Segmentation and classification of hyperspectral images using minimum spanning forest grown from automatically selected markers

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Volumn 40, Issue 5, 2010, Pages 1267-1279

  Tarabalka, Yuliya ^a,b   Chanussot, Jocelyn ^a   Benediktsson, Jón Atli ^b  

^a GIPSA LAB   (France)

^b UNIVERSITY OF ICELAND   (Iceland)

Author keywords

Classification; hyperspectral images; marker selection; minimum spanning forest (MSF); segmentation

Indexed keywords

CLASSIFICATION; HYPER-SPECTRAL IMAGES; MARKER SELECTION; MINIMUM SPANNING FORESTS; SEGMENTATION;

PIXELS;

IMAGE SEGMENTATION;

BIOLOGICAL MARKER;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; CHEMISTRY; CLASSIFICATION; DECISION SUPPORT SYSTEM; ENVIRONMENTAL MONITORING; METHODOLOGY; SPECTROSCOPY; TREE;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BIOLOGICAL MARKERS; DECISION SUPPORT TECHNIQUES; ENVIRONMENTAL MONITORING; PATTERN RECOGNITION, AUTOMATED; SPECTRUM ANALYSIS; TREES;

EID: 77956694762     PISSN: 10834419     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSMCB.2009.2037132     Document Type: Article

References (46)

