Efficient Hyperspectral Band Selection Using GA-SR-NMI-VI: A Hybrid Similarity and Evolutionary-Based Approach

Hyperspectral image (HSI) analysis requires effective band selection techniques to enhance classification accuracy while maintaining computational efficiency. SR-NMI-VI is a recent similarity-based ranking approach that leverages Normalized Mutual Information (NMI) and Variation of Information (VI) to compute band rankings. This paper presents an extended version of SR-NMI-VI called GA-SR-NMI-VI, an advanced feature selection approach that integrates Genetic Algorithm (GA) with Similarity-based Ranking. The proposed GA-SR-NMI-VI is a two-step approach, where SR-NMI-VI is first used for band ranking, followed by a genetic algorithm (GA)-based optimization to determine the optimal number of bands. For comparative evaluation, two additional methods are considered: GA-SR-SSIM-NMI-VI, which adds SSIM to enhance spatial-spectral ranking, and GR-FRPCA, an unsupervised low-rank clustering-based approach. To validate the proposed approaches, an experimental study has been conducted on various HSI datasets covering a diverse range of classes from 2 to 16 and covering different classification scenarios, including Oil Spill, Cubert Drone, WHU-Hi-LongKou, and WHU-Hi-HanChuan. Empirically, it is shown that the proposed GA-SR-NMI-VI and its SSIM-enhanced variant consistently achieve higher accuracy and kappa scores across various machine learning models. GA-SR-NMI-VI achieves a 70–80% reduction in the number of bands while improving classification accuracy by 2–5%. Notably, traditional classifiers like Random Forest and SVM perform comparably to deep learning models while benefiting from lower computational costs, highlighting the effectiveness of GA-based methods in scenarios where deep learning may be computationally expensive or infeasible. The details of the experimental setup and the reproducible code are available at the following link: https://github.com/neerajkumarnadipelli/GA-SR-NMI-VI.