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Band Selection with CFI and Supervised Classification for Hyperspectral Images

In this paper, we propose a new feature selection method for hyperspectral images. Firstly, the bands are selected by combining the information entropy, classification separability and correlation coefficients with the Choquet fuzzy integral. After that, maximum likelihood classification method is used for the classification. Experiments on the AVIRIS dataset show that the proposed method removes the redundant spectral bands effectively.