An effective feature selection approach based on hybrid Grey Wolf Optimizer and Genetic Algorithm for hyperspectral image

Feature selection (FS) is a critical step in hyperspectral image (HSI) classification, essential for reducing data dimensionality while preserving classification accuracy. However, FS for HSIs remains an NP-hard challenge, as existing swarm intelligence and evolutionary algorithms (SIEAs) often suffer from limited exploration capabilities or susceptibility to local optima, particularly in high-dimensional scenarios. To address these challenges, we propose GWOGA, a novel hybrid algorithm that combines Grey Wolf Optimizer (GWO) and Genetic Algorithm (GA), aiming to achieve an effective balance between exploration and exploitation. The innovation of GWOGA lies in three core strategies: (1) chaotic map and Opposition-Based Learning (OBL) for uniformly distributed population initialization, enhancing diversity and mitigating premature convergence; (2) elite learning strategy to prioritize high-ranking solutions, strengthening the search hierarchy and efficiency; and (3) a hybrid optimization mechanism where GWO ensures rapid early-stage convergence, while GA refines global search in later stages to escape local optima. Experiments on three benchmark HSIs (i.e., Indian Pines, KSC, and Botswana) demonstrate that GWOGA outperforms state-of-the-art algorithms, achieving higher classification accuracy with fewer selected bands. The results highlight GWOGA's robustness, generalizability, and potential for real-world applications in HSI FS.