1. R. Jain, R. Kasturi, and B. G. Schunck, Machine Vision. New York: McGraw-Hill, 1995.
2. C.-I Chang, Hyperspectral Data Exploitation: Theory and Applications. New York: Wiley-Interscience, 2007.
3. D. A. Landgrebe, Signal Theory Methods in Multispectral Remote Sensing. New York: Wiley, 2003.
4. P. K. Goel, S. O. Prasher, R. M. Patel, J. A. Landry, R. B. Bonnell, and A. A. Viau, "Classification of hyperspectral data by decision trees and artificial neural networks to identify weed stress and nitrogen status of corn," Comput. Electron. Agric., vol.39, no.2, pp. 67-93, 2003.
5. 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.
6. M. Fauvel, "Spectral and spatial methods for the classification of urban remote sensing data," Ph.D. dissertation, Grenoble Inst. Technol., Grenoble, France, 2007.
7. 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.
8. 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.
9. Y. Tarabalka, J. A. Benediktsson, and J. Chanussot, "Classification of hyperspectral data using support vector machines and adaptive neighborhoods," in Proc. 6th EARSeL SIG IS Workshop, Tel Aviv, Israel, 2009.
10. M. Zhang, L. O. Hall, and D. B. Goldgof, "A generic knowledge-guided image segmentation and labeling system using fuzzy clustering algorithms," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol.32, no.5, pp. 571-582, Oct. 2002.
11. S. Makrogiannis, G. Economou, and S. Fotopoulos, "A region dissimilarity relation that combines feature-space and spatial information for color image segmentation," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol.35, no.1, pp. 44-53, Feb. 2005.
12. R. Gonzalez and R. Woods, Digital Image Processing, 2nd ed. Englewood Cliffs, NJ: Prentice-Hall, 2002.
13. 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.
14. 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, Aug. 2009.
15. P. Soille, Morphological Image Analysis, 2nd ed. New York: Springer-Verlag, 2003.
16. A. C. Jalba, M. H. Wilkinson, and J. B. T. M. Roerdink, "Automatic segmentation of diatom images for classification," Microsc. Res. Tech., vol. 65, no. 1/2, pp. 72-85, Sep. 2004.
17. G. Noyel, J. Angulo, and D. Jeulin, "Morphological segmentation of hyperspectral images," Image Anal. Stereol., vol.26, pp. 101-109, 2007.
18. O. Gómez, J. A. González, and E. F. Morales, "Image segmentation using automatic seeded region growing and instance-based learning," in Proc. 12th Iberoamericann Congr. Pattern Recog., Valparaiso, Chile, Nov. 2007, pp. 192-201.
19. G. Noyel, "Filtrage, Réduction de Dimension, Classification et Segmentation Morphologique Hyperspectrale," Ph.D. dissertation, Center Math. Morphology, Paris School Mines, Fontainebleau, France, 2008.
20. L. Kaufman and P. J. Rousseeuw, Finding Groups in Data. An Introduction to Cluster Analysis. Hoboken, NJ: Wiley, 1990.
21. Y. Tarabalka, J. Chanussot, and J. A. Benediktsson, "Classification based marker selection for watershed transform of hyperspectral images," in Proc. IGARSS, Cape Town, South Africa, 2009.
22. J. Stawiaski, "Mathematical morphology and graphs: Application to interactive medical image segmentation," Ph.D. dissertation, Paris School Mines, Paris, France, 2008.
23. O. J. Morris, M. J. Lee, and A. G. Constantinides, "Graph theory for image analysis: An approach based on the shortest spanning tree," Proc. Inst. Elect. Eng.-Commun., Radar Signal Process., vol.133, no.2, pp. 146-152, Apr. 1986.
24. J. Shi and J. Malik, "Normalized cuts and image segmentation," IEEE Trans. Pattern Anal. Mach. Intell., vol.22, no.8, pp. 888-905, Aug. 2000.
25. W. Tao, H. Jin, and Y. Zhang, "Color image segmentation based on mean shift and normalized cuts," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol.37, no.5, pp. 1382-1389, Oct. 2007.
26. C. Allène, J. Audibert, M. Couprie, J. Cousty, and R. Keriven, "Some links between min-cuts, optimal spanning forests and watersheds," in Proc. 8th Int. Symp. Math. Morphol., Rio de Janeiro, Brazil, Oct. 2007, pp. 253-264.
27. C. Couprie, L. Grady, L. Najman, and H. Talbot, "Power watersheds: A new image segmentation framework extending graph cuts, random walker and optimal spanning forest," in Proc. ICCV, Kyoto, Japan, 2009, pp. 731-738.
28. V. Vapnik, Statistical Learning Theory. New York: Wiley, 1998.
29. M. Fauvel, J. Chanussot, and J. A. Benediktsson, "Evaluation of kernels for multiclass classification of hyperspectral remote sensing data," in Proc. ICASSP, May 2006, pp. II-813-II-816.
30. G. Licciardi, F. Pacifici, D. Tuia, S. Prasad, T. West, F. Giacco, J. Inglada, E. Christophe, J. Chanussot, and P. Gamba, "Decision fusion for the classification of hyperspectral data: Outcome of the 2008 GRS-S data fusion contest," IEEE Trans. Geosci. Remote Sens., vol.47, no.11, pp. 3857-3865, Nov. 2009.
31. T.-F.Wu, C.-J. Lin, and R. C.Weng, "Probability estimates for multi-class classification by pairwise coupling," J.Mach. Learn. Res., vol.5, pp. 975-1005, Dec. 2004.
32. C. Chang and C. Lin, LIBSVM: A Library for Support Vector Machines, 2001. [Online]. Available: http://www.csie.ntu.edu.tw/~cjlin/ libsvm
33. H.-T. Lin, C.-J. Lin, and R. C. Weng, "A note on Platt's probabilistic outputs for support vector machines," Dept. Comput. Sci., Nat. Taiwan Univ., Taipei, Taiwan, 2003.
34. J. Platt, "Probabilistic outputs for support vector machines and comparison to regularized likelihood methods," in Advances in Large Margin Classifiers, A. Smola, P. Bartlett, B. Schölkopf, and D. Schuurmans, Eds. Cambridge, MA: MIT Press, 2000.
35. L. Shapiro and G. Stockman, Computer Vision. Englewood Cliffs, NJ: Prentice-Hall, 2002.
36. F. van der Meer, "The effectiveness of spectral similarity measures for the analysis of hyperspectral imagery," Int. J. Appl. Earth Observation Geoinformation, vol.8, no.1, pp. 3-17, 2006.
37. C.-I Chang, "An information theoretic-based approach to spectral variability, similarity and discriminability for hyperspectral image analysis," IEEE Trans. Inf. Theory, vol.46, no.5, pp. 1927-1932, Aug. 2000.
38. P. Corsini, B. Lazzerini, and F. Marcelloni, "A fuzzy relational clustering algorithm based on a dissimilarity measure extracted from data," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol.34, no.1, pp. 775-781, Feb. 2004.
39. R. C. Prim, "Shortest connection networks and some generalizations," Bell Syst. Tech. J., vol.36, pp. 1389-1401, 1957.
40. T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein, Introduction to Algorithms, 2nd ed. Cambridge, MA: MIT Press, 2001.
41. A. Widayati, B. Verbist, and A. Meijerink, "Application of combined pixel-based and spatial-based approaches for improved mixed vegetation classification using IKONOS," in Proc. 23rd Asian Conf. Remote Sens., 2002, p. 8.
42. S. Tadjudin and D. A. Landgrebe, "Covariance estimation with limited training samples," IEEE Trans. Geosci. Remote Sens., vol.37, no.4, pp. 2113-2118, Jul. 1999.
43. 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.
44. J. A. Benediktsson and I. Kanellopoulos, "Classification of multisource and hyperspectral data based on decision fusion," IEEE Trans. Geosci. Remote Sens., vol.37, no.3, pp. 1367-1377, May 1999.
45. A. Plaza, J. A. Benediktsson, J. Boardman, J. Brazile, L. Bruzzone, G. Camps-Valls, J. Chanussot, M. Fauvel, P. Gamba, J. A. Gualtieri, M. Marconcini, J. C. Tilton, and G. Trianni, "Recent advances in techniques for hyperspectral image processing," Remote Sens. Environ., vol.113, pp. S110-S122, 2009, Supplement 1.
46. J. A. Benediktsson, J. A. Palmason, and J. R. Sveinsson, "Classification of hyperspectral data from urban areas based on extended morphological profiles," IEEE Trans. Geosci. Remote Sens., vol.43, no.3, pp. 480-491, Mar. 2005.