(Semi-) Supervised probabilistic principal component analysis for hyperspectral remote sensing image classification

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

Volumn 7, Issue 6, 2014, Pages 2224-2236

  Xia, Junshi ^a   Chanussot, Jocelyn ^a,b   Du, Peijun ^c   He, Xiyan ^a  

^a GIPSA LAB   (France)

^b UNIVERSITY OF ICELAND   (Iceland)

^c NANJING UNIVERSITY   (China)

Author keywords

Classification; dimensionality reduction; feature extraction; hyperspectral remote sensing; semi supervised

Indexed keywords

CLASSIFICATION (OF INFORMATION); EXTRACTION; FEATURE EXTRACTION; IMAGE ANALYSIS; IMAGE CLASSIFICATION; REMOTE SENSING; SPECTROSCOPY;

DIMENSIONALITY REDUCTION; FEATURE EXTRACTION METHODS; HYPERSPECTRAL REMOTE SENSING; HYPERSPECTRAL REMOTE SENSING IMAGE; LOW COMPUTATIONAL COMPLEXITY; PROBABILISTIC PRINCIPAL COMPONENT ANALYSIS; SEMI-SUPERVISED; SUPERVISED PROBABILISTIC PRINCIPAL COMPONENT ANALYSIS;

PRINCIPAL COMPONENT ANALYSIS;

ALGORITHM; IMAGE ANALYSIS; IMAGE CLASSIFICATION; PRINCIPAL COMPONENT ANALYSIS; PROBABILITY; REMOTE SENSING; SPECTRAL ANALYSIS;

EID: 84905920772     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2013.2279693     Document Type: Article

References (40)

