Abstract
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 classifi-cation 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 Hy-perspectral 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.
Highlights
In hyperspectral imaging, sensors record data from hundreds of contiguous bands of the electromagnetic spectrum
A novel band selection approach based on the Kolmogorov Variational Distance for Hyperspectral image classification was introduced
The first experiment performed on the Indian Pine dataset have proved the efficiency and reliability of the Kolmogorov Variational Distance (KoVD) criterion as a similarity measure
Summary
Sensors record data from hundreds of contiguous bands of the electromagnetic spectrum. Two approaches for dimensionality reduction can be found in literature, band selection [6] [3] [7] [8] and band extraction [9] [10] [11] [12]. The wrapper approach [6] take advantage of the classifier itself and use it as the criterion for band selection [16], the result is a subset with a high classification score, the drawback of this technique is that the bias toward the used classifier. A new band selection approach is introduced in this paper, based on the Kolmogorov Variational Distance for Hyperspectral image classification. Our main contributions in this study is a novel band selection approach with the Kolmogorov Variational Distance modeled with GMM-REM and GMM-BIC.
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