Semi-Supervised Hyperspectral Band Selection Based on Dynamic Classifier Selection

The abundant spectral information of hyperspectral imagery makes it suitable for the classification of land cover types. However, the high dimensionality also brings some negative effects for the classification tasks. Dynamic classifier selection, in which the base classifiers are selected according to each new sample to be classified, can select the best classifier for each query sample. In this paper, a semi-supervised wrapper band selection method-the band selection based on dynamic classifier selection-is introduced to select the most discriminating bands. In the proposed method, band selection is conducted based on the selection of base classifier. Specifically, the support vector machine classification map is filtered to provide a high-quality reference, and K-nearest neighbors method is used to define the local region, finally, the band with the best classification performance is selected. Three widely used real hyperspectral datasets are used to illustrate the effectiveness of the proposed method, experimental results show that the proposed method obtains state-of-the-art performance.