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• W68432876 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." @default.
• W68432876 created "2016-06-24" @default.
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• W68432876 date "2010-01-01" @default.
• W68432876 modified "2023-09-26" @default.
• W68432876 title "A Hybrid Evolutionary Approach to Band Selection for Hyperspectral Image Classification" @default.
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• W68432876 doi "https://doi.org/10.1007/978-3-642-12990-2_37" @default.
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