A classification algorithm for hyperspectral images based on synergetics theory

IEEE Transactions on Geoscience and Remote Sensing

Volumn 51, Issue 5, 2013, Pages 2887-2898

  Cerra, Daniele ^a   Mul̈ler, Rupert ^a   Reinartz, Peter ^a  

^a GERMAN AEROSPACE CENTER DLR   (Germany)

Author keywords

Hyperspectral image analysis; Image classification; Least squares approximation (LS); Synergetics theory

Indexed keywords

CLASSIFICATION ALGORITHM; CLASSIFICATION METHODOLOGIES; HYPER-SPECTRAL IMAGES; HYPERSPECTRAL IMAGE ANALYSIS; HYPERSPECTRAL SENSORS; PATTERN RECOGNITION ALGORITHMS; SINGLE TRAINING SAMPLE; SYNERGETICS THEORY;

ALGORITHMS; CLASSIFICATION (OF INFORMATION); INDEPENDENT COMPONENT ANALYSIS; LEAST SQUARES APPROXIMATIONS; PATTERN RECOGNITION; SPECTROSCOPY; VECTOR SPACES;

IMAGE CLASSIFICATION;

ALGORITHM; DATA SET; IMAGE ANALYSIS; IMAGE CLASSIFICATION; LEAST SQUARES METHOD; PATTERN RECOGNITION; PIXEL; SENSOR; SPECTRAL ANALYSIS; SYNERGISM;

EID: 84885021831     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2012.2219059     Document Type: Article

References (41)

