Band selection for hyperspectral imagery using affinity propagation

IET Computer Vision

Volumn 3, Issue 4, 2009, Pages 213-222

  Qian, Y ^a   Yao, F ^a   Jia, S ^b  

^a ZHEJIANG UNIVERSITY   (China)

^b SHENZHEN UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

AFFINITY PROPAGATION; BAND SELECTION; CLASSIFICATION TASKS; CLUSTERING METHODS; COMPUTATIONAL COSTS; DATA POINTS; FACTOR GRAPHS; HYPERSPECTRAL IMAGERY; KNOWLEDGE DISCOVERY; LARGE DATA; PIXEL CLASSIFICATION; PIXEL IMAGES; SPECTRAL BAND;

BIOINFORMATICS; CLUSTERING ALGORITHMS; COMPUTER VISION; COST REDUCTION; EVOLUTIONARY ALGORITHMS; IMAGE ANALYSIS; PIXELS; REMOTE SENSING;

IMAGE CLASSIFICATION;

EID: 72149111677     PISSN: 17519632     EISSN: 17519640     Source Type: Journal    
DOI: 10.1049/iet-cvi.2009.0034     Document Type: Article

References (27)

1. Chang, C.-I.: Hyperspectral imaging: techniques for spectral detection and classification ', (Kluwer Academic, 2003)
2. Kumar, S., Ghosh, J., and Crawford, M.: ' Best-bases feature extraction algorithms for classification of hyperspectral data ', IEEE Trans. Geosci. Remote Sens., 2001, 39, (7), p. 1368-1379 0196-2892
3. Jimenez-Rodrguez, L.O., Arzuaga-Cruz, E., and Velez-Reyes, M.: ' Unsupervised linear feature-extraction methods and their effects in the classification of high-dimensional data ', IEEE Trans. Geosci. Remote Sens., 2007, 45, (2), p. 469-483 0196-2892
4. Serpico, B.S., and Bruzzone, L.: ' A new search algorithm for feature selection in hyperspectral remote sensing images ', IEEE Trans. Geosci. Remote Sens., 2001, 39, (7), p. 1360-1367 0196-2892
5. Guyon, I., and Elisseeff, A.: ' An introduction to variable and feature selection ', J. Mach. Learn. Res., 2003, 3, p. 1157-1182 1532-4435
6. Chang, C.-I., and Wang, S.: ' Constrained band selection for hyperspectral imagery ', IEEE Trans. Geosci. Remote Sens., 2006, 44, (6), p. 1575-1585 0196-2892 (Pubitemid 43824504)
7. Dash, M., Liu, H., and Yao, J.: ' Dimensionality reduction of unsupervised data ', Proc. Int. Conf. Tools with Artificial Intelligence, November, 1997, p. 532-539
8. Chang, C.-I., Du, Q., Sun, T.L., and Althouse, M.L.G.: ' A joint band prioritization and band-deccorelation approach to band selection for hyperspectral image classification ', IEEE Trans. Geosci. Remote Sens., 1999, 37, (6), p. 2631-2641 0196-2892
9. Sotoca, J.M., and Pla, F.: ' Hyperspectral data selection from mutual information between image bands ', Proc. SSPR&SPR 2006, August, 2006, p. 853-861
10. Cover, T.: ' Elements of information theory ', (John Wiley & Sons, 1991)
11. Mitra, P., Murthy, C.A., and Pal, S.K.: ' Unsupervised feature selection using feature similarity ', IEEE Trans. Pattern Anal. Mach. Intell., 2002, 24, (3), p. 301-312 0162-8828
12. Jain, A.K., Murty, M.N., and Flynn, P.J.: ' Data clustering: a review ', ACM Comput. Surveys, 1999, 31, (3), p. 264-323
13. Martínez-usó, A., Pla, F., Sotoca, J.M., and García-sevilla, P.: ' Clustering-based hyperspectral band selection using information measures ', IEEE Trans. Geosci. Remote Sens., 2007, 45, (12), p. 4158-4171 0196-2892
14. Frey, B.J., and Dueck, D.: ' Clustering by passing messages between data points ', Science, 2007, 315, p. 972-976 0036-8075
15. Dueck, D.: ' Affinity propagation: clustering data by passing messages ', 2009, PhD, University of Toronto
16. Agarwal, P.K., and Procopiuc, C.M.: ' Exact and approximation algorithms for clustering ', Algorithmica, 2002, 33, (2), p. 201-226
17. Kschischang, F.R., Frey, B.J., and Loeliger, H.: ' Factor graphs and the sum-product algorithm ', IEEE Trans. Inf. Theory, 2001, 47, (2), p. 498-519 0018-9448
18. Cardoso, J.F.: ' Dependence, correlation and Gaussianity in independent component analysis ', J. Mach. Learn. Res., 2003, 4, (12), p. 1177-1203 1532-4435
19. Kohavi, R.: ' A study of cross-validation and bootstrap for accuracy estimation and model selection ', Int. Joint Conf. on Artificial Intelligence, IJCAI, 1995, 2, p. 1137-1143
20. Goutte, C., Toft, P., Rostrup, E., Nielsen, F., and Hansen, L.: ' On clustering fMRI time series ', NeuroImage, 1998, 9, (3), p. 298-310
21. Cover, T., and Hart, P.: ' Nearest neighbor pattern classification ', IEEE Trans. Inf. Theory, 1967, 13, (1), p. 21-27 0018-9448
22. Melgani, F., and Bruzzone, L.: ' Classification of hyperspectral remote sensing images with support vector machines ', IEEE Trans. Geosci. Remote Sens., 2004, 42, (8), p. 1778-1790 0196-2892
23. Seeger, M.: ' Gaussian processes for machine learning ', Int. J. Neural Syst., 2004, 14, (2), p. 69-106 0129-0657
24. Rifkin, R., and Klautau, A.: ' In defense of one-vs-all classification ', J. Mach. Learn. Res., 2004, 5, (1), p. 101-141 1532-4435
25. ftp://ftp.ecn.purdue.edu/biehl/MultiSpec/92AV3C (original files) and ftp://ftp.ecn.purdue.edu/pub/biehl/PC-MultiSpec/ThyFiles.zip (ground truth)
26. Neher, R., and Srivastava, A.: ' A bayesian mrf framework for labeling terrain using hyperspectral imaging ', IEEE Trans. Geosci. Remote Sens., 2005, 43, (6), p. 1363-1374 0196-2892
27. http://tlclab.unipv.it/dftc/home.do, accessed 2008