Unsupervised Band Selection for Hyperspectral Image Classification Using Mutual Information Based Clustering

High spectral dimension of the hyperspectral images renders classification task computationally intensive. To mitigate this, a fully unsupervised band selection and ranking algorithm is proposed that leverages K-medoids clustering and mutual information to identify a subset of informative, uncorrelated spectral bands. The proposed method’s performance is assessed on the Botswana and Indian Pines datasets, outperforming several other mutual information based approaches with respect to overall classification accuracy. These results validate the efficacy of the proposed method for hyperspectral data analysis.