Exemplar-Free Lifelong Hyperspectral Image Classification With Spectral Consistency

Hyperspectral image (HSI) classification suffers from severe catastrophic forgetting in exemplar-free lifelong learning, where models must continuously learn new land-cover categories without accessing historical training samples. This challenge persists due to high-dimensional spectral-volumetric complexity and cross-task spectral drift, which current methods inadequately address. We propose HyperSC, a novel framework that synergizes spectral-consistent auxiliary sample synthesis with stability-plasticity fused learning. The framework consists of three key components: a spectral consistency model inversion (SCMI) module, a spectral progressive enhancement (SPE) module, and a fusion distillation learning (FDL) module. The SCMI module synthesizes class-conditional auxiliary samples through spectral moment matching, in which the mean and variance of each spectral band are constrained to match class-specific real historical data distributions, thereby achieving spectral consistency. The SPE module injects class-specific Gaussian noise and applies momentum-based updating to enhance sample diversity while preserving spectral fidelity. The FDL module jointly trains on fused real and auxiliary samples by coordinating cross-entropy classification, output-layer knowledge distillation, and intermediate-layer feature alignment, thereby enabling plasticity for new class learning while maintaining stability against catastrophic forgetting for previous tasks. Extensive experiments on three HSI datasets (Indian Pines, Houston, and Salinas) demonstrate HyperSC’s superiority compared to previous exemplar-free lifelong learning methods. The code is available at https://github.com/lzlsxs/hypersc