A Novel Band Selection Approach for Hyperspectral Image Classification using the Kolmogorov Variational Distance

In this paper, we introduce a novel band selection approach based on the Kolmogorov Variational Distance (KoVD) for Hyperspectral image classification. The main reason we are taking interest in KoVD is its unique relation to the classification error. Our previous works on band selection using the Mutual Information (MI), the Divergence Distance (DD), or the Bhattacharyya Distance (BD) inspire this study; thus, we are particularly interested in finding out how KoVD performs against these distances in terms of the numbers of band retained and the classification accuracy. All the distances in this study are modeled with the Gaussian Mixture Model (GMM) using the Bayes Information Criterion (BIC) / Robust Expectation-Maximization (REM). The experiments are carried on four benchmark Hyperspectral images: Kennedy Space Center, Salinas, Botswana, and Indian Pines (92AV3C). The results show that band selection based on the Kolmogorov Variational Distance performs better than BD and DD, meanwhile against MI the results were too close.