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

Abstract

Hyperspectral image (HSI) analysis requires effective band selection techniques to enhance classification accuracy while maintaining computational efficiency. Similarity-based ranking normalized mutual information and variation of information (SR-NMI-VI) is a recent SR approach that leverages NMI and VI to compute band rankings. This letter presents an extended version of SR-NMI-VI called genetic algorithm (GA)-SR-NMI-VI, an advanced feature selection approach that integrates 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 GA-based optimization to determine the optimal number of bands. For comparative evaluation, two additional methods are considered: GA-SR-structural similarity index (SSIM)-NMI-VI, which adds SSIM to enhance spatial–spectral ranking, and graph-regularized fast and robust principal component analysis (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 such as random forest and support vector machine (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