A marker-based approach for the automated selection of a single segmentation from a hierarchical set of image segmentations

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

Volumn 5, Issue 1, 2012, Pages 262-272

  Tarabalka, Yuliya ^a   Tilton, James C ^a   Benediktsson, Jn Atli ^b   Chanussot, Jocelyn ^c  

^a NASA GODDARD SPACE FLIGHT CENTER   (United States)

^b UNIVERSITY OF ICELAND   (Iceland)

^c GIPSA LAB   (France)

Author keywords

Classification; hierarchical segmentation; hyperspectral images; marker selection

Indexed keywords

AIRBORNE IMAGES; AUTOMATED SELECTION; CLASSIFICATION ACCURACY; HIERARCHICAL SEGMENTATION; HYPER-SPECTRAL IMAGES; HYPERSPECTRAL; HYPERSPECTRAL IMAGE ANALYSIS; MARKER SELECTION; OBJECT CLUSTERING; REMOTE SENSING IMAGES; SEGMENTATION LEVELS; SPECTRAL-SPATIAL CLASSIFICATION;

ALGORITHMS; CLASSIFICATION (OF INFORMATION); DATA FUSION; IMAGE ANALYSIS; IMAGE RECONSTRUCTION; INDEPENDENT COMPONENT ANALYSIS;

IMAGE SEGMENTATION;

AIRBORNE SENSING; ALGORITHM; HIERARCHICAL SYSTEM; IMAGE ANALYSIS; IMAGE CLASSIFICATION; REMOTE SENSING; SEGMENTATION;

EID: 84857699240     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2011.2173466     Document Type: Article

References (38)

1. H. Bagan, W. Takeuchi,T.Kinoshita, Y. Bao, andY.Yamagata, "Land cover classification and change analysis in the Horqin sandy land from 1975 to 2007," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 3, no. 2, pp. 168-177, Jun. 2010.
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. Agricult., vol. 39, no. 2, pp. 67-93, 2003. (Pubitemid 36530841)
5. C. H. Chen and P.-G. Peter Ho, "Statistical pattern recognition in remote sensing," Pattern Recognit., vol. 41, no. 9, pp. 2731-2741, 2008.
6. J.-M. Yang, P.-T. Yu, and B.-C. Kuo, "A nonparametric feature extraction and its application to nearest neighbor classification for hyperspectral image data," IEEE Trans. Geosci. Remote Sens., vol. 48, no. 3, pp. 1279-1293, Mar. 2010.
7. 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)
8. C. Chang and C. Lin, "LIBSVM - A Library for Support Vector Machines," 2008 [Online]. Available: http://www.csie.ntu.edu.tw/~cjlin/ libsvm
9. R. L. Kettig and D. A. Landgrebe, "Classification of multispectral image data by extraction and classification of homogeneous objects," IEEE Trans. Geosci. Electron., vol. 14, no. 1, pp. 19-26, Jan. 1976.
10. X. Huang and L. Zhang, "A comparative study of spatial approaches for urban mapping using hyperspectral ROSIS images over Pavia city, northern Italy," Int. J. Remote Sens., vol. 30, no. 12, pp. 3205-3221, 2009.
11. 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, supplement 1, pp. S110-S122, 2009.
12. Y. Tarabalka, J. Chanussot, and J. A. Benediktsson, "Segmentation and classification of hyperspectral images using minimum spanning forest grown from automatically selected markers," IEEE Trans. Syst., Man, Cybern.: B, vol. 40, no. 5, pp. 1267-1279, Oct. 2010.
13. G. Camps-Valls, L. Gomez-Chova, J. Munoz-Mari, J. Vila-Frances, 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)
14. 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.
15. Y. Tarabalka, J. Chanussot, and J. A. Benediktsson, "Segmentation and classification of hyperspectral images using watershed transformation," Pattern Recognit., vol. 43, no. 7, pp. 2367-2379, Jul. 2010.
16. Y. Tarabalka, J. A. Benediktsson, J. Chanussot, and J. C. Tilton, "Multiple spectral-spatial classification approach for hyperspectral data," IEEE Trans. Geosci. Remote Sens., vol. 48, no. 11, pp. 4122-4132, Oct. 2010.
17. R. Gonzalez and R. Woods, Digital Image Processing, 2nd ed. Englewood Cliffs, NJ: Prentice-Hall, 2002.
18. K. Fu and J. Mui, "A survey on image segmentation," Pattern Recognit., vol. 13, no. 1, pp. 3-16, 1981.
19. J.-M. Beaulieu and M. Goldberg, "Hierarchy in picture segmentation: A stepwise optimization approach," IEEE Trans. Pattern Anal. Machine Intell., vol. 11, no. 2, pp. 150-163, 1989.
20. J. Tilton, "Image segmentation by region growing and spectral clustering with a natural convergence criterion," in Proc. IGARSS'08, 1998, vol. 4, pp. 1766-1768.
21. J. C. Tilton, "RHSEG User's Manual: Including HSWO, HSEG, HSEGExtract, HSEGReader and HSEGViewer," version 1.50, 2010, via email request to James.C.Tilton@nasa.gov.
22. A. J. Plaza and J. C. Tilton, "Automated selection of results in hierarchical segmentations of remotely sensed hyperspectral images," in Proc. IGARSS'05, Jul. 2005, vol. 7, pp. 4946-4949.
23. P. Soille, Morphological Image Analysis, 2nd ed. New York: Springer-Verlag, 2003.
24. O. Gómez, J. A. González, and E. F. Morales, "Image segmentation using automatic seeded region growing and instance-based learning," in Proc. 12th Iberoamerican Congr. Pattern Recognit., Valparaiso, Chile, Nov. 2007, pp. 192-201.
25. G. Noyel, J. Angulo, and D. Jeulin, "Morphological segmentation of hyperspectral images," Image Anal. Stereol., vol. 26, pp. 101-109, 2007.
26. G. Noyel, "Filtrage, Réduction de Dimension, Classification et Segmentation Morphologique Hyperspectrale," Ph.D. dissertation, Ctr. Mathematical Morphology, Paris Sch. Mines, Paris, France, 2008.
27. L. Kaufman and P. J. Rousseeuw, Finding Groups in Data. An Introduction to Cluster Analysis. New York: Wiley, 1990.
28. R.O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification. New York: Wiley, 2001.
29. 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 GRSS data fusion contest," IEEE Trans. Geosci. Remote Sens., vol. 47, no. 11, pp. 3857-3865, Nov. 2008.
30. V. Vapnik, Statistical Learning Theory. New York: Wiley, 1998.
31. L. Shapiro and G. Stockman, Computer Vision. Englewood Cliffs, NJ: Prentice-Hall, 2002.
32. T.-F. Wu, C.-J. Lin, and R. C. Weng, "Probability estimates for multiclass classification by pairwise coupling," J. Machine Learning Res., no. 5, pp. 975-1005, 2004.
33. J. Stawiaski, "Mathematical morphology and graphs: Application to interactivemedical image segmentation," Ph.D. dissertation, Paris Sch. Mines, Paris, France, 2008.
34. R. C. Prim, "Shortest connection networks and some generalizations," Bell Syst. Tech. J., vol. 36, pp. 1389-1401, 1957.
35. J. A. Richards and X. Jia, Remote Sensing Digital Image Analysis: An Introduction. New York: Springer-Verlag, 1999.
36. S. Valero, P. Salembier, and J. Chanussot, "Comparison of merging orders and pruning strategies for binary partition tree in hyperspectral data," in Proc. ICIP'10, Hong Kong, 2010, pp. 1-4.
37. 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.
38. 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.