Spatial–Spectral Prototype-Guided Deep SMOTE for Imbalanced Hyperspectral Image Classification

The inherent class imbalance and scarcity of labeled samples remain major challenges in hyperspectral image (HSI) classification. SMOTE-based methods alleviate class imbalance by generating synthetic minority-class samples. However, their interpolation mechanisms often fail to capture spatial–spectral features adequately, resulting in noisy or boundary-ambiguous samples. To address this issue, this paper proposes Spatial–Spectral Prototype-guided Deep SMOTE (SSPD) for imbalanced HSI classification. Firstly, SSPD constructs a spatial–spectral representation guided by feature importance to suppress redundant spectral bands and isolated spatial noise. Secondly, SSPD employs a prototype-guided oversampling strategy to generate synthetic minority-class samples. Class prototypes determine the generation direction, while feature-importance weights constrain the interpolation process to preserve discriminative spectral responses. Finally, a stepwise ensemble with soft voting is employed to integrate classifiers trained under different imbalance ratios. Extensive experiments were conducted on Indian Pines, Pavia University, Salinas, and WHU-Hi benchmark datasets. Compared with the individual machine-learning classifiers, SSPD achieves more stable and competitive performance in terms of Overall Accuracy (OA), Average Accuracy (AA), and Kappa Coefficient (KC).