Abstract
Differential evolution is a new heuristic approach for global optimization method. Hyperspectral data included huge information, how to process the hyperspectral data lack of reliable and effective technique. We presented an evolutional program to select hyperspectral band, which is very fast and certainty, in order to avoid the band felling in local minima, the selected bands are contained in the entire spectral range, and have a good performance in hyperspectral data classification. The classification methods we used are very classic, which are good at the imbalance data, so that the classification accuracy are convincing, for example NBtree, Naive Bayes and J4.8. The results in classification of various kinds of minerals in uranium deposit are better than other methods, such as information entropy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Backer, D.S., Kempeneers, P., Debruyn, W., Scheunders, P.: A Band Selection Technique for Spectral Classification. IEE Geoscience and Remote Sensing Letters 2(3), 319–323 (2005)
Bajcsy, P., Groves, P.: Methodology for Hyperspectral Band Seletion. Photogrammetric Engineering and Remote Sensing 70(7), 793–802 (2004)
Hughes, G.F.: On the Mean Accuracy of Statistical Pattern Recognizers. IEEE Transactions on Information Theory IT-14, 55–63 (1968)
Han, J., Kamber, M., et al.: Data Mining Concepts and Techniques. China Machine Press, Beijing (2002)
Karlholm, J., Renhorn, I.: Wavelength Band Selection Method for Multispectral Target Detection. Applied Optics 41(32), 6786–6795 (2002)
Kumar, S., Ghosh, J., Crawford, M.M.: Best-bases Feature Extraction Algorithms for Classification of Hyperspectral Data. IEEE Trans. Geosci. Remote Sensing 39(7), 1368–1379 (2001)
Peng, W.L., Bai, Z.P., et al.: Introduction of Remote Sensing. Higher Education Press (2002)
Press, W.H., Flannery, B.P., Teukolsky, S.A., Vetterling, W.T.: Recipes in C. Cambridge University Press, Cambridge (1988)
Price, J.C.: Band Selection Procedure for Multispectral Scanners. Applied Optics 33(15), 3281–3287 (1994)
Price, K., Storn, R.: Home Page of Differential Evolution (2003), http://www.ICSI.Berkeley.edu/~storn/code.html
Riedmann, M., Milton, E.J.: Supervised Band Selection for Optimal Use of Data From Airborne Hyperspectral Sensors. In: IEEE International Geoscience and Remote Sensing Symposium (2003)
Smith, T.F., Waterman, M.S.: Identification of Common Molecular Subsequences. J. Mol. Biol. 147, 195–197 (1981)
Storn, R., Price, K.: Differential EvolutionCA Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. Journal of Global Optimization 11, 341–359 (1997)
Wiersma, D.J., Landgrebe, D.: Analytical Design of Multispectral Sensors. IEEE Trans. Geosci. Remote Sensing 18, 180–189 (1980)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, Amsterdam (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, X., Yu, C., Cai, Z. (2010). Differential Evolution Based Band Selection in Hyperspectral Data Classification. In: Cai, Z., Hu, C., Kang, Z., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2010. Lecture Notes in Computer Science, vol 6382. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16493-4_9
Download citation
DOI: https://doi.org/10.1007/978-3-642-16493-4_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16492-7
Online ISBN: 978-3-642-16493-4
eBook Packages: Computer ScienceComputer Science (R0)