Hybrid Multistrategy Remora Optimization Algorithm-Based Band Selection for Hyperspectral Image Classification

Hyperspectral image (HSI) is celebrated for its detailed spectral information but faces significant challenges in dimensionality reduction stemming from excessive spectral dimensions. Band selection (BS) is a critical technique in dimension reduction, aiming to identify an optimal subset of spectral bands with minimal redundancy and maximal feature separability. Swarm intelligence methods are renowned for their flexibility and high performance in optimization problems. These methods have been extensively introduced by scholars to address BS tasks in hyperspectral imaging. Among these, the remora optimization algorithm (ROA) stands out for its exceptional optimization proficiency. However, its conventional evolutionary operators are susceptible to local optimum stagnation. Therefore, a novel BS method based on an improved ROA, termed IROA-BS, is proposed for HSI classification. First, an evaluation function is designed to estimate the class separability and redundancy of selected band subsets. Second, the hybrid evolutionary operators are intended to diversify potential solutions, while a multistage mutation module is implemented to circumvent local optimum stagnation. Furthermore, a guided learning strategy is utilized to fine-tune the equilibrium between exploration and exploitation processes. The effectiveness of the proposed IROA-BS method is rigorously validated across three widely recognized hyperspectral remote sensing image datasets. Comparative analysis with the other advanced BS methods and swarm intelligence techniques validates the superiority and efficacy of the IROA-BS method in HSI BS applications.