1. D. W. Scott, The Curse of Dimensionality and Dimension Reduction. New York: Wiley, 2008, pp. 195-217.
2. J. Bioucas-Dias and J. Nascimento, "Hyperspectral subspace identification," IEEE Trans. Geosci. Remote Sens., vol. 46, no. 8, pp. 2435-2445, Aug. 2008.
3. J. Chanussot, M. M. Crawford, and B.-C. Kuo, "Foreword to the special issue on hyperspectral image and signal processing," IEEE Trans. Geosci. Remote Sens., vol. 48, no. 11, pp. 3871-3876, Nov. 2010.
4. S. Kaewpijit, J. Le Moigne, and T. El-Ghazawi, "Automatic reduction of hyperspectral imagery using wavelet spectral analysis," IEEE Trans. Geosci. Remote Sens., vol. 41, no. 4, pp. 863-871, Apr. 2003.
5. H. Haken, Synergetics Computers and Cognition. New York: Springer-Verlag, 1991, ser. Springer Series Synergetics.
6. A. Plaza, J. A. Benediktsson, J. W. Boardman, J. Brazile, L. Bruzzone, G. Camps-Valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J. C. Tilton, and G. Trianni (2009, Sep.). Recent advances in techniques for hyperspectral image processing. Remote Sens. Environ. [Online]. 113(Supplement 1), pp. S110-S122. Available: http://linkinghub.elsevier.com/retrieve/pii/ S0034425709000807
7. H. Haken (2007). Synergetics. Scholarpedia [Online]. 2(1), p. 1400. Available: http://www.scholarpedia.org/article/Synergetics
8. M. Bushev, Synergetics: Chaos, Order, Self-Organization. Singapore: World Scientific, 1994.
9. H. Haken (1980, Mar.). Synergetics. Naturwissenschaften [Online]. 67(3), pp. 121-128. Available: http://dx.doi.org/10.1007/BF01073611
10. W. K. Konen, T. Maurer, and C. Von Der Malsburg, "A fast dynamic link matching algorithm for invariant pattern recognition," Neural Netw., vol. 7, no. 6/7, pp. 1019-1030, 1994.
11. K. Crounse and L. Chua, "A synergetics approach to image processing in cellular neural networks," in Proc. IEEE ISCAS Connect. World, May 1996, vol. 3, pp. 134-137.
12. T. Hogg, T. Talhami, and D. Rees, "An improved synergetic algorithm for image classification," Pattern Recognit., vol. 31, no. 12, pp. 1893-1903, Dec. 1998. (Pubitemid 29289991)
13. H. Maeda, S. Okada, and T. Irie, "A new method for stereo matching problem in computer vision using synergetics," in Proc. IEEE Int. Conf. SMC, 1999, vol. 3, pp. 503-508.
14. F. G. Boebel and T. Wagner, "Theoretical foundations of synergetic image processing," in Proc. Appl. Synergetics Synergetics Eng. Conf., 1994, pp. 46-52.
15. H. Kawano, S. Imamura, H. Maeda, and N. Ikoma, "3D rough reconstruction of buildings from streetscape by synergetic stereo matching," in Proc. IEEE Int. Conf. SMC, Oct. 2006, vol. 3, pp. 1992-1999.
16. S. Gou and L. Jiao, "SAR image recognition using synergetic neural networks based on immune clonal programming," in Advances in Neural Networks - ISNN 2004. Berlin, Germany: Springer-Verlag, 2004, ser. Lecture Notes in Computer Science, pp. 918-923.
17. F. A. Kruse, A. B. Lefkoff, J. B. Boardman, K. B. Heidebrecht, A. T. Shapiro, P. J. Barloon, and A. F. H. Goetz, "The spectral image processing system (SIPS) - interactive visualization and analysis of imaging spectrometer data," Remote Sens. Environ., vol. 44, no. 2/3, pp. 145-163, May/Jun. 1993.
18. C.-I. Chang, "Orthogonal subspace projection (OSP) revisited: A comprehensive study and analysis," IEEE Trans. Geosci. Remote Sens., vol. 43, no. 3, pp. 502-518, Mar. 2005. (Pubitemid 40320272)
19. N. Keshava and J. Mustard, "Spectral unmixing," IEEE Signal Process. Mag., vol. 19, no. 1, pp. 44-57, Jan. 2002.
20. F. Y. Wang, P. J. A. Lever, and B. Pu, "A robotic vision system for object identification and manipulation using synergetic pattern recognition," Robot. Comput. Integr. Manuf., vol. 10, no. 6, pp. 445-459, Dec. 1993.
21. T. Wagner and F. G. Boebel, "Testing synergetic algorithms with industrial classification problems," Neural Netw., vol. 7, no. 8, pp. 1313-1321, 1994.
22. T. Zhao, L. Tang, H. Ip, and F. Qi, "On relevance feedback and similarity measure for image retrieval with synergetic neural nets," Neurocomputing, no. 51, pp. 105-124, Apr. 2003.
23. L. I. Kuncheva, Combining Pattern Classifiers: Methods and Algorithms. Hoboken, NJ: Wiley-Interscience, 2004.
24. K. K. Ladha, "The Condorcet jury theorem, free speech, and correlated votes," Amer. J. Pol. Sci., vol. 36, no. 3, pp. 617-634, Aug. 1992.
25. J. Plaza, A. J. Plaza, and C. Barra (2009). Multi-channel morphological profiles for classification of hyperspectral images using support vector machines. Sensors [Online]. 9(1), pp. 196-218. Available: http://www.mdpi.com/ 1424-8220/9/1/196/
26. 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, Mar. 2005. (Pubitemid 40320269)
27. A. Makarau, R. Mueller, G. Palubinskas, and P. Reinartz, "Hyperspectral data classification using factor graphs," in Proc. ISPRS, 2012, to be published.
28. J. Li, J. 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.
29. H. Du, C. Chang, H. Ren, C. Chang, J. Jensen, and F. D'Amico, "New hyperspectral discrimination measure for spectral characterization," Opt. Eng., vol. 43, no. 8, pp. 1777-1786, Aug. 2004.
30. T. Joachims, Making Large Scale SVM Learning Practical. Dortmund, Germany: Univ. Dortmund Press, 1999.
31. B. Demir and S. Erturk, "Empirical mode decomposition of hyperspectral images for support vector machine classification," IEEE Trans. Geosci. Remote Sens., vol. 48, no. 11, pp. 4071-4084, Nov. 2010.
32. 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.
33. G. Foody, "Thematic map comparison: Evaluating the statistical significance of differences in classification accuracy," Photogramm. Eng. Remote Sens., vol. 70, no. 5, pp. 627-633, May 2004. (Pubitemid 39081774)
34. F. R. Kschischang, B. J. Frey, and H.-A. Loeliger, "Factor graphs and the sum-product algorithm," IEEE Trans. Inf. Theory, vol. 47, no. 2, pp. 498-519, Feb. 2001.
35. C. Lee and D. A. Landgrebe, "Feature extraction based on decision boundaries," IEEE Trans. Pattern Anal. Mach. Intell., vol. 15, no. 4, pp. 388-400, Apr. 1993.
36. D. Boehning (1992, Mar.). Multinomial logistic regression algorithm. Ann. Inst. Stat. Math. [Online]. 44(1), pp. 197-200. Available: http://www. springerlink.com/index/P661G851671713U2.pdf
37. 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.
38. T. Stuffler, K. Foerster, S. Hofer, M. Leipold, B. Sang, H. Kaufmann, B. Penne, A. Mueller, and C. Chlebek, "Hyperspectral imaging - An advanced instrument concept for the EnMAP mission (Environmental Mapping and Analysis Program)," Acta Astron., vol. 65, no. 7/8, pp. 1107-1112, Oct./Nov. 2009.
39. 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.
40. G. Golub and C. Van Loan, "Total least squares," in Smoothing Techniques for Curve Estimation, T. Gasser and M. Rosenblatt, Eds. Berlin: Springer-Verlag, 1979, ser. Lecture Notes in Mathematics, pp. 69-76.
41. ImageSVM. [Online]. Available: http://www2.huberlin.de/hurs/projects/ imageSVM.php