A Multi‐Strategy Enhanced Salp Swarm Optimization Approach to Optimal Spectral Band Selection for Hyperspectral Remote Sensing Imaging

ABSTRACT Band Selection (BS) is an essential method in the classification of hyperspectral images, as it effectively decreases spectral redundancy in hyperspectral remote sensing data, lowers computational expenses, and identifies the best band subsets that offer improved discriminative power from numerous spectral dimensions. Evolutionary algorithms (EAs), known for their strong search capabilities, have been effectively utilized as BS techniques in hyperspectral image analysis. However, many current EA‐based BS methods encounter two significant issues: (1) a tendency to become trapped in local optima and experience premature convergence, and (2) a high sensitivity to the choice of initialization methods and hyperparameter settings, resulting in variable performance stability. To overcome these challenges, this study introduces a multi‐strategy enhanced salp swarm optimization method (MSSA) for optimal spectral band selection, referred to as MSSA‐BS. Initially, we improve the population initialization process to achieve a more even distribution of individuals within the initial population across the search space, which enhances diversity and reduces sensitivity to initialization. Furthermore, the algorithm incorporates a Lévy flight strategy and optimization of inertial weights to enhance search dynamics. This combined approach narrows the search area, speeds up evolutionary development, and aids in escaping local optima, thus boosting optimization effectiveness. Additionally, a chain follower mechanism is implemented to update the positions of the least effective individuals, further enhancing the algorithm's exploration capabilities. Together, these advancements systematically tackle the identified challenges. To assess the performance of MSSA‐BS, extensive experiments are carried out on three standard hyperspectral image (HSI) datasets. The findings indicate that MSSA‐BS achieves higher classification accuracy compared to various leading BS methods when used in conjunction with a support vector machine (SVM) classifier.