFORMATION); INDEPENDENT COMPONENT ANALYSIS; LEARNING ALGORITHMS; REMOTE SENSING; SPECTROSCOPY; SUPPORT VECTOR MACHINES;

DISCRIMINATIVE METHODS; GENERATIVE METHODS; HYPER-SPECTRAL IMAGES; HYPERSPECTRAL DATA ANALYSIS; HYPERSPECTRAL IMAGE CLASSIFICATION; MANIFOLD LEARNING; MANIFOLD LEARNING ALGORITHM; TRAINING SETS;

IMAGE CLASSIFICATION;

ALGORITHM; DATA PROCESSING; IMAGE CLASSIFICATION; MULTISPECTRAL IMAGE; REMOTE SENSING;

EID: 84899909472     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2014.2304304     Document Type: Article

References (34)

1. J. M. Bioucas-Dias, A. Plaza, G. Camps-Valls, P. Scheunders, N. 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.
2. D. A. Landgrebe, Signal Theory Methods in Multispectral Remote Sensing. Hoboken, NJ, USA: Wiley, 2003.
3. G. H. Hughes, "On the mean accuracy of statistical pattern recognizers," IEEE Trans. Inf. Theory, vol. 14, no. 1, pp. 55-63, Jan. 1968.
4. K. Fukunaga and R. R. Hayes, "Effects of sample size in classifier design," IEEE Trans. Pattern Anal. Mach. Intell., vol. 11, no. 8, pp. 873-885, Aug. 1989.
5. V. N. Vapnik, Statistical Learning Theory. Hoboken, NJ, USA: Wiley, 1998.
6. 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.
7. B. Guo, S. Gunn, R. Damper, and J. Nelson, "Customizing kernel functions for SVM-based hyperspectral image classification," IEEE Trans. Image Process., vol. 44, pp. 2839-2846, 2008.
8. M. Fauvel, J. Chanussot, and J. A. Benediktsson, "Aspatial-spectral kernelbased approach for the classification of remote-sensing images," Pattern Recognit., vol. 45, pp. 381-392, 2012.
9. B. Schölkopf, A. Smola, and K. R. Müller, "Kernel principal component analysis," in Proc. Int. Conf. Artificial Neural Networks (ICANN), vol. 1327, 1997, pp. 583-588.
10. J. Lafferty and G. Lebanon, "Diffusion kernels on statistical manifolds," J. Mach. Learn. Res., vol. 6, pp. 129-163, 2005.
11. A. Green, M. Berman, P. Switzer, and M. Craig, "A transformation for ordering multispectral data in terms of image quality with implications for noise removal," IEEE Trans. Geosci. Remote Sens., vol. 26, no. 1, pp. 65-74, Jan. 1988. (Pubitemid 18596008)
12. J. B. Tenenbaum, V. De Silva, and J. C. Langford, "A global geometric framework for nonlinear dimensionality reduction," Science, vol. 290, no. 5500, pp. 2319-2323, 2000. (Pubitemid 32041577)
13. S. T. Roweis and L. K. Saul, "Nonlinear dimensionality reduction by locally linear embedding," Science, vol. 290, no. 5500, pp. 2323-2326, 2000. (Pubitemid 32041578)
14. B. Hou, X. Zhang, Q. Ye, and Y. Zheng, "A novel method for hyperspectral image classification based on laplacian eigenmap pixels distribution-flow," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 6, no. 3, pp. 1602-1618, Jun. 2013.
15. 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. (Pubitemid 40320267)
16. T. Han and D. G. Goodenough, "Nonlinear feature extraction of hyperspectral data based on locally linear embedding (LLE)," in Proc. IEEE Int. Conf. Geosci. Remote Sens. Symp. (IGARSS), 2005, vol. 2, pp. 1237-1240.
17. W. Kim and M. Crawford, "Adaptive classification for hyperspectral image data using manifold regularization kernel machines," IEEE Trans. Geosci. Remote Sens., vol. 48, no. 11, pp. 4110-4121, Nov. 2010.
18. M. Li, M. M. Crawford, and J. Tian, "Local manifold learning-based knearest-neighbor for hyperspectral image classification," IEEE Trans. Geosci. Remote Sens., vol. 48, no. 11, pp. 4099-4109, Nov. 2010.
19. A. Y. Ng and M. I. Jordan, "On discriminative vs. generative classifiers: A comparison of logistic regression and naive Bayes," in Proc. Adv. Neural Inf. Process. Syst. (NIPS), 2001, pp. 841-848.
20. T. Hospedales, S. Gong, and T. Xiang, "Finding rare classes: Active learning with generative and discriminative models," IEEE Trans. Knowl. Data Eng., vol. 25, no. 2, pp. 374-386, Feb. 2013.
21. J. H. Xue and D. M. Titterington, "Comment on 'On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes'," Neural Process. Lett., vol. 28, no. 3, pp. 1370-4621, 2008.
22. 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. (Pubitemid 40811944)
23. S. Wenzel and L. Hotz, "The role of sequences for incremental learning," in Proc. Int. Conf. Agents Artif. Intell. (ICAART), 2010, vol. 1, pp. 434-439.
24. N. Courty, T. Burger, and L. Johann, "PerTurbo: A new classification algorithm based on the spectrum perturbations of the Laplace-Beltrami operator," in Proc. Euro. Conf. Mach. Learn. Practice Principles Practice Knowl. Discovery Databases (ECML/PKDD), 2011, vol. 1, pp. 359-374.
25. I. Chavel, Eigenvalues in Riemannian geometry. New York, NY, USA: Academic, 1984.
26. L. Chapel, T. Burger, N. Courty, and S. Lefèvre, "Classwise hyperspectral image classification with PerTurbo method," in Proc. IEEE Int. Conf. Geosci. Remote Sens. Symp. (IGARSS), 2012, pp. 6883-6886.
27. C. Öztireli, M. Alexa, and M. Gross, "Spectral sampling of manifolds," in Proc. ACM Trans. Graphics (SIGGRAPH ASIA), 2010.
28. H. Cevikalp, D. Larlus, M. Neamtu, B. Triggs, and F. Jurie, "Manifold based local classifiers: Linear and nonlinear approaches," J. Signal Process. Syst., vol. 61, no. 1, pp. 61-73, 2010.
29. M. Belkin and P. Niyogi, "Laplacian eigenmaps and spectral techniques for embedding and clustering," in Proc. Adv. Neural Inf. Process. Syst. (NIPS), 2001, vol. 14, pp. 585-591.
30. K. Zhang, V. Zheng, Q. Wang, J. Kwok, Q. Yang, and I. Marsic, "Covariate shift in Hilbert space: A solution via surrogate kernels," in Proc. Int. Conf. Mach. Learn. (ICML), 2013, vol. 28, pp. 388-395.
31. N. Shawe-Taylor and A. Kandola, "On kernel target alignment," in Proc. Adv. Neural Inf. Process. Syst. (NIPS), 2002, vol. 14, p. 367.
32. S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge, U.K.: Cambridge Univ. Press, 2004.
33. A. Karatzoglou, A. Smola, K. Hornik, and A. Zeileis, "Kernlab-An S4 package for kernel methods in R," J. Stat. Softw., vol. 11, no. 9, pp. 1-20, 2004.
34. G. M. Foody, "Thematic map comparison: Evaluating the statistical significance of differences in classification accuracy," Photogramm. Eng. Remote Sens., vol. 70, no. 5, pp. 627-633, 2004. (Pubitemid 39081774)