1. J. C. Harsanyi and C. I Chang, "Hyperspectral image classification and dimensionality reduction: An orthogonal subspace projection approach," IEEE Trans. Geosci. Remote Sens., vol. 32, no. 4, pp. 779-785, 1994.
2. Y. Tarabalka, J. A. Benediktsson, J. Chanussot, and J. Tilton, "Multiple spectral-spatial classification approach for hyperspectral data," IEEE Trans. Geosci. Remote Sens., vol. 48, no. 11, pp. 4122-4132, 2010.
3. I. Dopido, A. Villa, A. Plaza, and P. Gamba, "A quantitative and comparative assessment of unmixing-based feature extraction techniques for hyperspectral image classification," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 5, no. 2, pp. 421-435, 2012.
4. G. Hughes, "On the mean accuracy of statistical pattern recognizers," IEEE Trans. Inf. Theory, vol. 14, no. 1, pp. 55-63, 1968.
5. D. A. Landgrebe, Signal Theory Methods in Multispectral Remote Sensing. New York, NY, USA: Wiley, 1984.
6. A. C. Braun, U. Weidner, and S. Hinz, "Classification in high-dimensional feature spaces assessment using SVM, IVM and RVM with focus on simulated Enmap data," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 5, no. 2, pp. 436-443, 2012.
7. M. P. and G. M. Foody, "Feature selection for classification of hyperspectral data by SVM," IEEE Trans. Geosci. Remote Sens., vol. 48, no. 5, pp. 2297-2307, 2010.
8. B. Mojaradi, H. Abrishami-Moghaddam, M. Zoej, and R. Duin, "Dimensionality reduction of hyperspectral data via spectral feature extraction," IEEE Trans. Geosci. Remote Sens., vol. 47, no. 7, pp. 2091-2105, 2009.
9. A. Plaza, P. Martinez, J. Plaza, and R. Perez, "Dimensionality reduction and classification of hyperspectral image data using sequences of extended morphological transformations," IEEE Trans. Geosci. Remote Sens., vol. 43, no. 3, pp. 466-479, 2005. (Pubitemid 40320269)
10. H. Zeng and H. J. Triosell, "Dimensionality reduction in hyperspectral image classification," in Proc. IEEE ICIP, 2004, pp. 913-916. (Pubitemid 40820183)
11. I. Guyon, S. Gunn, M. Nikravesh, and L. Zadeh, Feature Extraction: Foundations and Applications. New York, NY, USA: Springer-Verlag, 2006.
12. J. A. Richards and X. Jia, Remote Sensing Digital Image Analysis: An Introduction. New York, NY, USA: Springer-Verlag, 2006.
13. A. A. Green, M. Berman, P. Switzer, and M. D. 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, 1988. (Pubitemid 18596008)
14. Q. Du, "Unsupervised real-time constrained linear discriminant analysis to hyperspectral image classification," Pattern Recognition, vol. 40, no. 5, pp. 1510-1519, 2007. (Pubitemid 46123379)
15. M. Fauvel, J. Chanussot, and J. A. Benediktsson, "Kernel principal component analysis for the classification of hyperspectral remote sensing data over urban areas," EURASIP J. Advances in Signal Processing, vol. 2009, no. 2, pp. 1-15.
16. J.Wen, Z. Tian, X. Liu, and W. Lin, "Neighborhood preserving orthogonal PNMF feature extraction for hyperspectral image classification," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 6, no. 2, pp. 759-768, 2013.
17. A. Cheriyadat and L. M. Bruce, "Why principal component analysis is not an appropriate feature extraction method for hyperspectral data," in Proc. IEEE IGARSS, 2003, pp. 3420-3422.
18. Y. Song, F. Nie, C. Zhang, and S. Xiang, "A unified framework for semi-supervised dimensionality reduction," Pattern Recognition, vol. 41, no. 9, pp. 2789-2799, 2008.
19. T. V. Bandos, L. Bruzzone, and G. Camps-Valls, "Classification of hyperspectral images with regularized linear discriminant analysis," IEEE Trans. Geosci. Remote Sens., vol. 47, no. 3, pp. 862-873, 2009.
20. B.-C. Kuo and D. A. Landgrebe, "Nonparametric weighted feature extraction for classification," IEEE Trans. Geosci. Remote Sens., vol. 42, no. 5, pp. 1096-1105, 2004.
21. 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, 2009.
22. F. P. Nie, D. Xu, I.W. Tsang, and C. Zhang, "Flexiblemanifold embedding: A framework for semi-supervised and unsupervised dimension reduction," IEEE Trans. Image Process., vol. 19, no. 7, pp. 1921-1932, 2010.
23. D. Q. Zhang, Z. H. Zhou, and S. C. Chen, "Semi-supervised dimensionality reduction," in Proc. 7th SIAM Int. Conf. Data Mining, 2007, pp. 387-393.
24. M. Sugiyama, T. Id, S. Nakajima, and J. Sese, "Semi-supervised local Fisher discriminant analysis for dimensionality reduction," Machine Learning, vol. 78, pp. 35-61, 2010.
25. S. P. Yu, K. Yu, V. Tresp, H. P. Kriegel, and M. R. Wu, "Supervised probabilistic principal component analysis," in Proc. 12th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, 2006, pp. 464-473. (Pubitemid 44535543)
26. B. Scholkopf, A. J. Smola, and M. K. R, "Nonlinear component analysis as a kernel eigenvalue problem," Neural Computation, vol. 10, no. 5, pp. 1299-1319, 1998. (Pubitemid 128463674)
27. F. Dell'Acqua, P. Gamba, A. Ferari, J. A. Palmason, B. J. A, and K. Arnason, "Exploiting spectral and spatial information in hyperspectral urban data with high resolution," IEEE Geosci. Remote Sens. Lett., vol. 1, no. 4, pp. 322-326, 2004.
28. C. Lee and D. A. Landgrebe, "Decision boundary feature extraction for neural networks," IEEE Geosci. Remote Sens. Lett., vol. 38, no. 1, pp. 75-83, 1997. (Pubitemid 127767782)
29. M. Sugiyama, "Dimensionality reduction of multimodal labeled data by local Fisher discriminant analysis," J. Machine Learning Research, vol. 27, no. 8, pp. 1021-1064, 2007.
30. B. C. Kuo and D. A. Landgrebe, "A robust classification procedure based on mixture classifiers and nonparametric weighted feature extraction," IEEE Trans. Geosci. Remote Sens., vol. 40, no. 11, pp. 2486-2494, 2002.
31. S. Roweis, "EM algorithms for PCA and SPCA," in Advances in Neural Information Processing Systems. Cambridge, MA, USA: MIT Press, 1998, pp. 626-632.
32. M. E. Tipping and C. M. Bishop, "Probabilistic principal component analysis," BMC Bioinformatics, vol. 61, no. 3, pp. 611-622, 1999.
33. S. Tadjudin and D. A. Landgrebe, "Covariance estimation with limited training samples," IEEE Trans. Geosci. Remote Sens., vol. 37, no. 4, pp. 2113-2118, 1999.
34. K. Zhang, I. W. Tsang, and J. T. Kwok, "Improved nystrm low-rank approximation and error analysis," in Proc. 25th Int. Conf. Machine Learning, New York, NY, USA, 2008, pp. 1232-1239.
35. W. Liao, A. Pizurica, P. Scheunders, W. Philips, and Y. Pi, "Semisupervised local discriminant analysis for feature extraction in hyperspectral images," IEEE Trans. Geosci. Remote Sens., vol. 51, no. 1, pp. 184-198, 2013.
36. Y. Tarabalka, J. Chanussot, and J. A. Benediktsson, "Segmentation and classification of hyperspectral images using watershed transformation," Pattern Recognition, vol. 43, no. 7, pp. 2367-2379, 2010.
37. 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, 2009.
38. X. Huang and L. Zhang, "An adaptivemean-shift analysis approach for object extraction and classification from urban hyperspectral imagery," IEEE Trans. Geosci. Remote Sens., vol. 46, no. 12, pp. 4173-4185, 2008.
39. X. Huang and L. Zhang, "A comparative study of spatial approaches for urban mapping using hyperspectral ROSIS image over Pavia city, northern of Italy," Int. J. Remote Sens., vol. 30, no. 12, pp. 3205-3221, 2009.
40. X. Huang and L. Zhang, "An SVM ensemble approach combining spectral, structural, semantic features for the classification of high resolution remotely sensed imagery," IEEE Trans. Geosci. Remote Sens., vol. 51, no. 1, pp. 257-272, 2013.