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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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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