A new hyperspectral band selection and classification framework based on combining multiple classifiers

Due to the high-dimension characteristics of hyperspectral data, dimensionality reduction is becoming an important problem in hyperspectral image classification. Band selection can retain the information which is capable of keeping the original physical meaning of the spectral channels, and thus it has attracted more research interests. This paper tackles the band selection problem from the perspective of multiple classifiers combination, which can obtain higher classification accuracy. In the newly formulated framework of band selection and classification based on combination of multiple classifiers (BS_CMC), stochastic algorithms are firstly employed to generate several groups of initial band subset, on which a pool of classifiers is constructed. Then, improved classifier selection algorithm based on error diversity is proposed to select several member classifiers from the initial classifier pool. And finally the classification is performed through dynamic classifier selection based on local classification accuracy. The experimental results on two benchmark data sets show that the proposed approach can select those bands with more discriminative information and improve the classification accuracy effectively.