Abstract
Abstract Hyperspectral image (HSI) band selection often suffers from band redundancy and information loss due to the neglect of spectral characteristics. To address this issue, this paper proposes a semi-supervised multi-objective particle swarm optimization with boundary selection and curve fitting (MOPSO_BSCF) method for band selection in hyperspectral image classification. The method establishes a dual-objective evaluation framework: the criterion based on Fisher discriminant analysis enhances class separability, while the criterion based on information entropy improves information richness and reduces band redundancy. MOPSO_BSCF performs adaptive band boundary grouping through local density estimation combined with K-means clustering to suppress high-redundancy interference. Subsequently, multi-curve fitting and cross-validation are applied to the Pareto front to select candidate solutions on the favorable side of the fitted front as the global best solution, thereby accelerating convergence. Experiments on three hyperspectral datasets using SVM and KNN included established and recent band-selection methods, controlled comparisons with three MOPSO variants, component ablations, robustness analyses, and statistical significance testing. MOPSO_BSCF achieved the highest mean OA, AA, and Kappa for most evaluated dataset, classifier, and band-subset combinations. The Wilcoxon tests identified significant improvements in several settings and comparable performance in others. The ablation results further supported adaptive curve-model selection, early curve-fitting activation, and multi-start K-means boundary construction. These findings demonstrate competitive and generally stable band-selection performance under the evaluated conditions.