Abstract
With the development of the remote-sensing imaging technology, there are more and more applications of hyperspectral image classification tasks, in which to select a minimal and effective subset from a mass of bands is the key issue. This paper put forward a novel band selection strategy based on conditional mutual information between adjacent bands and branch and bound algorithm for the high correlation between the bands. In addition, genetic algorithm and support vector machine are employed to search for the best band combination. Experimental results on two benchmark data set have shown that this approach is competitive and robust.
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References
Landgrebe, D.A.: Signal theory methods in multispectral remote sensing. Wiley, Hoboken (2003)
Liu, C., Zhao, C., Zhang, Y.: A new method of hyperspectral remote sensing image dimensional reduction. Journal of Image and Graphics 10, 218–224 (2005)
Serpico, S.B., Bruzzone, L.: A new search algorithm for feature selection in hyperspectral remote sensing images. IEEE Trans. on Geoscience and Remote Sensing 39, 1360–1367 (2001)
Serpico, S.B., Moser, G.: Extraction of spectral channels from hyperspectral images for classification purposes. IEEE Trans. on Geoscience and Remote Sensing 45, 484–495 (2007)
Guo, B., Damper, R.I., Gunn, S.R., et al.: A fast separability-based feature-selection method for high-dimensional remotely sensed image classification. Pattern Recognition 41, 1653–1662 (2008)
Huang, R., He, M.: Band selection based on feature weighting for classification of hyperspectral data. IEEE Trans. on Geoscience and Remote Sensing Letters 2, 156–159 (2005)
Wang, L., Gu, Y., Zhang, Y.: Band selection method based on combination of support vector machines and subspatial partition. Systems Engineering and Electronics 27, 974–977 (2005)
Peng, H., Long, F., Ding, C.: Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. on Pattern Analysis and Machine Intelligence 27, 1226–1238 (2005)
Sotoca, J.M., Pla, F.: Hyperspectral data selection from mutual information between image bands. In: Yeung, D.-Y., Kwok, J.T., Fred, A., Roli, F., de Ridder, D. (eds.) SSPR 2006 and SPR 2006. LNCS, vol. 4109, pp. 853–861. Springer, Heidelberg (2006)
Kwak, N., Choi, C.-H.: Input feature selection for classification problems. IEEE Trans. on Neural Networks 13, 143–159 (2002)
Novovicova, J., Somol, P., Haindl, M., Pudil, P.: Conditional mutual information based feature selection for classification task. In: Rueda, L., Mery, D., Kittler, J. (eds.) CIARP 2007. LNCS, vol. 4756, pp. 417–426. Springer, Heidelberg (2007)
Oh, I.-S., Lee, J.-S., Moon, B.-R.: Hybrid genetic algorithms for feature selection. IEEE Trans. on Pattern Analysis and Machine Intelligence 26, 1424–1437 (2004)
Chang, C., Lin, C.: LIBSVM: a library for support vector machines (2009), Software available at: http://www.csie.ntu.edu.tw/~cjlin/libsvm
Nakariyakul, S., Casasent, D.P.: Adaptive branch and bound algorithm for selecting optimal features. Pattern Recognition Letters 28, 1415–1427 (2007)
Zuo, L., Zheng, J., Wang, F., et al.: A genetic algorithm based wrapper feature selection method for classification of hyper spectral data using support vector machine. Geographical Research 27, 493–501 (2008)
Benediktsson, J.A., Palmason, J.A., Sveinsson, J.R.: Classification of hyperspectral data from urban areas based on extended morphological profiles. IEEE Trans. on Geoscience and Remote Sensing 43, 480–491 (2005)
Dundar, M.M., Landgrebe, D.A.: Toward an optimal supervised classifier for the analysis of hyperspectral data. IEEE Trans. on Geoscience and Remote Sensing 42, 271–277 (2004)
Kuo, B.C., Chang, K.Y.: Feature extractions for small sample size classification problem. IEEE Trans. on Geoscience and Remote Sensing 45, 756–764 (2007)
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Wu, H., Zhu, J., Li, S., Wan, D., Lin, L. (2010). A Hybrid Evolutionary Approach to Band Selection for Hyperspectral Image Classification. In: Zeng, Z., Wang, J. (eds) Advances in Neural Network Research and Applications. Lecture Notes in Electrical Engineering, vol 67. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12990-2_37
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DOI: https://doi.org/10.1007/978-3-642-12990-2_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12989-6
Online ISBN: 978-3-642-12990-2
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