Using the one-dimensional S-transform as a discrimination tool in classification of hyperspectral images

Canadian Journal of Remote Sensing

Volumn 33, Issue 6, 2007, Pages 551-560

  Sahoo, Bhaskar C ^a   Oommen, Thomas ^b   Misra, Debasmita ^a   Newby, Gregory ^c  

^a UNIVERSITY OF ALASKA FAIRBANKS   (United States)

^b Tufts University   (United States)

^c Arctic Region Supercomputing Center   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DISCRIMINANT ANALYSIS; IMAGE CLASSIFICATION; REMOTE SENSING; SUPPORT VECTOR MACHINES; WAVELENGTH;

S-AMPLITUDE SPECTRA; SPECTRAL SIGNATURE; SUPPORT VECTOR CLASSIFICATION (SVC);

DATA PROCESSING;

EID: 42949149624     PISSN: 07038992     EISSN: 17127971     Source Type: Journal    
DOI: 10.5589/m07-057     Document Type: Article

References (20)

1. Ashutosh, S. 2002. Principal component-based algorithm on multispectral remote sensing data for spectral discrimination of tree cover from other vegetation types. Current Science, Vol. 82, No. 1, pp. 67-69.
2. Barry, P., Shippert, P., Gorodetzky, D., and Beck, R. 2003. Draft Hyperion hyperspectral mapping exercise using atmospheric correction and end members from spectral libraries and regions of interest with data from Cuprite, Nevada. US Geological Survey Earth Resources Observation Systems Data Center, Sioux Falls, S.D. EO-1 User Guide, Version 2.3. 74 pp.
3. Chang, C.C., and Lin, C.J. 2001. LIBSVM: a library for support vector machines. Available from www.csie.ntu.edu.tw/-cjlin/libsvm.
4. Foody, G.M., and Mathur, A. 2004. A relative evaluation of multiclass image classification by support vector machines. IEEE Transactions on Geoscience and Remote Sensing, Vol. 42, pp. 1335-1343.
5. Herold, M., Roberts, D.A., Gardner, M.E., and Dennison, P.E. 2004. Spectrometry for urban area remote sensing - development and analysis of a spectral library from 350 to 2400 nm. Remote Sensing of Environment, Vol. 91, pp. 304-319.
6. Huang, C., Davis, L.S., and Townshed, J.R.G. 2002. An assessment of support vector machines for land cover classification. International Journal of Remote Sensing, Vol. 23, pp. 725-749.
7. Hughes, G.F. 1968. On the mean accuracy of statistical pattern recognizers. IEEE Transactions on Information Theory, Vol. 14, No. 1, pp. 55-63.
8. Kaewpijit, S., Moigne, J.L., and El-Ghazawi, T. 2003. Automatic reduction of hyperspectral imagery using wavelet spectral analysis. IEEE Transactions on Geoscience and Remote Sensing, Vol. 41, No. 4, pp. 863-871.
9. Kecman, V., Huang, T.-M., and Vogt, M. 2004. Iterative single data algorithm for training kernel machines from huge data sets: theory and performance. In Support vector machines: theory and applications. Edited by L. Wang. Springer, New York. pp. 255-274.
10. Kohavi, R., and Provost, F. 1998. Glossary of terms. Machine Learning, Vol. 30, No. 2/3, pp. 271-274.
11. Mansinha, L., Stockwell, R.G., Lowe, R.P., Eramian, M., and Schincariol, R.A. 1997. Local S-spectrum analysis of 1-D and 2-D data. Physics of the Earth and Planetary Interiors, Vol. 103, pp. 329-336.
12. McClellan, J.H., Schafer, R.W., and Yoder, M.A. 1999. DSP first: a multimedia approach. 1st ed. Prentice Hall Inc., Upper Saddle River, N.J. 88 pp.
13. Melgani, F., and Bruzzone, L. 2004. Classification of hyperspectral remote sensing images with support vector machines. IEEE Transactions on Geoscience and Remote Sensing, Vol. 42, No. 8, pp. 1778-1790.
14. Meyer, D. 2001. Support vector machines. R News, Vol. 1, No. 3, pp. 23-26. Available from cran.r-project.org/doc/Rnews/Rnews_2001-3.pdf.
15. Nash, G.D., and Johnson, G.W. 2002. Soil mineralogy anomaly detection in Dixie Valley, Nevada using hyperspectral data. In Proceedings of the 27th Workshop on Geothermal Reservoir Engineering, 28-30 January 2002, Stanford, Calif. Stanford Geothermal Program, Stanford University, Stanford, Calif. SGP-TR-171.
16. Oommen, T., Misra, D., Twarakavi, N.K., Prakash, A., Sahoo, B.C., and Bandopadhyay, S. 2008. An objective analysis of support vector machine based classification for remote sensing. Mathematical Geology. In press.
17. Pal, M., and Mather, P.M. 2005. Support vector machines for classification in remote sensing. International Journal of Remote Sensing, Vol. 26, pp. 1007-1011.
18. Stewart, J.H., and Carlson, J.E. 1978. Geologic map of Nevada. US Geological Survey, Denver, Colo. Scale 1: 500 000.
19. Stockwell, R.G., Mansinha, L., and Lowe, R.P. 1996. Localization of the complex spectrum: the S transform. IEEE Transactions on Signal Processing, Vol. 44, No. 4, pp. 998-1001.
20. Zhu, G., and Blumberg, D.G. 2002. Classification using ASTER data and SVM algorithms - the case study of Beer Sheva, Israel. Remote Sensing of Environment, Vol. 80, pp. 233-240.