Band clustering and selection and decision fusion for target detection in hyperspectral imagery

A band clustering and selection approach based on a hyperspectral measure, spectral information divergence (SID) is presented in this paper. Hyperspectral image data is analyzed for target detection. Hyperspectral image data and spectral signatures of the targets are used to measure the SID. Virtual dimensionality (VD) is used to select optimal number of bands. For end member extraction, vertex component analysis (VCA) is used. For decision fusion a new approach based on spectral discriminatory entropy (SDE) is proposed. A comparative study is conducted to show the effectiveness of new approach of band clustering and selection. Decision fusion is also compared with full band and individual SID detection schemes.